The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95)
A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Works about clinical trials that involve at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table.
Computer-based representation of physical systems and phenomena such as chemical processes.
The probability distribution associated with two mutually exclusive outcomes; used to model cumulative incidence rates and prevalence rates. The Bernoulli distribution is a special case of binomial distribution.
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.
Works about pre-planned studies of the safety, efficacy, or optimum dosage schedule (if appropriate) of one or more diagnostic, therapeutic, or prophylactic drugs, devices, or techniques selected according to predetermined criteria of eligibility and observed for predefined evidence of favorable and unfavorable effects. This concept includes clinical trials conducted both in the U.S. and in other countries.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.
Studies in which a number of subjects are selected from all subjects in a defined population. Conclusions based on sample results may be attributed only to the population sampled.
Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.
The form and structure of analytic studies in epidemiologic and clinical research.
A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
Small-scale tests of methods and procedures to be used on a larger scale if the pilot study demonstrates that these methods and procedures can work.
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
The application of STATISTICS to biological systems and organisms involving the retrieval or collection, analysis, reduction, and interpretation of qualitative and quantitative data.
The use of statistical and mathematical methods to analyze biological observations and phenomena.
A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
The proportion of one particular in the total of all ALLELES for one genetic locus in a breeding POPULATION.
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
An analysis comparing the allele frequencies of all available (or a whole GENOME representative set of) polymorphic markers in unrelated patients with a specific symptom or disease condition, and those of healthy controls to identify markers associated with a specific disease or condition.
The study of chance processes or the relative frequency characterizing a chance process.
The complete summaries of the frequencies of the values or categories of a measurement made on a group of items, a population, or other collection of data. The distribution tells either how many or what proportion of the group was found to have each value (or each range of values) out of all the possible values that the quantitative measure can have.
In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)
Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.
The influence of study results on the chances of publication and the tendency of investigators, reviewers, and editors to submit or accept manuscripts for publication based on the direction or strength of the study findings. Publication bias has an impact on the interpretation of clinical trials and meta-analyses. Bias can be minimized by insistence by editors on high-quality research, thorough literature reviews, acknowledgement of conflicts of interest, modification of peer review practices, etc.
Works about studies that are usually controlled to assess the effectiveness and dosage (if appropriate) of diagnostic, therapeutic, or prophylactic drugs, devices, or techniques. These studies are performed on several hundred volunteers, including a limited number of patients with the target disease or disorder, and last about two years. This concept includes phase II studies conducted in both the U.S. and in other countries.
An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.
Nonrandom association of linked genes. This is the tendency of the alleles of two separate but already linked loci to be found together more frequently than would be expected by chance alone.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
Establishment of the level of a quantifiable effect indicative of a biologic process. The evaluation is frequently to detect the degree of toxic or therapeutic effect.
The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.
Genotypic differences observed among individuals in a population.
Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.
A quantitative method of combining the results of independent studies (usually drawn from the published literature) and synthesizing summaries and conclusions which may be used to evaluate therapeutic effectiveness, plan new studies, etc., with application chiefly in the areas of research and medicine.
Elements of limited time intervals, contributing to particular results or situations.
Factors that modify the effect of the putative causal factor(s) under study.
Positive test results in subjects who do not possess the attribute for which the test is conducted. The labeling of healthy persons as diseased when screening in the detection of disease. (Last, A Dictionary of Epidemiology, 2d ed)
The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.
A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.
A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.
The analysis of a sequence such as a region of a chromosome, a haplotype, a gene, or an allele for its involvement in controlling the phenotype of a specific trait, metabolic pathway, or disease.
A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.
A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.
The introduction of error due to systematic differences in the characteristics between those selected and those not selected for a given study. In sampling bias, error is the result of failure to ensure that all members of the reference population have a known chance of selection in the sample.
Those biological processes that are involved in the transmission of hereditary traits from one organism to another.
Sequential operating programs and data which instruct the functioning of a digital computer.
Computer-assisted interpretation and analysis of various mathematical functions related to a particular problem.
Research aimed at assessing the quality and effectiveness of health care as measured by the attainment of a specified end result or outcome. Measures include parameters such as improved health, lowered morbidity or mortality, and improvement of abnormal states (such as elevated blood pressure).
Precise and detailed plans for the study of a medical or biomedical problem and/or plans for a regimen of therapy.
The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.
Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
A class of statistical methods applicable to a large set of probability distributions used to test for correlation, location, independence, etc. In most nonparametric statistical tests, the original scores or observations are replaced by another variable containing less information. An important class of nonparametric tests employs the ordinal properties of the data. Another class of tests uses information about whether an observation is above or below some fixed value such as the median, and a third class is based on the frequency of the occurrence of runs in the data. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed, p1284; Corsini, Concise Encyclopedia of Psychology, 1987, p764-5)
The genetic constitution of individuals with respect to one member of a pair of allelic genes, or sets of genes that are closely linked and tend to be inherited together such as those of the MAJOR HISTOCOMPATIBILITY COMPLEX.
Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.
The co-inheritance of two or more non-allelic GENES due to their being located more or less closely on the same CHROMOSOME.
Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.
Any method used for determining the location of and relative distances between genes on a chromosome.
The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.
New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.
Studies to determine the advantages or disadvantages, practicability, or capability of accomplishing a projected plan, study, or project.
A method of studying a drug or procedure in which both the subjects and investigators are kept unaware of who is actually getting which specific treatment.
Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.
The complete genetic complement contained in the DNA of a set of CHROMOSOMES in a HUMAN. The length of the human genome is about 3 billion base pairs.
A plant family of the order Pinales, class Pinopsida, division Coniferophyta, known for the various conifers.
The term "United States" in a medical context often refers to the country where a patient or study participant resides, and is not a medical term per se, but relevant for epidemiological studies, healthcare policies, and understanding differences in disease prevalence, treatment patterns, and health outcomes across various geographic locations.
Methods, procedures, and tests performed to diagnose disease, disordered function, or disability.
A publication issued at stated, more or less regular, intervals.
"The business or profession of the commercial production and issuance of literature" (Webster's 3d). It includes the publisher, publication processes, editing and editors. Production may be by conventional printing methods or by electronic publishing.
Works about controlled studies which are planned and carried out by several cooperating institutions to assess certain variables and outcomes in specific patient populations, for example, a multicenter study of congenital anomalies in children.
Studies in which variables relating to an individual or group of individuals are assessed over a period of time.
Works about clinical trials involving one or more test treatments, at least one control treatment, specified outcome measures for evaluating the studied intervention, and a bias-free method for assigning patients to the test treatment. The treatment may be drugs, devices, or procedures studied for diagnostic, therapeutic, or prophylactic effectiveness. Control measures include placebos, active medicines, no-treatment, dosage forms and regimens, historical comparisons, etc. When randomization using mathematical techniques, such as the use of a random numbers table, is employed to assign patients to test or control treatments, the trials are characterized as RANDOMIZED CONTROLLED TRIALS AS TOPIC.
Committees established to review interim data and efficacy outcomes in clinical trials. The findings of these committees are used in deciding whether a trial should be continued as designed, changed, or terminated. Government regulations regarding federally-funded research involving human subjects (the "Common Rule") require (45 CFR 46.111) that research ethics committees reviewing large-scale clinical trials monitor the data collected using a mechanism such as a data monitoring committee. FDA regulations (21 CFR 50.24) require that such committees be established to monitor studies conducted in emergency settings.
Criteria and standards used for the determination of the appropriateness of the inclusion of patients with specific conditions in proposed treatment plans and the criteria used for the inclusion of subjects in various clinical trials and other research protocols.
Earlier than planned termination of clinical trials.
Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
Diseases that are caused by genetic mutations present during embryo or fetal development, although they may be observed later in life. The mutations may be inherited from a parent's genome or they may be acquired in utero.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.
The nursing specialty that deals with the care of women throughout their pregnancy and childbirth and the care of their newborn children.
The family Odobenidae, suborder PINNIPEDIA, order CARNIVORA. It is represented by a single species of large, nearly hairless mammal found on Arctic shorelines, whose upper canines are modified into tusks.
The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.
Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.
Genetic loci associated with a QUANTITATIVE TRAIT.
A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.
The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.
A system for verifying and maintaining a desired level of quality in a product or process by careful planning, use of proper equipment, continued inspection, and corrective action as required. (Random House Unabridged Dictionary, 2d ed)
The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.
The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)
Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.
Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.
An infant during the first month after birth.
A formal process of examination of patient care or research proposals for conformity with ethical standards. The review is usually conducted by an organized clinical or research ethics committee (CLINICAL ETHICS COMMITTEES or RESEARCH ETHICS COMMITTEES), sometimes by a subset of such a committee, an ad hoc group, or an individual ethicist (ETHICISTS).
Individuals whose ancestral origins are in the southeastern and eastern areas of the Asian continent.
Research techniques that focus on study designs and data gathering methods in human and animal populations.
A statistical analytic technique used with discrete dependent variables, concerned with separating sets of observed values and allocating new values. It is sometimes used instead of regression analysis.
Individuals whose ancestral origins are in the continent of Europe.
Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.
The presence of apparently similar characters for which the genetic evidence indicates that different genes or different genetic mechanisms are involved in different pedigrees. In clinical settings genetic heterogeneity refers to the presence of a variety of genetic defects which cause the same disease, often due to mutations at different loci on the same gene, a finding common to many human diseases including ALZHEIMER DISEASE; CYSTIC FIBROSIS; LIPOPROTEIN LIPASE DEFICIENCY, FAMILIAL; and POLYCYSTIC KIDNEY DISEASES. (Rieger, et al., Glossary of Genetics: Classical and Molecular, 5th ed; Segen, Dictionary of Modern Medicine, 1992)
Research that involves the application of the natural sciences, especially biology and physiology, to medicine.
An approach of practicing medicine with the goal to improve and evaluate patient care. It requires the judicious integration of best research evidence with the patient's values to make decisions about medical care. This method is to help physicians make proper diagnosis, devise best testing plan, choose best treatment and methods of disease prevention, as well as develop guidelines for large groups of patients with the same disease. (from JAMA 296 (9), 2006)
A subdiscipline of human genetics which entails the reliable prediction of certain human disorders as a function of the lineage and/or genetic makeup of an individual or of any two parents or potential parents.
A generic concept reflecting concern with the modification and enhancement of life attributes, e.g., physical, political, moral and social environment; the overall condition of a human life.
Works about studies performed to evaluate the safety of diagnostic, therapeutic, or prophylactic drugs, devices, or techniques in healthy subjects and to determine the safe dosage range (if appropriate). These tests also are used to determine pharmacologic and pharmacokinetic properties (toxicity, metabolism, absorption, elimination, and preferred route of administration). They involve a small number of persons and usually last about 1 year. This concept includes phase I studies conducted both in the U.S. and in other countries.
A distribution function used to describe the occurrence of rare events or to describe the sampling distribution of isolated counts in a continuum of time or space.
A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.
A quantitative measure of the frequency on average with which articles in a journal have been cited in a given period of time.
Works about comparative studies to verify the effectiveness of diagnostic, therapeutic, or prophylactic drugs, devices, or techniques determined in phase II studies. During these trials, patients are monitored closely by physicians to identify any adverse reactions from long-term use. These studies are performed on groups of patients large enough to identify clinically significant responses and usually last about three years. This concept includes phase III studies conducted in both the U.S. and in other countries.

The significance of non-significance. (1/2102)

We discuss the implications of empirical results that are statistically non-significant. Figures illustrate the interrelations among effect size, sample sizes and their dispersion, and the power of the experiment. All calculations (detailed in Appendix) are based on actual noncentral t-distributions, with no simplifying mathematical or statistical assumptions, and the contribution of each tail is determined separately. We emphasize the importance of reporting, wherever possible, the a priori power of a study so that the reader can see what the chances were of rejecting a null hypothesis that was false. As a practical alternative, we propose that non-significant inference be qualified by an estimate of the sample size that would be required in a subsequent experiment in order to attain an acceptable level of power under the assumption that the observed effect size in the sample is the same as the true effect size in the population; appropriate plots are provided for a power of 0.8. We also point out that successive outcomes of independent experiments each of which may not be statistically significant on its own, can be easily combined to give an overall p value that often turns out to be significant. And finally, in the event that the p value is high and the power sufficient, a non-significant result may stand and be published as such.  (+info)

A simulation study of confounding in generalized linear models for air pollution epidemiology. (2/2102)

Confounding between the model covariates and causal variables (which may or may not be included as model covariates) is a well-known problem in regression models used in air pollution epidemiology. This problem is usually acknowledged but hardly ever investigated, especially in the context of generalized linear models. Using synthetic data sets, the present study shows how model overfit, underfit, and misfit in the presence of correlated causal variables in a Poisson regression model affect the estimated coefficients of the covariates and their confidence levels. The study also shows how this effect changes with the ranges of the covariates and the sample size. There is qualitative agreement between these study results and the corresponding expressions in the large-sample limit for the ordinary linear models. Confounding of covariates in an overfitted model (with covariates encompassing more than just the causal variables) does not bias the estimated coefficients but reduces their significance. The effect of model underfit (with some causal variables excluded as covariates) or misfit (with covariates encompassing only noncausal variables), on the other hand, leads to not only erroneous estimated coefficients, but a misguided confidence, represented by large t-values, that the estimated coefficients are significant. The results of this study indicate that models which use only one or two air quality variables, such as particulate matter [less than and equal to] 10 microm and sulfur dioxide, are probably unreliable, and that models containing several correlated and toxic or potentially toxic air quality variables should also be investigated in order to minimize the situation of model underfit or misfit.  (+info)

Laboratory assay reproducibility of serum estrogens in umbilical cord blood samples. (3/2102)

We evaluated the reproducibility of laboratory assays for umbilical cord blood estrogen levels and its implications on sample size estimation. Specifically, we examined correlation between duplicate measurements of the same blood samples and estimated the relative contribution of variability due to study subject and assay batch to the overall variation in measured hormone levels. Cord blood was collected from a total of 25 female babies (15 Caucasian and 10 Chinese-American) from full-term deliveries at two study sites between March and December 1997. Two serum aliquots per blood sample were assayed, either at the same time or 4 months apart, for estrone, total estradiol, weakly bound estradiol, and sex hormone-binding globulin (SHBG). Correlation coefficients (Pearson's r) between duplicate measurements were calculated. We also estimated the components of variance for each hormone or protein associated with variation among subjects and variation between assay batches. Pearson's correlation coefficients were >0.90 for all of the compounds except for total estradiol when all of the subjects were included. The intraclass correlation coefficient, defined as a proportion of the total variance due to between-subject variation, for estrone, total estradiol, weakly bound estradiol, and SHBG were 92, 80, 85, and 97%, respectively. The magnitude of measurement error found in this study would increase the sample size required for detecting a difference between two populations for total estradiol and SHBG by 25 and 3%, respectively.  (+info)

A note on power approximations for the transmission/disequilibrium test. (4/2102)

The transmission/disequilibrium test (TDT) is a popular method for detection of the genetic basis of a disease. Investigators planning such studies require computation of sample size and power, allowing for a general genetic model. Here, a rigorous method is presented for obtaining the power approximations of the TDT for samples consisting of families with either a single affected child or affected sib pairs. Power calculations based on simulation show that these approximations are quite precise. By this method, it is also shown that a previously published power approximation of the TDT is erroneous.  (+info)

Comparison of linkage-disequilibrium methods for localization of genes influencing quantitative traits in humans. (5/2102)

Linkage disequilibrium has been used to help in the identification of genes predisposing to certain qualitative diseases. Although several linkage-disequilibrium tests have been developed for localization of genes influencing quantitative traits, these tests have not been thoroughly compared with one another. In this report we compare, under a variety of conditions, several different linkage-disequilibrium tests for identification of loci affecting quantitative traits. These tests use either single individuals or parent-child trios. When we compared tests with equal samples, we found that the truncated measured allele (TMA) test was the most powerful. The trait allele frequencies, the stringency of sample ascertainment, the number of marker alleles, and the linked genetic variance affected the power, but the presence of polygenes did not. When there were more than two trait alleles at a locus in the population, power to detect disequilibrium was greatly diminished. The presence of unlinked disequilibrium (D'*) increased the false-positive error rates of disequilibrium tests involving single individuals but did not affect the error rates of tests using family trios. The increase in error rates was affected by the stringency of selection, the trait allele frequency, and the linked genetic variance but not by polygenic factors. In an equilibrium population, the TMA test is most powerful, but, when adjusted for the presence of admixture, Allison test 3 becomes the most powerful whenever D'*>.15.  (+info)

Measurement of continuous ambulatory peritoneal dialysis prescription adherence using a novel approach. (6/2102)

OBJECTIVE: The purpose of the study was to test a novel approach to monitoring the adherence of continuous ambulatory peritoneal dialysis (CAPD) patients to their dialysis prescription. DESIGN: A descriptive observational study was done in which exchange behaviors were monitored over a 2-week period of time. SETTING: Patients were recruited from an outpatient dialysis center. PARTICIPANTS: A convenience sample of patients undergoing CAPD at Piedmont Dialysis Center in Winston-Salem, North Carolina was recruited for the study. Of 31 CAPD patients, 20 (64.5%) agreed to participate. MEASURES: Adherence of CAPD patients to their dialysis prescription was monitored using daily logs and an electronic monitoring device (the Medication Event Monitoring System, or MEMS; APREX, Menlo Park, California, U.S.A.). Patients recorded in their logs their exchange activities during the 2-week observation period. Concurrently, patients were instructed to deposit the pull tab from their dialysate bag into a MEMS bottle immediately after performing each exchange. The MEMS bottle was closed with a cap containing a computer chip that recorded the date and time each time the bottle was opened. RESULTS: One individual's MEMS device malfunctioned and thus the data presented in this report are based upon the remaining 19 patients. A significant discrepancy was found between log data and MEMS data, with MEMS data indicating a greater number and percentage of missed exchanges. MEMS data indicated that some patients concentrated their exchange activities during the day, with shortened dwell times between exchanges. Three indices were developed for this study: a measure of the average time spent in noncompliance, and indices of consistency in the timing of exchanges within and between days. Patients who were defined as consistent had lower scores on the noncompliance index compared to patients defined as inconsistent (p = 0.015). CONCLUSIONS: This study describes a methodology that may be useful in assessing adherence to the peritoneal dialysis regimen. Of particular significance is the ability to assess the timing of exchanges over the course of a day. Clinical implications are limited due to issues of data reliability and validity, the short-term nature of the study, the small sample, and the fact that clinical outcomes were not considered in this methodology study. Additional research is needed to further develop this data-collection approach.  (+info)

Statistical power of MRI monitored trials in multiple sclerosis: new data and comparison with previous results. (7/2102)

OBJECTIVES: To evaluate the durations of the follow up and the reference population sizes needed to achieve optimal and stable statistical powers for two period cross over and parallel group design clinical trials in multiple sclerosis, when using the numbers of new enhancing lesions and the numbers of active scans as end point variables. METHODS: The statistical power was calculated by means of computer simulations performed using MRI data obtained from 65 untreated relapsing-remitting or secondary progressive patients who were scanned monthly for 9 months. The statistical power was calculated for follow up durations of 2, 3, 6, and 9 months and for sample sizes of 40-100 patients for parallel group and of 20-80 patients for two period cross over design studies. The stability of the estimated powers was evaluated by applying the same procedure on random subsets of the original data. RESULTS: When using the number of new enhancing lesions as the end point, the statistical power increased for all the simulated treatment effects with the duration of the follow up until 3 months for the parallel group design and until 6 months for the two period cross over design. Using the number of active scans as the end point, the statistical power steadily increased until 6 months for the parallel group design and until 9 months for the two period cross over design. The power estimates in the present sample and the comparisons of these results with those obtained by previous studies with smaller patient cohorts suggest that statistical power is significantly overestimated when the size of the reference data set decreases for parallel group design studies or the duration of the follow up decreases for two period cross over studies. CONCLUSIONS: These results should be used to determine the duration of the follow up and the sample size needed when planning MRI monitored clinical trials in multiple sclerosis.  (+info)

Power and sample size calculations in case-control studies of gene-environment interactions: comments on different approaches. (8/2102)

Power and sample size considerations are critical for the design of epidemiologic studies of gene-environment interactions. Hwang et al. (Am J Epidemiol 1994;140:1029-37) and Foppa and Spiegelman (Am J Epidemiol 1997;146:596-604) have presented power and sample size calculations for case-control studies of gene-environment interactions. Comparisons of calculations using these approaches and an approach for general multivariate regression models for the odds ratio previously published by Lubin and Gail (Am J Epidemiol 1990; 131:552-66) have revealed substantial differences under some scenarios. These differences are the result of a highly restrictive characterization of the null hypothesis in Hwang et al. and Foppa and Spiegelman, which results in an underestimation of sample size and overestimation of power for the test of a gene-environment interaction. A computer program to perform sample size and power calculations to detect additive or multiplicative models of gene-environment interactions using the Lubin and Gail approach will be available free of charge in the near future from the National Cancer Institute.  (+info)

In clinical research, sample size refers to the number of participants or observations included in a study. It is a critical aspect of study design that can impact the validity and generalizability of research findings. A larger sample size typically provides more statistical power, which means that it is more likely to detect true effects if they exist. However, increasing the sample size also increases the cost and time required for a study. Therefore, determining an appropriate sample size involves balancing statistical power with practical considerations.

The calculation of sample size depends on several factors, including the expected effect size, the variability of the outcome measure, the desired level of statistical significance, and the desired power of the study. Statistical software programs are often used to calculate sample sizes that balance these factors while minimizing the overall sample size required to detect a meaningful effect.

It is important to note that a larger sample size does not necessarily mean that a study is more rigorous or well-designed. The quality of the study's methods, including the selection of participants, the measurement of outcomes, and the analysis of data, are also critical factors that can impact the validity and generalizability of research findings.

A research design in medical or healthcare research is a systematic plan that guides the execution and reporting of research to address a specific research question or objective. It outlines the overall strategy for collecting, analyzing, and interpreting data to draw valid conclusions. The design includes details about the type of study (e.g., experimental, observational), sampling methods, data collection techniques, data analysis approaches, and any potential sources of bias or confounding that need to be controlled for. A well-defined research design helps ensure that the results are reliable, generalizable, and relevant to the research question, ultimately contributing to evidence-based practice in medicine and healthcare.

Statistical data interpretation involves analyzing and interpreting numerical data in order to identify trends, patterns, and relationships. This process often involves the use of statistical methods and tools to organize, summarize, and draw conclusions from the data. The goal is to extract meaningful insights that can inform decision-making, hypothesis testing, or further research.

In medical contexts, statistical data interpretation is used to analyze and make sense of large sets of clinical data, such as patient outcomes, treatment effectiveness, or disease prevalence. This information can help healthcare professionals and researchers better understand the relationships between various factors that impact health outcomes, develop more effective treatments, and identify areas for further study.

Some common statistical methods used in data interpretation include descriptive statistics (e.g., mean, median, mode), inferential statistics (e.g., hypothesis testing, confidence intervals), and regression analysis (e.g., linear, logistic). These methods can help medical professionals identify patterns and trends in the data, assess the significance of their findings, and make evidence-based recommendations for patient care or public health policy.

Statistical models are mathematical representations that describe the relationship between variables in a given dataset. They are used to analyze and interpret data in order to make predictions or test hypotheses about a population. In the context of medicine, statistical models can be used for various purposes such as:

1. Disease risk prediction: By analyzing demographic, clinical, and genetic data using statistical models, researchers can identify factors that contribute to an individual's risk of developing certain diseases. This information can then be used to develop personalized prevention strategies or early detection methods.

2. Clinical trial design and analysis: Statistical models are essential tools for designing and analyzing clinical trials. They help determine sample size, allocate participants to treatment groups, and assess the effectiveness and safety of interventions.

3. Epidemiological studies: Researchers use statistical models to investigate the distribution and determinants of health-related events in populations. This includes studying patterns of disease transmission, evaluating public health interventions, and estimating the burden of diseases.

4. Health services research: Statistical models are employed to analyze healthcare utilization, costs, and outcomes. This helps inform decisions about resource allocation, policy development, and quality improvement initiatives.

5. Biostatistics and bioinformatics: In these fields, statistical models are used to analyze large-scale molecular data (e.g., genomics, proteomics) to understand biological processes and identify potential therapeutic targets.

In summary, statistical models in medicine provide a framework for understanding complex relationships between variables and making informed decisions based on data-driven insights.

A randomized controlled trial (RCT) is a type of clinical study in which participants are randomly assigned to receive either the experimental intervention or the control condition, which may be a standard of care, placebo, or no treatment. The goal of an RCT is to minimize bias and ensure that the results are due to the intervention being tested rather than other factors. This design allows for a comparison between the two groups to determine if there is a significant difference in outcomes. RCTs are often considered the gold standard for evaluating the safety and efficacy of medical interventions, as they provide a high level of evidence for causal relationships between the intervention and health outcomes.

A computer simulation is a process that involves creating a model of a real-world system or phenomenon on a computer and then using that model to run experiments and make predictions about how the system will behave under different conditions. In the medical field, computer simulations are used for a variety of purposes, including:

1. Training and education: Computer simulations can be used to create realistic virtual environments where medical students and professionals can practice their skills and learn new procedures without risk to actual patients. For example, surgeons may use simulation software to practice complex surgical techniques before performing them on real patients.
2. Research and development: Computer simulations can help medical researchers study the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone. By creating detailed models of cells, tissues, organs, or even entire organisms, researchers can use simulation software to explore how these systems function and how they respond to different stimuli.
3. Drug discovery and development: Computer simulations are an essential tool in modern drug discovery and development. By modeling the behavior of drugs at a molecular level, researchers can predict how they will interact with their targets in the body and identify potential side effects or toxicities. This information can help guide the design of new drugs and reduce the need for expensive and time-consuming clinical trials.
4. Personalized medicine: Computer simulations can be used to create personalized models of individual patients based on their unique genetic, physiological, and environmental characteristics. These models can then be used to predict how a patient will respond to different treatments and identify the most effective therapy for their specific condition.

Overall, computer simulations are a powerful tool in modern medicine, enabling researchers and clinicians to study complex systems and make predictions about how they will behave under a wide range of conditions. By providing insights into the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone, computer simulations are helping to advance our understanding of human health and disease.

Binomial distribution is a type of discrete probability distribution that describes the number of successes in a fixed number of independent Bernoulli trials with the same probability of success. It is called a "binomial" distribution because it involves the sum of two outcomes: success and failure. The binomial distribution is defined by two parameters: n, the number of trials, and p, the probability of success on any given trial. The possible values of the random variable range from 0 to n.

The formula for calculating the probability mass function (PMF) of a binomial distribution is:

P(X=k) = C(n, k) \* p^k \* (1-p)^(n-k),

where X is the number of successes, n is the number of trials, k is the specific number of successes, p is the probability of success on any given trial, and C(n, k) is the number of combinations of n items taken k at a time.

Binomial distribution has many applications in medical research, such as testing the effectiveness of a treatment or diagnostic test, where the trials could represent individual patients or samples, and success could be defined as a positive response to treatment or a correct diagnosis.

Genetic models are theoretical frameworks used in genetics to describe and explain the inheritance patterns and genetic architecture of traits, diseases, or phenomena. These models are based on mathematical equations and statistical methods that incorporate information about gene frequencies, modes of inheritance, and the effects of environmental factors. They can be used to predict the probability of certain genetic outcomes, to understand the genetic basis of complex traits, and to inform medical management and treatment decisions.

There are several types of genetic models, including:

1. Mendelian models: These models describe the inheritance patterns of simple genetic traits that follow Mendel's laws of segregation and independent assortment. Examples include autosomal dominant, autosomal recessive, and X-linked inheritance.
2. Complex trait models: These models describe the inheritance patterns of complex traits that are influenced by multiple genes and environmental factors. Examples include heart disease, diabetes, and cancer.
3. Population genetics models: These models describe the distribution and frequency of genetic variants within populations over time. They can be used to study evolutionary processes, such as natural selection and genetic drift.
4. Quantitative genetics models: These models describe the relationship between genetic variation and phenotypic variation in continuous traits, such as height or IQ. They can be used to estimate heritability and to identify quantitative trait loci (QTLs) that contribute to trait variation.
5. Statistical genetics models: These models use statistical methods to analyze genetic data and infer the presence of genetic associations or linkage. They can be used to identify genetic risk factors for diseases or traits.

Overall, genetic models are essential tools in genetics research and medical genetics, as they allow researchers to make predictions about genetic outcomes, test hypotheses about the genetic basis of traits and diseases, and develop strategies for prevention, diagnosis, and treatment.

Reproducibility of results in a medical context refers to the ability to obtain consistent and comparable findings when a particular experiment or study is repeated, either by the same researcher or by different researchers, following the same experimental protocol. It is an essential principle in scientific research that helps to ensure the validity and reliability of research findings.

In medical research, reproducibility of results is crucial for establishing the effectiveness and safety of new treatments, interventions, or diagnostic tools. It involves conducting well-designed studies with adequate sample sizes, appropriate statistical analyses, and transparent reporting of methods and findings to allow other researchers to replicate the study and confirm or refute the results.

The lack of reproducibility in medical research has become a significant concern in recent years, as several high-profile studies have failed to produce consistent findings when replicated by other researchers. This has led to increased scrutiny of research practices and a call for greater transparency, rigor, and standardization in the conduct and reporting of medical research.

Clinical trials are research studies that involve human participants and are designed to evaluate the safety and efficacy of new medical treatments, drugs, devices, or behavioral interventions. The purpose of clinical trials is to determine whether a new intervention is safe, effective, and beneficial for patients, as well as to compare it with currently available treatments. Clinical trials follow a series of phases, each with specific goals and criteria, before a new intervention can be approved by regulatory authorities for widespread use.

Clinical trials are conducted according to a protocol, which is a detailed plan that outlines the study's objectives, design, methodology, statistical analysis, and ethical considerations. The protocol is developed and reviewed by a team of medical experts, statisticians, and ethicists, and it must be approved by an institutional review board (IRB) before the trial can begin.

Participation in clinical trials is voluntary, and participants must provide informed consent before enrolling in the study. Informed consent involves providing potential participants with detailed information about the study's purpose, procedures, risks, benefits, and alternatives, as well as their rights as research subjects. Participants can withdraw from the study at any time without penalty or loss of benefits to which they are entitled.

Clinical trials are essential for advancing medical knowledge and improving patient care. They help researchers identify new treatments, diagnostic tools, and prevention strategies that can benefit patients and improve public health. However, clinical trials also pose potential risks to participants, including adverse effects from experimental interventions, time commitment, and inconvenience. Therefore, it is important for researchers to carefully design and conduct clinical trials to minimize risks and ensure that the benefits outweigh the risks.

An algorithm is not a medical term, but rather a concept from computer science and mathematics. In the context of medicine, algorithms are often used to describe step-by-step procedures for diagnosing or managing medical conditions. These procedures typically involve a series of rules or decision points that help healthcare professionals make informed decisions about patient care.

For example, an algorithm for diagnosing a particular type of heart disease might involve taking a patient's medical history, performing a physical exam, ordering certain diagnostic tests, and interpreting the results in a specific way. By following this algorithm, healthcare professionals can ensure that they are using a consistent and evidence-based approach to making a diagnosis.

Algorithms can also be used to guide treatment decisions. For instance, an algorithm for managing diabetes might involve setting target blood sugar levels, recommending certain medications or lifestyle changes based on the patient's individual needs, and monitoring the patient's response to treatment over time.

Overall, algorithms are valuable tools in medicine because they help standardize clinical decision-making and ensure that patients receive high-quality care based on the latest scientific evidence.

Single Nucleotide Polymorphism (SNP) is a type of genetic variation that occurs when a single nucleotide (A, T, C, or G) in the DNA sequence is altered. This alteration must occur in at least 1% of the population to be considered a SNP. These variations can help explain why some people are more susceptible to certain diseases than others and can also influence how an individual responds to certain medications. SNPs can serve as biological markers, helping scientists locate genes that are associated with disease. They can also provide information about an individual's ancestry and ethnic background.

"Sampling studies" is not a specific medical term, but rather a general term that refers to research studies in which a sample of individuals or data is collected and analyzed to make inferences about a larger population. In medical research, sampling studies can be used to estimate the prevalence of diseases or risk factors within a certain population, to evaluate the effectiveness of treatments or interventions, or to study the relationships between various health-related variables.

The sample for a sampling study may be selected using various methods, such as random sampling, stratified sampling, cluster sampling, or convenience sampling. The choice of sampling method depends on the research question, the characteristics of the population of interest, and practical considerations related to cost, time, and feasibility.

It is important to note that sampling studies have limitations and potential sources of bias, just like any other research design. Therefore, it is essential to carefully consider the study methods and limitations when interpreting the results of sampling studies in medical research.

Treatment outcome is a term used to describe the result or effect of medical treatment on a patient's health status. It can be measured in various ways, such as through symptoms improvement, disease remission, reduced disability, improved quality of life, or survival rates. The treatment outcome helps healthcare providers evaluate the effectiveness of a particular treatment plan and make informed decisions about future care. It is also used in clinical research to compare the efficacy of different treatments and improve patient care.

Epidemiologic research design refers to the plan and structure of an epidemiological study, which describes how data will be collected, analyzed, and interpreted. It includes specifying the research question, selecting the study population, choosing the study design (such as cohort, case-control, or cross-sectional), outlining the data collection methods, and describing the statistical analysis plan. A well-designed epidemiologic research study aims to establish a reliable association between exposures and health outcomes in a population, which can inform public health policies and interventions.

Genetic predisposition to disease refers to an increased susceptibility or vulnerability to develop a particular illness or condition due to inheriting specific genetic variations or mutations from one's parents. These genetic factors can make it more likely for an individual to develop a certain disease, but it does not guarantee that the person will definitely get the disease. Environmental factors, lifestyle choices, and interactions between genes also play crucial roles in determining if a genetically predisposed person will actually develop the disease. It is essential to understand that having a genetic predisposition only implies a higher risk, not an inevitable outcome.

A case-control study is an observational research design used to identify risk factors or causes of a disease or health outcome. In this type of study, individuals with the disease or condition (cases) are compared with similar individuals who do not have the disease or condition (controls). The exposure history or other characteristics of interest are then compared between the two groups to determine if there is an association between the exposure and the disease.

Case-control studies are often used when it is not feasible or ethical to conduct a randomized controlled trial, as they can provide valuable insights into potential causes of diseases or health outcomes in a relatively short period of time and at a lower cost than other study designs. However, because case-control studies rely on retrospective data collection, they are subject to biases such as recall bias and selection bias, which can affect the validity of the results. Therefore, it is important to carefully design and conduct case-control studies to minimize these potential sources of bias.

Genotype, in genetics, refers to the complete heritable genetic makeup of an individual organism, including all of its genes. It is the set of instructions contained in an organism's DNA for the development and function of that organism. The genotype is the basis for an individual's inherited traits, and it can be contrasted with an individual's phenotype, which refers to the observable physical or biochemical characteristics of an organism that result from the expression of its genes in combination with environmental influences.

It is important to note that an individual's genotype is not necessarily identical to their genetic sequence. Some genes have multiple forms called alleles, and an individual may inherit different alleles for a given gene from each parent. The combination of alleles that an individual inherits for a particular gene is known as their genotype for that gene.

Understanding an individual's genotype can provide important information about their susceptibility to certain diseases, their response to drugs and other treatments, and their risk of passing on inherited genetic disorders to their offspring.

I'm sorry for any confusion, but "pilot projects" is not a medical term per se. Instead, it is a general term used in various fields, including healthcare and medicine, to describe a small-scale initiative that is implemented on a temporary basis to evaluate its feasibility, effectiveness, or impact before deciding whether to expand or continue it.

In the context of healthcare, pilot projects might involve testing new treatment protocols, implementing innovative care models, or introducing technology solutions in a limited setting to assess their potential benefits and drawbacks. The results of these projects can help inform decisions about broader implementation and provide valuable insights for improving the quality and efficiency of healthcare services.

Statistics, as a topic in the context of medicine and healthcare, refers to the scientific discipline that involves the collection, analysis, interpretation, and presentation of numerical data or quantifiable data in a meaningful and organized manner. It employs mathematical theories and models to draw conclusions, make predictions, and support evidence-based decision-making in various areas of medical research and practice.

Some key concepts and methods in medical statistics include:

1. Descriptive Statistics: Summarizing and visualizing data through measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation).
2. Inferential Statistics: Drawing conclusions about a population based on a sample using hypothesis testing, confidence intervals, and statistical modeling.
3. Probability Theory: Quantifying the likelihood of events or outcomes in medical scenarios, such as diagnostic tests' sensitivity and specificity.
4. Study Designs: Planning and implementing various research study designs, including randomized controlled trials (RCTs), cohort studies, case-control studies, and cross-sectional surveys.
5. Sampling Methods: Selecting a representative sample from a population to ensure the validity and generalizability of research findings.
6. Multivariate Analysis: Examining the relationships between multiple variables simultaneously using techniques like regression analysis, factor analysis, or cluster analysis.
7. Survival Analysis: Analyzing time-to-event data, such as survival rates in clinical trials or disease progression.
8. Meta-Analysis: Systematically synthesizing and summarizing the results of multiple studies to provide a comprehensive understanding of a research question.
9. Biostatistics: A subfield of statistics that focuses on applying statistical methods to biological data, including medical research.
10. Epidemiology: The study of disease patterns in populations, which often relies on statistical methods for data analysis and interpretation.

Medical statistics is essential for evidence-based medicine, clinical decision-making, public health policy, and healthcare management. It helps researchers and practitioners evaluate the effectiveness and safety of medical interventions, assess risk factors and outcomes associated with diseases or treatments, and monitor trends in population health.

Biostatistics is the application of statistics to a wide range of topics in biology, public health, and medicine. It involves the design, execution, analysis, and interpretation of statistical studies in these fields. Biostatisticians use various mathematical and statistical methods to analyze data from clinical trials, epidemiological studies, and other types of research in order to make inferences about populations and test hypotheses. They may also be involved in the development of new statistical methods for specific applications in biology and medicine.

The goals of biostatistics are to help researchers design valid and ethical studies, to ensure that data are collected and analyzed in a rigorous and unbiased manner, and to interpret the results of statistical analyses in the context of the underlying biological or medical questions. Biostatisticians may work closely with researchers in many different areas, including genetics, epidemiology, clinical trials, public health, and health services research.

Some specific tasks that biostatisticians might perform include:

* Designing studies and experiments to test hypotheses or answer research questions
* Developing sampling plans and determining sample sizes
* Collecting and managing data
* Performing statistical analyses using appropriate methods
* Interpreting the results of statistical analyses and drawing conclusions
* Communicating the results of statistical analyses to researchers, clinicians, and other stakeholders

Biostatistics is an important tool for advancing our understanding of biology and medicine, and for improving public health. It plays a key role in many areas of research, including the development of new drugs and therapies, the identification of risk factors for diseases, and the evaluation of public health interventions.

Biometry, also known as biometrics, is the scientific study of measurements and statistical analysis of living organisms. In a medical context, biometry is often used to refer to the measurement and analysis of physical characteristics or features of the human body, such as height, weight, blood pressure, heart rate, and other physiological variables. These measurements can be used for a variety of purposes, including diagnosis, treatment planning, monitoring disease progression, and research.

In addition to physical measurements, biometry may also refer to the use of statistical methods to analyze biological data, such as genetic information or medical images. This type of analysis can help researchers and clinicians identify patterns and trends in large datasets, and make predictions about health outcomes or treatment responses.

Overall, biometry is an important tool in modern medicine, as it allows healthcare professionals to make more informed decisions based on data and evidence.

Bayes' theorem, also known as Bayes' rule or Bayes' formula, is a fundamental principle in the field of statistics and probability theory. It describes how to update the probability of a hypothesis based on new evidence or data. The theorem is named after Reverend Thomas Bayes, who first formulated it in the 18th century.

In mathematical terms, Bayes' theorem states that the posterior probability of a hypothesis (H) given some observed evidence (E) is proportional to the product of the prior probability of the hypothesis (P(H)) and the likelihood of observing the evidence given the hypothesis (P(E|H)):

Posterior Probability = P(H|E) = [P(E|H) x P(H)] / P(E)

Where:

* P(H|E): The posterior probability of the hypothesis H after observing evidence E. This is the probability we want to calculate.
* P(E|H): The likelihood of observing evidence E given that the hypothesis H is true.
* P(H): The prior probability of the hypothesis H before observing any evidence.
* P(E): The marginal likelihood or probability of observing evidence E, regardless of whether the hypothesis H is true or not. This value can be calculated as the sum of the products of the likelihood and prior probability for all possible hypotheses: P(E) = Σ[P(E|Hi) x P(Hi)]

Bayes' theorem has many applications in various fields, including medicine, where it can be used to update the probability of a disease diagnosis based on test results or other clinical findings. It is also widely used in machine learning and artificial intelligence algorithms for probabilistic reasoning and decision making under uncertainty.

Gene frequency, also known as allele frequency, is a measure in population genetics that reflects the proportion of a particular gene or allele (variant of a gene) in a given population. It is calculated as the number of copies of a specific allele divided by the total number of all alleles at that genetic locus in the population.

For example, if we consider a gene with two possible alleles, A and a, the gene frequency of allele A (denoted as p) can be calculated as follows:

p = (number of copies of allele A) / (total number of all alleles at that locus)

Similarly, the gene frequency of allele a (denoted as q) would be:

q = (number of copies of allele a) / (total number of all alleles at that locus)

Since there are only two possible alleles for this gene in this example, p + q = 1. These frequencies can help researchers understand genetic diversity and evolutionary processes within populations.

"Likelihood functions" is a statistical concept that is used in medical research and other fields to estimate the probability of obtaining a given set of data, given a set of assumptions or parameters. In other words, it is a function that describes how likely it is to observe a particular outcome or result, based on a set of model parameters.

More formally, if we have a statistical model that depends on a set of parameters θ, and we observe some data x, then the likelihood function is defined as:

L(θ | x) = P(x | θ)

This means that the likelihood function describes the probability of observing the data x, given a particular value of the parameter vector θ. By convention, the likelihood function is often expressed as a function of the parameters, rather than the data, so we might instead write:

L(θ) = P(x | θ)

The likelihood function can be used to estimate the values of the model parameters that are most consistent with the observed data. This is typically done by finding the value of θ that maximizes the likelihood function, which is known as the maximum likelihood estimator (MLE). The MLE has many desirable statistical properties, including consistency, efficiency, and asymptotic normality.

In medical research, likelihood functions are often used in the context of Bayesian analysis, where they are combined with prior distributions over the model parameters to obtain posterior distributions that reflect both the observed data and prior knowledge or assumptions about the parameter values. This approach is particularly useful when there is uncertainty or ambiguity about the true value of the parameters, as it allows researchers to incorporate this uncertainty into their analyses in a principled way.

A Genome-Wide Association Study (GWAS) is an analytical approach used in genetic research to identify associations between genetic variants, typically Single Nucleotide Polymorphisms (SNPs), and specific traits or diseases across the entire genome. This method involves scanning the genomes of many individuals, usually thousands, to find genetic markers that occur more frequently in people with a particular disease or trait than in those without it.

The goal of a GWAS is to identify genetic loci (positions on chromosomes) associated with a trait or disease, which can help researchers understand the underlying genetic architecture and biological mechanisms contributing to the condition. It's important to note that while GWAS can identify associations between genetic variants and traits/diseases, these studies do not necessarily prove causation. Further functional validation studies are often required to confirm the role of identified genetic variants in the development or progression of a trait or disease.

In the context of medicine and healthcare, 'probability' does not have a specific medical definition. However, in general terms, probability is a branch of mathematics that deals with the study of numerical quantities called probabilities, which are assigned to events or sets of events. Probability is a measure of the likelihood that an event will occur. It is usually expressed as a number between 0 and 1, where 0 indicates that the event is impossible and 1 indicates that the event is certain to occur.

In medical research and statistics, probability is often used to quantify the uncertainty associated with statistical estimates or hypotheses. For example, a p-value is a probability that measures the strength of evidence against a hypothesis. A small p-value (typically less than 0.05) suggests that the observed data are unlikely under the assumption of the null hypothesis, and therefore provides evidence in favor of an alternative hypothesis.

Probability theory is also used to model complex systems and processes in medicine, such as disease transmission dynamics or the effectiveness of medical interventions. By quantifying the uncertainty associated with these models, researchers can make more informed decisions about healthcare policies and practices.

In medical statistics, a statistical distribution refers to the pattern of frequency or proportion of certain variables in a population. It describes how the data points in a sample are distributed and can be used to make inferences about a larger population. There are various types of statistical distributions, including normal (or Gaussian) distribution, binomial distribution, Poisson distribution, and exponential distribution, among others. These distributions have specific mathematical properties that allow researchers to calculate probabilities and make predictions based on the data. For example, a normal distribution is characterized by its mean and standard deviation, while a Poisson distribution models the number of events occurring within a fixed interval of time or space. Understanding statistical distributions is crucial for interpreting medical research findings and making informed decisions in healthcare.

I'm sorry for any confusion, but the "Monte Carlo method" is actually a term from the field of mathematics and computer science, not medicine. It refers to a statistical technique that allows for the modeling of complex systems by running multiple simulations with random inputs. This method is widely used in various fields such as physics, engineering, and finance, but it is not a medical concept or term.

An allele is a variant form of a gene that is located at a specific position on a specific chromosome. Alleles are alternative forms of the same gene that arise by mutation and are found at the same locus or position on homologous chromosomes.

Each person typically inherits two copies of each gene, one from each parent. If the two alleles are identical, a person is said to be homozygous for that trait. If the alleles are different, the person is heterozygous.

For example, the ABO blood group system has three alleles, A, B, and O, which determine a person's blood type. If a person inherits two A alleles, they will have type A blood; if they inherit one A and one B allele, they will have type AB blood; if they inherit two B alleles, they will have type B blood; and if they inherit two O alleles, they will have type O blood.

Alleles can also influence traits such as eye color, hair color, height, and other physical characteristics. Some alleles are dominant, meaning that only one copy of the allele is needed to express the trait, while others are recessive, meaning that two copies of the allele are needed to express the trait.

Publication bias refers to the tendency of researchers, editors, and pharmaceutical companies to handle and publish research results in a way that depends on the nature and direction of the study findings. This type of bias is particularly common in clinical trials related to medical interventions or treatments.

In publication bias, studies with positive or "statistically significant" results are more likely to be published and disseminated than those with negative or null results. This can occur for various reasons, such as the reluctance of researchers and sponsors to report negative findings, or the preference of journal editors to publish positive and novel results that are more likely to attract readers and citations.

Publication bias can lead to a distorted view of the scientific evidence, as it may overemphasize the benefits and underestimate the risks or limitations of medical interventions. This can have serious consequences for clinical decision-making, patient care, and public health policies. Therefore, it is essential to minimize publication bias by encouraging and facilitating the registration, reporting, and dissemination of all research results, regardless of their outcome.

Phase II clinical trials are a type of medical research study that aims to assess the safety and effectiveness of a new drug or intervention in a specific patient population. These studies typically follow successful completion of Phase I clinical trials, which focus primarily on evaluating the safety and dosage of the treatment in a small group of healthy volunteers.

In Phase II clinical trials, the treatment is tested in a larger group of patients (usually several hundred) who have the condition or disease that the treatment is intended to treat. The main goals of these studies are to:

1. Determine the optimal dosage range for the treatment
2. Evaluate the safety and side effects of the treatment at different doses
3. Assess how well the treatment works in treating the target condition or disease

Phase II clinical trials are typically randomized, controlled studies, meaning that participants are randomly assigned to receive either the new treatment or a comparison group, such as a placebo or standard of care. The study is also often blinded, meaning that neither the participants nor the researchers know who is receiving which treatment. This helps to minimize bias and ensure that the results are due to the treatment itself rather than other factors.

Overall, Phase II clinical trials play an important role in determining whether a new drug or intervention is safe and effective enough to move on to larger, more expensive Phase III clinical trials, which involve even larger groups of patients and are designed to confirm and expand upon the results of Phase II studies.

Medical Definition:

"Risk factors" are any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury. They can be divided into modifiable and non-modifiable risk factors. Modifiable risk factors are those that can be changed through lifestyle choices or medical treatment, while non-modifiable risk factors are inherent traits such as age, gender, or genetic predisposition. Examples of modifiable risk factors include smoking, alcohol consumption, physical inactivity, and unhealthy diet, while non-modifiable risk factors include age, sex, and family history. It is important to note that having a risk factor does not guarantee that a person will develop the disease, but rather indicates an increased susceptibility.

Linkage disequilibrium (LD) is a term used in genetics that refers to the non-random association of alleles at different loci (genetic locations) on a chromosome. This means that certain combinations of genetic variants, or alleles, at different loci occur more frequently together in a population than would be expected by chance.

Linkage disequilibrium can arise due to various factors such as genetic drift, selection, mutation, and population structure. It is often used in the context of genetic mapping studies to identify regions of the genome that are associated with particular traits or diseases. High levels of LD in a region of the genome suggest that the loci within that region are in linkage, meaning they tend to be inherited together.

The degree of LD between two loci can be measured using various statistical methods, such as D' and r-squared. These measures provide information about the strength and direction of the association between alleles at different loci, which can help researchers identify causal genetic variants underlying complex traits or diseases.

Sensitivity and specificity are statistical measures used to describe the performance of a diagnostic test or screening tool in identifying true positive and true negative results.

* Sensitivity refers to the proportion of people who have a particular condition (true positives) who are correctly identified by the test. It is also known as the "true positive rate" or "recall." A highly sensitive test will identify most or all of the people with the condition, but may also produce more false positives.
* Specificity refers to the proportion of people who do not have a particular condition (true negatives) who are correctly identified by the test. It is also known as the "true negative rate." A highly specific test will identify most or all of the people without the condition, but may also produce more false negatives.

In medical testing, both sensitivity and specificity are important considerations when evaluating a diagnostic test. High sensitivity is desirable for screening tests that aim to identify as many cases of a condition as possible, while high specificity is desirable for confirmatory tests that aim to rule out the condition in people who do not have it.

It's worth noting that sensitivity and specificity are often influenced by factors such as the prevalence of the condition in the population being tested, the threshold used to define a positive result, and the reliability and validity of the test itself. Therefore, it's important to consider these factors when interpreting the results of a diagnostic test.

"Endpoint determination" is a medical term that refers to the process of deciding when a clinical trial or study should be stopped or concluded based on the outcomes or results that have been observed. The endpoint of a study is the primary outcome or result that the study is designed to investigate and measure.

In endpoint determination, researchers use pre-specified criteria, such as statistical significance levels or safety concerns, to evaluate whether the study has met its objectives or if there are any significant benefits or risks associated with the intervention being studied. The decision to end a study early can be based on various factors, including the achievement of a predefined level of efficacy, the emergence of unexpected safety issues, or the realization that the study is unlikely to achieve its intended goals.

Endpoint determination is an important aspect of clinical trial design and conduct, as it helps ensure that studies are conducted in an ethical and scientifically rigorous manner, and that their results can be used to inform medical practice and policy.

Population Genetics is a subfield of genetics that deals with the genetic composition of populations and how this composition changes over time. It involves the study of the frequency and distribution of genes and genetic variations in populations, as well as the evolutionary forces that contribute to these patterns, such as mutation, gene flow, genetic drift, and natural selection.

Population genetics can provide insights into a wide range of topics, including the history and relationships between populations, the genetic basis of diseases and other traits, and the potential impacts of environmental changes on genetic diversity. This field is important for understanding evolutionary processes at the population level and has applications in areas such as conservation biology, medical genetics, and forensic science.

Genetic variation refers to the differences in DNA sequences among individuals and populations. These variations can result from mutations, genetic recombination, or gene flow between populations. Genetic variation is essential for evolution by providing the raw material upon which natural selection acts. It can occur within a single gene, between different genes, or at larger scales, such as differences in the number of chromosomes or entire sets of chromosomes. The study of genetic variation is crucial in understanding the genetic basis of diseases and traits, as well as the evolutionary history and relationships among species.

Oligonucleotide Array Sequence Analysis is a type of microarray analysis that allows for the simultaneous measurement of the expression levels of thousands of genes in a single sample. In this technique, oligonucleotides (short DNA sequences) are attached to a solid support, such as a glass slide, in a specific pattern. These oligonucleotides are designed to be complementary to specific target mRNA sequences from the sample being analyzed.

During the analysis, labeled RNA or cDNA from the sample is hybridized to the oligonucleotide array. The level of hybridization is then measured and used to determine the relative abundance of each target sequence in the sample. This information can be used to identify differences in gene expression between samples, which can help researchers understand the underlying biological processes involved in various diseases or developmental stages.

It's important to note that this technique requires specialized equipment and bioinformatics tools for data analysis, as well as careful experimental design and validation to ensure accurate and reproducible results.

I'm sorry for any confusion, but "Linear Models" is actually a term from the field of statistics and machine learning, rather than medicine. A linear model is a type of statistical model that is used to analyze the relationship between two or more variables. In a linear model, the relationship between the dependent variable (the outcome or result) and the independent variable(s) (the factors being studied) is assumed to be linear, meaning that it can be described by a straight line on a graph.

The equation for a simple linear model with one independent variable (x) and one dependent variable (y) looks like this:

y = β0 + β1*x + ε

In this equation, β0 is the y-intercept or the value of y when x equals zero, β1 is the slope or the change in y for each unit increase in x, and ε is the error term or the difference between the actual values of y and the predicted values of y based on the linear model.

Linear models are widely used in medical research to study the relationship between various factors (such as exposure to a risk factor or treatment) and health outcomes (such as disease incidence or mortality). They can also be used to adjust for confounding variables, which are factors that may influence both the independent variable and the dependent variable, and thus affect the observed relationship between them.

Genetic polymorphism refers to the occurrence of multiple forms (called alleles) of a particular gene within a population. These variations in the DNA sequence do not generally affect the function or survival of the organism, but they can contribute to differences in traits among individuals. Genetic polymorphisms can be caused by single nucleotide changes (SNPs), insertions or deletions of DNA segments, or other types of genetic rearrangements. They are important for understanding genetic diversity and evolution, as well as for identifying genetic factors that may contribute to disease susceptibility in humans.

A meta-analysis is a statistical method used to combine and summarize the results of multiple independent studies, with the aim of increasing statistical power, improving estimates of effect size, and identifying sources of heterogeneity. It involves systematically searching for and selecting relevant studies, assessing their quality and risk of bias, extracting and analyzing data using appropriate statistical models, and interpreting the findings in the context of the existing literature. Meta-analyses can provide more reliable evidence than individual studies, especially when the results are inconsistent or inconclusive, and can inform clinical guidelines, public health policies, and future research directions.

In the field of medicine, "time factors" refer to the duration of symptoms or time elapsed since the onset of a medical condition, which can have significant implications for diagnosis and treatment. Understanding time factors is crucial in determining the progression of a disease, evaluating the effectiveness of treatments, and making critical decisions regarding patient care.

For example, in stroke management, "time is brain," meaning that rapid intervention within a specific time frame (usually within 4.5 hours) is essential to administering tissue plasminogen activator (tPA), a clot-busting drug that can minimize brain damage and improve patient outcomes. Similarly, in trauma care, the "golden hour" concept emphasizes the importance of providing definitive care within the first 60 minutes after injury to increase survival rates and reduce morbidity.

Time factors also play a role in monitoring the progression of chronic conditions like diabetes or heart disease, where regular follow-ups and assessments help determine appropriate treatment adjustments and prevent complications. In infectious diseases, time factors are crucial for initiating antibiotic therapy and identifying potential outbreaks to control their spread.

Overall, "time factors" encompass the significance of recognizing and acting promptly in various medical scenarios to optimize patient outcomes and provide effective care.

An effect modifier in epidemiology refers to a variable that influences the direction or strength of the association between an exposure and an outcome. In other words, it is a factor that changes the effect of the exposure on the risk of developing a disease or condition. When there is effect modification, the relationship between the exposure and the outcome may differ depending on the level or category of the effect modifier.

Effect modification is an important concept in epidemiology because it can help identify subgroups of the population that are more or less susceptible to the effects of a particular exposure. For example, the association between smoking and lung cancer may be stronger among people who have a certain genetic variant compared to those who do not. In this case, the genetic variant is an effect modifier because it changes the strength of the association between smoking and lung cancer.

Effect modification should be distinguished from confounding, which is a type of bias that can occur when a third variable is associated with both the exposure and the outcome and affects the observed association between them. Unlike effect modification, confounding can be controlled for using statistical methods such as stratification or regression analysis.

A "false positive reaction" in medical testing refers to a situation where a diagnostic test incorrectly indicates the presence of a specific condition or disease in an individual who does not actually have it. This occurs when the test results give a positive outcome, while the true health status of the person is negative or free from the condition being tested for.

False positive reactions can be caused by various factors including:

1. Presence of unrelated substances that interfere with the test result (e.g., cross-reactivity between similar molecules).
2. Low specificity of the test, which means it may detect other conditions or irrelevant factors as positive.
3. Contamination during sample collection, storage, or analysis.
4. Human errors in performing or interpreting the test results.

False positive reactions can have significant consequences, such as unnecessary treatments, anxiety, and increased healthcare costs. Therefore, it is essential to confirm any positive test result with additional tests or clinical evaluations before making a definitive diagnosis.

Gene expression profiling is a laboratory technique used to measure the activity (expression) of thousands of genes at once. This technique allows researchers and clinicians to identify which genes are turned on or off in a particular cell, tissue, or organism under specific conditions, such as during health, disease, development, or in response to various treatments.

The process typically involves isolating RNA from the cells or tissues of interest, converting it into complementary DNA (cDNA), and then using microarray or high-throughput sequencing technologies to determine which genes are expressed and at what levels. The resulting data can be used to identify patterns of gene expression that are associated with specific biological states or processes, providing valuable insights into the underlying molecular mechanisms of diseases and potential targets for therapeutic intervention.

In recent years, gene expression profiling has become an essential tool in various fields, including cancer research, drug discovery, and personalized medicine, where it is used to identify biomarkers of disease, predict patient outcomes, and guide treatment decisions.

Cluster analysis is a statistical method used to group similar objects or data points together based on their characteristics or features. In medical and healthcare research, cluster analysis can be used to identify patterns or relationships within complex datasets, such as patient records or genetic information. This technique can help researchers to classify patients into distinct subgroups based on their symptoms, diagnoses, or other variables, which can inform more personalized treatment plans or public health interventions.

Cluster analysis involves several steps, including:

1. Data preparation: The researcher must first collect and clean the data, ensuring that it is complete and free from errors. This may involve removing outlier values or missing data points.
2. Distance measurement: Next, the researcher must determine how to measure the distance between each pair of data points. Common methods include Euclidean distance (the straight-line distance between two points) or Manhattan distance (the distance between two points along a grid).
3. Clustering algorithm: The researcher then applies a clustering algorithm, which groups similar data points together based on their distances from one another. Common algorithms include hierarchical clustering (which creates a tree-like structure of clusters) or k-means clustering (which assigns each data point to the nearest centroid).
4. Validation: Finally, the researcher must validate the results of the cluster analysis by evaluating the stability and robustness of the clusters. This may involve re-running the analysis with different distance measures or clustering algorithms, or comparing the results to external criteria.

Cluster analysis is a powerful tool for identifying patterns and relationships within complex datasets, but it requires careful consideration of the data preparation, distance measurement, and validation steps to ensure accurate and meaningful results.

A confidence interval (CI) is a range of values that is likely to contain the true value of a population parameter with a certain level of confidence. It is commonly used in statistical analysis to express the uncertainty associated with estimates derived from sample data.

For example, if we calculate a 95% confidence interval for the mean height of a population based on a sample of individuals, we can say that we are 95% confident that the true population mean height falls within the calculated range. The width of the confidence interval gives us an idea of how precise our estimate is - narrower intervals indicate more precise estimates, while wider intervals suggest greater uncertainty.

Confidence intervals are typically calculated using statistical formulas that take into account the sample size, standard deviation, and level of confidence desired. They can be used to compare different groups or to evaluate the effectiveness of interventions in medical research.

Genetic association studies are a type of epidemiological research that aims to identify statistical associations between genetic variations and particular traits or diseases. These studies typically compare the frequency of specific genetic markers, such as single nucleotide polymorphisms (SNPs), in individuals with a given trait or disease to those without it.

The goal of genetic association studies is to identify genetic factors that contribute to the risk of developing common complex diseases, such as diabetes, heart disease, or cancer. By identifying these genetic associations, researchers hope to gain insights into the underlying biological mechanisms of these diseases and develop new strategies for prevention, diagnosis, and treatment.

It's important to note that while genetic association studies can identify statistical associations between genetic markers and traits or diseases, they cannot prove causality. Further research is needed to confirm and validate these findings and to understand the functional consequences of the identified genetic variants.

Genetic markers are specific segments of DNA that are used in genetic mapping and genotyping to identify specific genetic locations, diseases, or traits. They can be composed of short tandem repeats (STRs), single nucleotide polymorphisms (SNPs), restriction fragment length polymorphisms (RFLPs), or variable number tandem repeats (VNTRs). These markers are useful in various fields such as genetic research, medical diagnostics, forensic science, and breeding programs. They can help to track inheritance patterns, identify genetic predispositions to diseases, and solve crimes by linking biological evidence to suspects or victims.

Analysis of Variance (ANOVA) is a statistical technique used to compare the means of two or more groups and determine whether there are any significant differences between them. It is a way to analyze the variance in a dataset to determine whether the variability between groups is greater than the variability within groups, which can indicate that the groups are significantly different from one another.

ANOVA is based on the concept of partitioning the total variance in a dataset into two components: variance due to differences between group means (also known as "between-group variance") and variance due to differences within each group (also known as "within-group variance"). By comparing these two sources of variance, ANOVA can help researchers determine whether any observed differences between groups are statistically significant, or whether they could have occurred by chance.

ANOVA is a widely used technique in many areas of research, including biology, psychology, engineering, and business. It is often used to compare the means of two or more experimental groups, such as a treatment group and a control group, to determine whether the treatment had a significant effect. ANOVA can also be used to compare the means of different populations or subgroups within a population, to identify any differences that may exist between them.

Selection bias is a type of statistical bias that occurs when the sample used in a study is not representative of the population as a whole, typically because of the way the sample was selected or because some members of the intended sample were excluded. This can lead to skewed or inaccurate results, as the sample may not accurately reflect the characteristics and behaviors of the entire population.

Selection bias can occur in various ways, such as through self-selection (when individuals choose whether or not to participate in a study), through the use of nonrandom sampling methods (such as convenience sampling or snowball sampling), or through the exclusion of certain groups or individuals from the sample. This type of bias is particularly problematic in observational studies, as it can be difficult to control for all of the factors that may influence the results.

To minimize the risk of selection bias, researchers often use random sampling methods (such as simple random sampling or stratified random sampling) to ensure that the sample is representative of the population. They may also take steps to increase the diversity of the sample and to reduce the likelihood of self-selection. By carefully designing and implementing their studies, researchers can help to minimize the impact of selection bias on their results and improve the validity and reliability of their findings.

Genetic processes refer to the various biochemical interactions and cellular events that occur within an organism to maintain, transmit, and express genetic information. These processes include:

1. Replication: The process by which DNA molecules are copied exactly before cell division, ensuring that each new cell receives an identical copy of the genome.

2. Transcription: The conversion of genetic information encoded in DNA into RNA, a single-stranded molecule that serves as a template for protein synthesis or can have other regulatory functions.

3. RNA Processing: The modification and maturation of RNA transcripts, including capping, tailing, splicing, and editing, which result in mature mRNAs, rRNAs, tRNAs, and other non-coding RNAs.

4. Translation: The process by which the genetic code present in mRNA is translated into a specific sequence of amino acids during protein synthesis, catalyzed by ribosomes and mediated by tRNAs and various translation factors.

5. Protein Folding and Modification: After translation, proteins undergo folding to attain their native conformation and may be further modified through processes such as cleavage, glycosylation, phosphorylation, or ubiquitination, which can influence protein stability, localization, or function.

6. Genetic Inheritance: The transmission of genetic information from parents to offspring through the processes of meiosis and fertilization, resulting in the formation of genetically unique individuals.

7. Gene Regulation: The control of gene expression at various levels, including transcriptional, post-transcriptional, translational, and post-translational regulation, which enables cells to respond to developmental cues and environmental stimuli.

8. Mutation and Repair: Occasional changes in the DNA sequence, known as mutations, can occur due to errors during replication, exposure to genotoxic agents, or through other mechanisms. Cells have various DNA repair pathways that help maintain genome stability by correcting these errors.

9. Epigenetic Modifications: Chemical modifications of DNA and histone proteins that do not alter the DNA sequence but can influence gene expression and chromatin structure, often in a heritable manner. These modifications include DNA methylation, histone acetylation, and various other covalent marks on histones.

10. Genome Rearrangements: Large-scale changes in the genome, such as chromosomal translocations, deletions, duplications, or inversions, can have significant consequences for gene expression and function, potentially leading to phenotypic variation or disease.

I am not aware of a widely accepted medical definition for the term "software," as it is more commonly used in the context of computer science and technology. Software refers to programs, data, and instructions that are used by computers to perform various tasks. It does not have direct relevance to medical fields such as anatomy, physiology, or clinical practice. If you have any questions related to medicine or healthcare, I would be happy to try to help with those instead!

I'm sorry for any confusion, but "Mathematical Computing" is not a recognized medical term. Mathematical computing is a branch of computer science that focuses on the development and analysis of algorithms and computational methods for solving mathematical problems. It involves the use of computers to perform mathematical calculations and simulations, and it includes various subfields such as numerical analysis, symbolic computation, and computational geometry. If you have any questions about a medical term or concept, I would be happy to help with that instead!

Clinical protocols, also known as clinical practice guidelines or care paths, are systematically developed statements that assist healthcare professionals and patients in making decisions about the appropriate healthcare for specific clinical circumstances. They are based on a thorough evaluation of the available scientific evidence and consist of a set of recommendations that are designed to optimize patient outcomes, improve the quality of care, and reduce unnecessary variations in practice. Clinical protocols may cover a wide range of topics, including diagnosis, treatment, follow-up, and disease prevention, and are developed by professional organizations, government agencies, and other groups with expertise in the relevant field.

The odds ratio (OR) is a statistical measure used in epidemiology and research to estimate the association between an exposure and an outcome. It represents the odds that an event will occur in one group versus the odds that it will occur in another group, assuming that all other factors are held constant.

In medical research, the odds ratio is often used to quantify the strength of the relationship between a risk factor (exposure) and a disease outcome. An OR of 1 indicates no association between the exposure and the outcome, while an OR greater than 1 suggests that there is a positive association between the two. Conversely, an OR less than 1 implies a negative association.

It's important to note that the odds ratio is not the same as the relative risk (RR), which compares the incidence rates of an outcome in two groups. While the OR can approximate the RR when the outcome is rare, they are not interchangeable and can lead to different conclusions about the association between an exposure and an outcome.

A cohort study is a type of observational study in which a group of individuals who share a common characteristic or exposure are followed up over time to determine the incidence of a specific outcome or outcomes. The cohort, or group, is defined based on the exposure status (e.g., exposed vs. unexposed) and then monitored prospectively to assess for the development of new health events or conditions.

Cohort studies can be either prospective or retrospective in design. In a prospective cohort study, participants are enrolled and followed forward in time from the beginning of the study. In contrast, in a retrospective cohort study, researchers identify a cohort that has already been assembled through medical records, insurance claims, or other sources and then look back in time to assess exposure status and health outcomes.

Cohort studies are useful for establishing causality between an exposure and an outcome because they allow researchers to observe the temporal relationship between the two. They can also provide information on the incidence of a disease or condition in different populations, which can be used to inform public health policy and interventions. However, cohort studies can be expensive and time-consuming to conduct, and they may be subject to bias if participants are not representative of the population or if there is loss to follow-up.

Regression analysis is a statistical technique used in medicine, as well as in other fields, to examine the relationship between one or more independent variables (predictors) and a dependent variable (outcome). It allows for the estimation of the average change in the outcome variable associated with a one-unit change in an independent variable, while controlling for the effects of other independent variables. This technique is often used to identify risk factors for diseases or to evaluate the effectiveness of medical interventions. In medical research, regression analysis can be used to adjust for potential confounding variables and to quantify the relationship between exposures and health outcomes. It can also be used in predictive modeling to estimate the probability of a particular outcome based on multiple predictors.

Nonparametric statistics is a branch of statistics that does not rely on assumptions about the distribution of variables in the population from which the sample is drawn. In contrast to parametric methods, nonparametric techniques make fewer assumptions about the data and are therefore more flexible in their application. Nonparametric tests are often used when the data do not meet the assumptions required for parametric tests, such as normality or equal variances.

Nonparametric statistical methods include tests such as the Wilcoxon rank-sum test (also known as the Mann-Whitney U test) for comparing two independent groups, the Wilcoxon signed-rank test for comparing two related groups, and the Kruskal-Wallis test for comparing more than two independent groups. These tests use the ranks of the data rather than the actual values to make comparisons, which allows them to be used with ordinal or continuous data that do not meet the assumptions of parametric tests.

Overall, nonparametric statistics provide a useful set of tools for analyzing data in situations where the assumptions of parametric methods are not met, and can help researchers draw valid conclusions from their data even when the data are not normally distributed or have other characteristics that violate the assumptions of parametric tests.

A haplotype is a group of genes or DNA sequences that are inherited together from a single parent. It refers to a combination of alleles (variant forms of a gene) that are located on the same chromosome and are usually transmitted as a unit. Haplotypes can be useful in tracing genetic ancestry, understanding the genetic basis of diseases, and developing personalized medical treatments.

In population genetics, haplotypes are often used to study patterns of genetic variation within and between populations. By comparing haplotype frequencies across populations, researchers can infer historical events such as migrations, population expansions, and bottlenecks. Additionally, haplotypes can provide information about the evolutionary history of genes and genomic regions.

In clinical genetics, haplotypes can be used to identify genetic risk factors for diseases or to predict an individual's response to certain medications. For example, specific haplotypes in the HLA gene region have been associated with increased susceptibility to certain autoimmune diseases, while other haplotypes in the CYP450 gene family can affect how individuals metabolize drugs.

Overall, haplotypes provide a powerful tool for understanding the genetic basis of complex traits and diseases, as well as for developing personalized medical treatments based on an individual's genetic makeup.

Prospective studies, also known as longitudinal studies, are a type of cohort study in which data is collected forward in time, following a group of individuals who share a common characteristic or exposure over a period of time. The researchers clearly define the study population and exposure of interest at the beginning of the study and follow up with the participants to determine the outcomes that develop over time. This type of study design allows for the investigation of causal relationships between exposures and outcomes, as well as the identification of risk factors and the estimation of disease incidence rates. Prospective studies are particularly useful in epidemiology and medical research when studying diseases with long latency periods or rare outcomes.

Genetic linkage is the phenomenon where two or more genetic loci (locations on a chromosome) tend to be inherited together because they are close to each other on the same chromosome. This occurs during the process of sexual reproduction, where homologous chromosomes pair up and exchange genetic material through a process called crossing over.

The closer two loci are to each other on a chromosome, the lower the probability that they will be separated by a crossover event. As a result, they are more likely to be inherited together and are said to be linked. The degree of linkage between two loci can be measured by their recombination frequency, which is the percentage of meiotic events in which a crossover occurs between them.

Linkage analysis is an important tool in genetic research, as it allows researchers to identify and map genes that are associated with specific traits or diseases. By analyzing patterns of linkage between markers (identifiable DNA sequences) and phenotypes (observable traits), researchers can infer the location of genes that contribute to those traits or diseases on chromosomes.

A questionnaire in the medical context is a standardized, systematic, and structured tool used to gather information from individuals regarding their symptoms, medical history, lifestyle, or other health-related factors. It typically consists of a series of written questions that can be either self-administered or administered by an interviewer. Questionnaires are widely used in various areas of healthcare, including clinical research, epidemiological studies, patient care, and health services evaluation to collect data that can inform diagnosis, treatment planning, and population health management. They provide a consistent and organized method for obtaining information from large groups or individual patients, helping to ensure accurate and comprehensive data collection while minimizing bias and variability in the information gathered.

Chromosome mapping, also known as physical mapping, is the process of determining the location and order of specific genes or genetic markers on a chromosome. This is typically done by using various laboratory techniques to identify landmarks along the chromosome, such as restriction enzyme cutting sites or patterns of DNA sequence repeats. The resulting map provides important information about the organization and structure of the genome, and can be used for a variety of purposes, including identifying the location of genes associated with genetic diseases, studying evolutionary relationships between organisms, and developing genetic markers for use in breeding or forensic applications.

Prevalence, in medical terms, refers to the total number of people in a given population who have a particular disease or condition at a specific point in time, or over a specified period. It is typically expressed as a percentage or a ratio of the number of cases to the size of the population. Prevalence differs from incidence, which measures the number of new cases that develop during a certain period.

Neoplasms are abnormal growths of cells or tissues in the body that serve no physiological function. They can be benign (non-cancerous) or malignant (cancerous). Benign neoplasms are typically slow growing and do not spread to other parts of the body, while malignant neoplasms are aggressive, invasive, and can metastasize to distant sites.

Neoplasms occur when there is a dysregulation in the normal process of cell division and differentiation, leading to uncontrolled growth and accumulation of cells. This can result from genetic mutations or other factors such as viral infections, environmental exposures, or hormonal imbalances.

Neoplasms can develop in any organ or tissue of the body and can cause various symptoms depending on their size, location, and type. Treatment options for neoplasms include surgery, radiation therapy, chemotherapy, immunotherapy, and targeted therapy, among others.

A feasibility study is a preliminary investigation or analysis conducted to determine the viability of a proposed project, program, or product. In the medical field, feasibility studies are often conducted before implementing new treatments, procedures, equipment, or facilities. These studies help to assess the practicality and effectiveness of the proposed intervention, as well as its potential benefits and risks.

Feasibility studies in healthcare typically involve several steps:

1. Problem identification: Clearly define the problem that the proposed project, program, or product aims to address.
2. Objectives setting: Establish specific, measurable, achievable, relevant, and time-bound (SMART) objectives for the study.
3. Literature review: Conduct a thorough review of existing research and best practices related to the proposed intervention.
4. Methodology development: Design a methodology for data collection and analysis that will help answer the research questions and achieve the study's objectives.
5. Resource assessment: Evaluate the availability and adequacy of resources, including personnel, time, and finances, required to carry out the proposed intervention.
6. Risk assessment: Identify potential risks and challenges associated with the implementation of the proposed intervention and develop strategies to mitigate them.
7. Cost-benefit analysis: Estimate the costs and benefits of the proposed intervention, including direct and indirect costs, as well as short-term and long-term benefits.
8. Stakeholder engagement: Engage relevant stakeholders, such as patients, healthcare providers, administrators, and policymakers, to gather their input and support for the proposed intervention.
9. Decision-making: Based on the findings of the feasibility study, make an informed decision about whether or not to proceed with the proposed project, program, or product.

Feasibility studies are essential in healthcare as they help ensure that resources are allocated efficiently and effectively, and that interventions are evidence-based, safe, and beneficial for patients.

The double-blind method is a study design commonly used in research, including clinical trials, to minimize bias and ensure the objectivity of results. In this approach, both the participants and the researchers are unaware of which group the participants are assigned to, whether it be the experimental group or the control group. This means that neither the participants nor the researchers know who is receiving a particular treatment or placebo, thus reducing the potential for bias in the evaluation of outcomes. The assignment of participants to groups is typically done by a third party not involved in the study, and the codes are only revealed after all data have been collected and analyzed.

Medical Definition:

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic imaging technique that uses a strong magnetic field and radio waves to create detailed cross-sectional or three-dimensional images of the internal structures of the body. The patient lies within a large, cylindrical magnet, and the scanner detects changes in the direction of the magnetic field caused by protons in the body. These changes are then converted into detailed images that help medical professionals to diagnose and monitor various medical conditions, such as tumors, injuries, or diseases affecting the brain, spinal cord, heart, blood vessels, joints, and other internal organs. MRI does not use radiation like computed tomography (CT) scans.

A human genome is the complete set of genetic information contained within the 23 pairs of chromosomes found in the nucleus of most human cells. It includes all of the genes, which are segments of DNA that contain the instructions for making proteins, as well as non-coding regions of DNA that regulate gene expression and provide structural support to the chromosomes.

The human genome contains approximately 3 billion base pairs of DNA and is estimated to contain around 20,000-25,000 protein-coding genes. The sequencing of the human genome was completed in 2003 as part of the Human Genome Project, which has had a profound impact on our understanding of human biology, disease, and evolution.

I apologize for any confusion, but "Pinaceae" is not a medical term. It is a taxonomic category in botany, referring to the pine family of coniferous trees and shrubs. This family includes familiar plants such as pines, firs, spruces, and hemlocks.

If you have any medical terminology or concepts that you would like me to define or explain, please let me know!

I'm sorry for any confusion, but "United States" is a geopolitical entity, specifically the name of the country consisting of 50 states, and it is not a medical term or concept. Therefore, I can't provide a medical definition for it. If you have any questions related to health, medicine, or biology, I would be happy to try to help answer those!

Diagnostic techniques and procedures are methods used by medical professionals to identify the cause of symptoms, illnesses, or diseases. These can include physical examinations, patient interviews, review of medical history, and various diagnostic tests. Diagnostic tests may involve invasive procedures such as biopsies or surgical interventions, or non-invasive imaging techniques like X-rays, CT scans, MRI scans, or ultrasounds. Functional tests, such as stress testing or electroencephalogram (EEG), can also be used to evaluate the functioning of specific organs or systems in the body. Laboratory tests, including blood tests, urine tests, and genetic tests, are also common diagnostic procedures. The choice of diagnostic technique or procedure depends on the presenting symptoms, the patient's medical history, and the suspected underlying condition.

A "periodical" in the context of medicine typically refers to a type of publication that is issued regularly, such as on a monthly or quarterly basis. These publications include peer-reviewed journals, magazines, and newsletters that focus on medical research, education, and practice. They may contain original research articles, review articles, case reports, editorials, letters to the editor, and other types of content related to medical science and clinical practice.

As a "Topic," periodicals in medicine encompass various aspects such as their role in disseminating new knowledge, their impact on clinical decision-making, their quality control measures, and their ethical considerations. Medical periodicals serve as a crucial resource for healthcare professionals, researchers, students, and other stakeholders to stay updated on the latest developments in their field and to share their findings with others.

I'm sorry for any confusion, but "publishing" is not a term that has a medical definition. Publishing typically refers to the process of preparing and disseminating information, such as books, journals, or articles, to the public or a specific audience. It could involve both print and digital media. If you have any questions related to medicine or healthcare, I'd be happy to try to help answer those!

A multicenter study is a type of clinical research study that involves multiple centers or institutions. These studies are often conducted to increase the sample size and diversity of the study population, which can improve the generalizability of the study results. In a multicenter study, data is collected from participants at multiple sites and then analyzed together to identify patterns, trends, and relationships in the data. This type of study design can be particularly useful for researching rare diseases or conditions, or for testing new treatments or interventions that require a large number of participants.

Multicenter studies can be either interventional (where participants are randomly assigned to receive different treatments or interventions) or observational (where researchers collect data on participants' characteristics and outcomes without intervening). In both cases, it is important to ensure standardization of data collection and analysis procedures across all study sites to minimize bias and ensure the validity and reliability of the results.

Multicenter studies can provide valuable insights into the effectiveness and safety of new treatments or interventions, as well as contribute to our understanding of disease mechanisms and risk factors. However, they can also be complex and expensive to conduct, requiring careful planning, coordination, and management to ensure their success.

Longitudinal studies are a type of research design where data is collected from the same subjects repeatedly over a period of time, often years or even decades. These studies are used to establish patterns of changes and events over time, and can help researchers identify causal relationships between variables. They are particularly useful in fields such as epidemiology, psychology, and sociology, where the focus is on understanding developmental trends and the long-term effects of various factors on health and behavior.

In medical research, longitudinal studies can be used to track the progression of diseases over time, identify risk factors for certain conditions, and evaluate the effectiveness of treatments or interventions. For example, a longitudinal study might follow a group of individuals over several decades to assess their exposure to certain environmental factors and their subsequent development of chronic diseases such as cancer or heart disease. By comparing data collected at multiple time points, researchers can identify trends and correlations that may not be apparent in shorter-term studies.

Longitudinal studies have several advantages over other research designs, including their ability to establish temporal relationships between variables, track changes over time, and reduce the impact of confounding factors. However, they also have some limitations, such as the potential for attrition (loss of participants over time), which can introduce bias and affect the validity of the results. Additionally, longitudinal studies can be expensive and time-consuming to conduct, requiring significant resources and a long-term commitment from both researchers and study participants.

Controlled clinical trials are a type of medical research study that compare the effects of one or more interventions (e.g., drugs, treatments, or procedures) to a standard of care or placebo in a group of participants who have a specific medical condition. These studies are designed to determine whether an intervention is safe and effective, and they typically involve randomly assigning participants to receive either the experimental intervention or the control.

In a controlled clinical trial, the researchers carefully control and monitor all aspects of the study to minimize bias and ensure that the results are as reliable and valid as possible. This may include using standardized measures to assess outcomes, blinding participants and researchers to treatment assignments, and analyzing data using statistical methods.

Controlled clinical trials are an important part of the process for developing and approving new medical treatments and interventions. They provide valuable information about the safety and efficacy of these interventions, and help to ensure that they are safe and effective for use in clinical practice.

A Clinical Trials Data Monitoring Committee (DTMC), also known as a Data and Safety Monitoring Board (DSMB), is a group of independent experts that oversees the safety and efficacy data of a clinical trial. The committee's primary role is to protect the interests of the study participants and ensure the integrity of the trial by regularly reviewing accumulating data during the trial.

The DTMC typically includes clinicians, statisticians, and other experts who are not involved in the design or conduct of the trial. They review unblinded data from the trial to assess whether any safety concerns have arisen, such as unexpected adverse events, or whether there is evidence that the experimental intervention is significantly more effective or harmful than the control group.

Based on their review, the DTMC may recommend changes to the trial protocol, such as modifying the dose of the experimental intervention, adding or removing study sites, or stopping the trial early if there is clear evidence of benefit or harm. The committee's recommendations are typically confidential and only shared with the trial sponsor and regulatory authorities.

Overall, the role of a DTMC is to ensure that clinical trials are conducted ethically and responsibly, with the safety and well-being of study participants as the top priority.

Patient selection, in the context of medical treatment or clinical research, refers to the process of identifying and choosing appropriate individuals who are most likely to benefit from a particular medical intervention or who meet specific criteria to participate in a study. This decision is based on various factors such as the patient's diagnosis, stage of disease, overall health status, potential risks, and expected benefits. The goal of patient selection is to ensure that the selected individuals will receive the most effective and safe care possible while also contributing to meaningful research outcomes.

Early termination of clinical trials refers to the discontinuation of a medical research study before its planned end date. This can occur for several reasons, including:

1. Safety concerns: If the experimental treatment is found to be harmful or poses significant risks to the participants, the trial may be stopped early to protect their well-being.
2. Efficacy demonstrated: If the experimental treatment shows promising results and is significantly better than the current standard of care, an independent data monitoring committee may recommend stopping the trial early so that the treatment can be made available to all patients as soon as possible.
3. Futility: If it becomes clear that the experimental treatment is unlikely to provide any meaningful benefit compared to the current standard of care, the trial may be stopped early to avoid exposing more participants to unnecessary risks and to allocate resources more efficiently.
4. Insufficient recruitment or funding: If there are not enough participants enrolled in the study or if funding for the trial is withdrawn, it may need to be terminated prematurely.
5. Violation of ethical guidelines or regulations: If the trial is found to be non-compliant with regulatory requirements or ethical standards, it may be stopped early by the sponsor, investigator, or regulatory authorities.

When a clinical trial is terminated early, the data collected up until that point are still analyzed and reported, but the results should be interpreted with caution due to the limited sample size and potential biases introduced by the early termination.

Follow-up studies are a type of longitudinal research that involve repeated observations or measurements of the same variables over a period of time, in order to understand their long-term effects or outcomes. In medical context, follow-up studies are often used to evaluate the safety and efficacy of medical treatments, interventions, or procedures.

In a typical follow-up study, a group of individuals (called a cohort) who have received a particular treatment or intervention are identified and then followed over time through periodic assessments or data collection. The data collected may include information on clinical outcomes, adverse events, changes in symptoms or functional status, and other relevant measures.

The results of follow-up studies can provide important insights into the long-term benefits and risks of medical interventions, as well as help to identify factors that may influence treatment effectiveness or patient outcomes. However, it is important to note that follow-up studies can be subject to various biases and limitations, such as loss to follow-up, recall bias, and changes in clinical practice over time, which must be carefully considered when interpreting the results.

The term "Theoretical Models" is used in various scientific fields, including medicine, to describe a representation of a complex system or phenomenon. It is a simplified framework that explains how different components of the system interact with each other and how they contribute to the overall behavior of the system. Theoretical models are often used in medical research to understand and predict the outcomes of diseases, treatments, or public health interventions.

A theoretical model can take many forms, such as mathematical equations, computer simulations, or conceptual diagrams. It is based on a set of assumptions and hypotheses about the underlying mechanisms that drive the system. By manipulating these variables and observing the effects on the model's output, researchers can test their assumptions and generate new insights into the system's behavior.

Theoretical models are useful for medical research because they allow scientists to explore complex systems in a controlled and systematic way. They can help identify key drivers of disease or treatment outcomes, inform the design of clinical trials, and guide the development of new interventions. However, it is important to recognize that theoretical models are simplifications of reality and may not capture all the nuances and complexities of real-world systems. Therefore, they should be used in conjunction with other forms of evidence, such as experimental data and observational studies, to inform medical decision-making.

Logistic models, specifically logistic regression models, are a type of statistical analysis used in medical and epidemiological research to identify the relationship between the risk of a certain health outcome or disease (dependent variable) and one or more independent variables, such as demographic factors, exposure variables, or other clinical measurements.

In contrast to linear regression models, logistic regression models are used when the dependent variable is binary or dichotomous in nature, meaning it can only take on two values, such as "disease present" or "disease absent." The model uses a logistic function to estimate the probability of the outcome based on the independent variables.

Logistic regression models are useful for identifying risk factors and estimating the strength of associations between exposures and health outcomes, adjusting for potential confounders, and predicting the probability of an outcome given certain values of the independent variables. They can also be used to develop clinical prediction rules or scores that can aid in decision-making and patient care.

Inborn genetic diseases, also known as inherited genetic disorders, are conditions caused by abnormalities in an individual's DNA that are present at conception. These abnormalities can include mutations, deletions, or rearrangements of genes or chromosomes. In many cases, these genetic changes are inherited from one or both parents and may be passed down through families.

Inborn genetic diseases can affect any part of the body and can cause a wide range of symptoms, which can vary in severity depending on the specific disorder. Some genetic disorders are caused by mutations in a single gene, while others are caused by changes in multiple genes or chromosomes. In some cases, environmental factors may also contribute to the development of these conditions.

Examples of inborn genetic diseases include cystic fibrosis, sickle cell anemia, Huntington's disease, Duchenne muscular dystrophy, and Down syndrome. These conditions can have significant impacts on an individual's health and quality of life, and many require ongoing medical management and treatment. In some cases, genetic counseling and testing may be recommended for individuals with a family history of a particular genetic disorder to help them make informed decisions about their reproductive options.

A cross-sectional study is a type of observational research design that examines the relationship between variables at one point in time. It provides a snapshot or a "cross-section" of the population at a particular moment, allowing researchers to estimate the prevalence of a disease or condition and identify potential risk factors or associations.

In a cross-sectional study, data is collected from a sample of participants at a single time point, and the variables of interest are measured simultaneously. This design can be used to investigate the association between exposure and outcome, but it cannot establish causality because it does not follow changes over time.

Cross-sectional studies can be conducted using various data collection methods, such as surveys, interviews, or medical examinations. They are often used in epidemiology to estimate the prevalence of a disease or condition in a population and to identify potential risk factors that may contribute to its development. However, because cross-sectional studies only provide a snapshot of the population at one point in time, they cannot account for changes over time or determine whether exposure preceded the outcome.

Therefore, while cross-sectional studies can be useful for generating hypotheses and identifying potential associations between variables, further research using other study designs, such as cohort or case-control studies, is necessary to establish causality and confirm any findings.

Data collection in the medical context refers to the systematic gathering of information relevant to a specific research question or clinical situation. This process involves identifying and recording data elements, such as demographic characteristics, medical history, physical examination findings, laboratory results, and imaging studies, from various sources including patient interviews, medical records, and diagnostic tests. The data collected is used to support clinical decision-making, inform research hypotheses, and evaluate the effectiveness of treatments or interventions. It is essential that data collection is performed in a standardized and unbiased manner to ensure the validity and reliability of the results.

Maternal-Child Nursing is a specialized field of nursing that focuses on the care of women, newborns, and children in various stages of development, from pregnancy and childbirth to adolescence. This area of nursing requires a deep understanding of the physical, emotional, and psychological needs of mothers and their children during these critical periods. Maternal-Child Nurses provide comprehensive care that includes health promotion, disease prevention, and management of acute and chronic health conditions. They work closely with other healthcare professionals to ensure the best possible outcomes for both mother and child.

I'm sorry for any confusion, but "walruses" is not a medical term. It is the plural form of "walrus," which refers to a large marine mammal known for its distinctive tusks and whiskers. Walruses are native to the Arctic regions and are well-adapted to life in cold waters. If you have any questions about medical terminology or health-related topics, I'd be happy to try to help answer them!

A phenotype is the physical or biochemical expression of an organism's genes, or the observable traits and characteristics resulting from the interaction of its genetic constitution (genotype) with environmental factors. These characteristics can include appearance, development, behavior, and resistance to disease, among others. Phenotypes can vary widely, even among individuals with identical genotypes, due to differences in environmental influences, gene expression, and genetic interactions.

A Severity of Illness Index is a measurement tool used in healthcare to assess the severity of a patient's condition and the risk of mortality or other adverse outcomes. These indices typically take into account various physiological and clinical variables, such as vital signs, laboratory values, and co-morbidities, to generate a score that reflects the patient's overall illness severity.

Examples of Severity of Illness Indices include the Acute Physiology and Chronic Health Evaluation (APACHE) system, the Simplified Acute Physiology Score (SAPS), and the Mortality Probability Model (MPM). These indices are often used in critical care settings to guide clinical decision-making, inform prognosis, and compare outcomes across different patient populations.

It is important to note that while these indices can provide valuable information about a patient's condition, they should not be used as the sole basis for clinical decision-making. Rather, they should be considered in conjunction with other factors, such as the patient's overall clinical presentation, treatment preferences, and goals of care.

Quantitative Trait Loci (QTL) are regions of the genome that are associated with variation in quantitative traits, which are traits that vary continuously in a population and are influenced by multiple genes and environmental factors. QTLs can help to explain how genetic variations contribute to differences in complex traits such as height, blood pressure, or disease susceptibility.

Quantitative trait loci are identified through statistical analysis of genetic markers and trait values in experimental crosses between genetically distinct individuals, such as strains of mice or plants. The location of a QTL is inferred based on the pattern of linkage disequilibrium between genetic markers and the trait of interest. Once a QTL has been identified, further analysis can be conducted to identify the specific gene or genes responsible for the variation in the trait.

It's important to note that QTLs are not themselves genes, but rather genomic regions that contain one or more genes that contribute to the variation in a quantitative trait. Additionally, because QTLs are identified through statistical analysis, they represent probabilistic estimates of the location of genetic factors influencing a trait and may encompass large genomic regions containing multiple genes. Therefore, additional research is often required to fine-map and identify the specific genes responsible for the variation in the trait.

Computational biology is a branch of biology that uses mathematical and computational methods to study biological data, models, and processes. It involves the development and application of algorithms, statistical models, and computational approaches to analyze and interpret large-scale molecular and phenotypic data from genomics, transcriptomics, proteomics, metabolomics, and other high-throughput technologies. The goal is to gain insights into biological systems and processes, develop predictive models, and inform experimental design and hypothesis testing in the life sciences. Computational biology encompasses a wide range of disciplines, including bioinformatics, systems biology, computational genomics, network biology, and mathematical modeling of biological systems.

Pregnancy is a physiological state or condition where a fertilized egg (zygote) successfully implants and grows in the uterus of a woman, leading to the development of an embryo and finally a fetus. This process typically spans approximately 40 weeks, divided into three trimesters, and culminates in childbirth. Throughout this period, numerous hormonal and physical changes occur to support the growing offspring, including uterine enlargement, breast development, and various maternal adaptations to ensure the fetus's optimal growth and well-being.

"Quality control" is a term that is used in many industries, including healthcare and medicine, to describe the systematic process of ensuring that products or services meet certain standards and regulations. In the context of healthcare, quality control often refers to the measures taken to ensure that the care provided to patients is safe, effective, and consistent. This can include processes such as:

1. Implementing standardized protocols and guidelines for care
2. Training and educating staff to follow these protocols
3. Regularly monitoring and evaluating the outcomes of care
4. Making improvements to processes and systems based on data and feedback
5. Ensuring that equipment and supplies are maintained and functioning properly
6. Implementing systems for reporting and addressing safety concerns or errors.

The goal of quality control in healthcare is to provide high-quality, patient-centered care that meets the needs and expectations of patients, while also protecting their safety and well-being.

In the context of medicine, risk is the probability or likelihood of an adverse health effect or the occurrence of a negative event related to treatment or exposure to certain hazards. It is usually expressed as a ratio or percentage and can be influenced by various factors such as age, gender, lifestyle, genetics, and environmental conditions. Risk assessment involves identifying, quantifying, and prioritizing risks to make informed decisions about prevention, mitigation, or treatment strategies.

Risk assessment in the medical context refers to the process of identifying, evaluating, and prioritizing risks to patients, healthcare workers, or the community related to healthcare delivery. It involves determining the likelihood and potential impact of adverse events or hazards, such as infectious diseases, medication errors, or medical devices failures, and implementing measures to mitigate or manage those risks. The goal of risk assessment is to promote safe and high-quality care by identifying areas for improvement and taking action to minimize harm.

Retrospective studies, also known as retrospective research or looking back studies, are a type of observational study that examines data from the past to draw conclusions about possible causal relationships between risk factors and outcomes. In these studies, researchers analyze existing records, medical charts, or previously collected data to test a hypothesis or answer a specific research question.

Retrospective studies can be useful for generating hypotheses and identifying trends, but they have limitations compared to prospective studies, which follow participants forward in time from exposure to outcome. Retrospective studies are subject to biases such as recall bias, selection bias, and information bias, which can affect the validity of the results. Therefore, retrospective studies should be interpreted with caution and used primarily to generate hypotheses for further testing in prospective studies.

A factual database in the medical context is a collection of organized and structured data that contains verified and accurate information related to medicine, healthcare, or health sciences. These databases serve as reliable resources for various stakeholders, including healthcare professionals, researchers, students, and patients, to access evidence-based information for making informed decisions and enhancing knowledge.

Examples of factual medical databases include:

1. PubMed: A comprehensive database of biomedical literature maintained by the US National Library of Medicine (NLM). It contains citations and abstracts from life sciences journals, books, and conference proceedings.
2. MEDLINE: A subset of PubMed, MEDLINE focuses on high-quality, peer-reviewed articles related to biomedicine and health. It is the primary component of the NLM's database and serves as a critical resource for healthcare professionals and researchers worldwide.
3. Cochrane Library: A collection of systematic reviews and meta-analyses focused on evidence-based medicine. The library aims to provide unbiased, high-quality information to support clinical decision-making and improve patient outcomes.
4. OVID: A platform that offers access to various medical and healthcare databases, including MEDLINE, Embase, and PsycINFO. It facilitates the search and retrieval of relevant literature for researchers, clinicians, and students.
5. ClinicalTrials.gov: A registry and results database of publicly and privately supported clinical studies conducted around the world. The platform aims to increase transparency and accessibility of clinical trial data for healthcare professionals, researchers, and patients.
6. UpToDate: An evidence-based, physician-authored clinical decision support resource that provides information on diagnosis, treatment, and prevention of medical conditions. It serves as a point-of-care tool for healthcare professionals to make informed decisions and improve patient care.
7. TRIP Database: A search engine designed to facilitate evidence-based medicine by providing quick access to high-quality resources, including systematic reviews, clinical guidelines, and practice recommendations.
8. National Guideline Clearinghouse (NGC): A database of evidence-based clinical practice guidelines and related documents developed through a rigorous review process. The NGC aims to provide clinicians, healthcare providers, and policymakers with reliable guidance for patient care.
9. DrugBank: A comprehensive, freely accessible online database containing detailed information about drugs, their mechanisms, interactions, and targets. It serves as a valuable resource for researchers, healthcare professionals, and students in the field of pharmacology and drug discovery.
10. Genetic Testing Registry (GTR): A database that provides centralized information about genetic tests, test developers, laboratories offering tests, and clinical validity and utility of genetic tests. It serves as a resource for healthcare professionals, researchers, and patients to make informed decisions regarding genetic testing.

A newborn infant is a baby who is within the first 28 days of life. This period is also referred to as the neonatal period. Newborns require specialized care and attention due to their immature bodily systems and increased vulnerability to various health issues. They are closely monitored for signs of well-being, growth, and development during this critical time.

An ethical review is the process of evaluating and assessing a research study or project that involves human participants, medical interventions, or personal data, to ensure that it is conducted in accordance with ethical principles and standards. The purpose of an ethical review is to protect the rights and welfare of the participants and to minimize any potential harm or risks associated with the research.

The ethical review is typically conducted by an independent committee called an Institutional Review Board (IRB), Research Ethics Committee (REC), or Ethics Review Board (ERB). The committee reviews the study protocol, informed consent procedures, recruitment methods, data collection and management plans, and potential conflicts of interest.

The ethical review process is guided by several key principles, including respect for persons, beneficence, and justice. These principles require that researchers obtain informed consent from participants, avoid causing harm, minimize risks, maximize benefits, and ensure fairness in the selection and treatment of research participants.

Overall, an ethical review is a critical component of responsible conduct in research and helps to ensure that studies are conducted with integrity, transparency, and respect for the rights and welfare of human participants.

The term "Asian Continental Ancestry Group" is a medical/ethnic classification used to describe a person's genetic background and ancestry. According to this categorization, individuals with origins in the Asian continent are grouped together. This includes populations from regions such as East Asia (e.g., China, Japan, Korea), South Asia (e.g., India, Pakistan, Bangladesh), Southeast Asia (e.g., Philippines, Indonesia, Thailand), and Central Asia (e.g., Kazakhstan, Uzbekistan, Tajikistan). It is important to note that this broad categorization may not fully capture the genetic diversity within these regions or accurately reflect an individual's specific ancestral origins.

Epidemiologic methods are systematic approaches used to investigate and understand the distribution, determinants, and outcomes of health-related events or diseases in a population. These methods are applied to study the patterns of disease occurrence and transmission, identify risk factors and causes, and evaluate interventions for prevention and control. The core components of epidemiologic methods include:

1. Descriptive Epidemiology: This involves the systematic collection and analysis of data on the who, what, when, and where of health events to describe their distribution in a population. It includes measures such as incidence, prevalence, mortality, and morbidity rates, as well as geographic and temporal patterns.

2. Analytical Epidemiology: This involves the use of statistical methods to examine associations between potential risk factors and health outcomes. It includes observational studies (cohort, case-control, cross-sectional) and experimental studies (randomized controlled trials). The goal is to identify causal relationships and quantify the strength of associations.

3. Experimental Epidemiology: This involves the design and implementation of interventions or experiments to test hypotheses about disease prevention and control. It includes randomized controlled trials, community trials, and other experimental study designs.

4. Surveillance and Monitoring: This involves ongoing systematic collection, analysis, and interpretation of health-related data for early detection, tracking, and response to health events or diseases.

5. Ethical Considerations: Epidemiologic studies must adhere to ethical principles such as respect for autonomy, beneficence, non-maleficence, and justice. This includes obtaining informed consent, ensuring confidentiality, and minimizing harm to study participants.

Overall, epidemiologic methods provide a framework for investigating and understanding the complex interplay between host, agent, and environmental factors that contribute to the occurrence of health-related events or diseases in populations.

Discriminant analysis is a statistical method used for classifying observations or individuals into distinct categories or groups based on multiple predictor variables. It is commonly used in medical research to help diagnose or predict the presence or absence of a particular condition or disease.

In discriminant analysis, a linear combination of the predictor variables is created, and the resulting function is used to determine the group membership of each observation. The function is derived from the means and variances of the predictor variables for each group, with the goal of maximizing the separation between the groups while minimizing the overlap.

There are two types of discriminant analysis:

1. Linear Discriminant Analysis (LDA): This method assumes that the predictor variables are normally distributed and have equal variances within each group. LDA is used when there are two or more groups to be distinguished.
2. Quadratic Discriminant Analysis (QDA): This method does not assume equal variances within each group, allowing for more flexibility in modeling the distribution of predictor variables. QDA is used when there are two or more groups to be distinguished.

Discriminant analysis can be useful in medical research for developing diagnostic models that can accurately classify patients based on a set of clinical or laboratory measures. It can also be used to identify which predictor variables are most important in distinguishing between different groups, providing insights into the underlying biological mechanisms of disease.

The term "European Continental Ancestry Group" is a medical/ethnic classification that refers to individuals who trace their genetic ancestry to the continent of Europe. This group includes people from various ethnic backgrounds and nationalities, such as Northern, Southern, Eastern, and Western European descent. It is often used in research and medical settings for population studies or to identify genetic patterns and predispositions to certain diseases that may be more common in specific ancestral groups. However, it's important to note that this classification can oversimplify the complex genetic diversity within and between populations, and should be used with caution.

"Age factors" refer to the effects, changes, or differences that age can have on various aspects of health, disease, and medical care. These factors can encompass a wide range of issues, including:

1. Physiological changes: As people age, their bodies undergo numerous physical changes that can affect how they respond to medications, illnesses, and medical procedures. For example, older adults may be more sensitive to certain drugs or have weaker immune systems, making them more susceptible to infections.
2. Chronic conditions: Age is a significant risk factor for many chronic diseases, such as heart disease, diabetes, cancer, and arthritis. As a result, age-related medical issues are common and can impact treatment decisions and outcomes.
3. Cognitive decline: Aging can also lead to cognitive changes, including memory loss and decreased decision-making abilities. These changes can affect a person's ability to understand and comply with medical instructions, leading to potential complications in their care.
4. Functional limitations: Older adults may experience physical limitations that impact their mobility, strength, and balance, increasing the risk of falls and other injuries. These limitations can also make it more challenging for them to perform daily activities, such as bathing, dressing, or cooking.
5. Social determinants: Age-related factors, such as social isolation, poverty, and lack of access to transportation, can impact a person's ability to obtain necessary medical care and affect their overall health outcomes.

Understanding age factors is critical for healthcare providers to deliver high-quality, patient-centered care that addresses the unique needs and challenges of older adults. By taking these factors into account, healthcare providers can develop personalized treatment plans that consider a person's age, physical condition, cognitive abilities, and social circumstances.

Genetic heterogeneity is a phenomenon in genetics where different genetic variations or mutations in various genes can result in the same or similar phenotypic characteristics, disorders, or diseases. This means that multiple genetic alterations can lead to the same clinical presentation, making it challenging to identify the specific genetic cause based on the observed symptoms alone.

There are two main types of genetic heterogeneity:

1. Allelic heterogeneity: Different mutations in the same gene can cause the same or similar disorders. For example, various mutations in the CFTR gene can lead to cystic fibrosis, a genetic disorder affecting the respiratory and digestive systems.
2. Locus heterogeneity: Mutations in different genes can result in the same or similar disorders. For instance, mutations in several genes, such as BRCA1, BRCA2, and PALB2, are associated with an increased risk of developing breast cancer.

Genetic heterogeneity is essential to consider when diagnosing genetic conditions, evaluating recurrence risks, and providing genetic counseling. It highlights the importance of comprehensive genetic testing and interpretation for accurate diagnosis and appropriate management of genetic disorders.

Biomedical research is a branch of scientific research that involves the study of biological processes and diseases in order to develop new treatments and therapies. This type of research often involves the use of laboratory techniques, such as cell culture and genetic engineering, as well as clinical trials in humans. The goal of biomedical research is to advance our understanding of how living organisms function and to find ways to prevent and treat various medical conditions. It encompasses a wide range of disciplines, including molecular biology, genetics, immunology, pharmacology, and neuroscience, among others. Ultimately, the aim of biomedical research is to improve human health and well-being.

Evidence-Based Medicine (EBM) is a medical approach that combines the best available scientific evidence with clinical expertise and patient values to make informed decisions about diagnosis, treatment, and prevention of diseases. It emphasizes the use of systematic research, including randomized controlled trials and meta-analyses, to guide clinical decision making. EBM aims to provide the most effective and efficient care while minimizing variations in practice, reducing errors, and improving patient outcomes.

Medical genetics is the branch of medicine that involves the study of inherited conditions and diseases, as well as the way they are passed down through families. It combines elements of clinical evaluation, laboratory testing, and genetic counseling to help diagnose, manage, and prevent genetic disorders. Medical genetics also includes the study of genetic variation and its role in contributing to both rare and common diseases. Additionally, it encompasses the use of genetic information for pharmacological decision making (pharmacogenomics) and reproductive decision making (preimplantation genetic diagnosis, prenatal testing).

Quality of Life (QOL) is a broad, multidimensional concept that usually includes an individual's physical health, psychological state, level of independence, social relationships, personal beliefs, and their relationship to salient features of their environment. It reflects the impact of disease and treatment on a patient's overall well-being and ability to function in daily life.

The World Health Organization (WHO) defines QOL as "an individual's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns." It is a subjective concept, meaning it can vary greatly from person to person.

In healthcare, QOL is often used as an outcome measure in clinical trials and other research studies to assess the impact of interventions or treatments on overall patient well-being.

Phase I clinical trials are the first stage of testing a new medical treatment or intervention in human subjects. The primary goal of a Phase I trial is to evaluate the safety and tolerability of the experimental treatment, as well as to determine an appropriate dosage range. These studies typically involve a small number of healthy volunteers or patients with the condition of interest, and are designed to assess the pharmacokinetics (how the body absorbs, distributes, metabolizes, and excretes the drug) and pharmacodynamics (the biological effects of the drug on the body) of the experimental treatment. Phase I trials may also provide initial evidence of efficacy, but this is not their primary objective. Overall, the data from Phase I trials help researchers determine whether it is safe to proceed to larger-scale testing in Phase II clinical trials.

I'm sorry for any confusion, but Poisson Distribution is actually a statistical concept rather than a medical term. Here's a general definition:

Poisson Distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space, as long as these events occur with a known average rate and independently of each other. It is often used in fields such as physics, engineering, economics, and medical research to model rare events or low-probability phenomena.

In the context of medical research, Poisson Distribution might be used to analyze the number of adverse events that occur during a clinical trial, the frequency of disease outbreaks in a population, or the rate of successes or failures in a series of experiments.

Prognosis is a medical term that refers to the prediction of the likely outcome or course of a disease, including the chances of recovery or recurrence, based on the patient's symptoms, medical history, physical examination, and diagnostic tests. It is an important aspect of clinical decision-making and patient communication, as it helps doctors and patients make informed decisions about treatment options, set realistic expectations, and plan for future care.

Prognosis can be expressed in various ways, such as percentages, categories (e.g., good, fair, poor), or survival rates, depending on the nature of the disease and the available evidence. However, it is important to note that prognosis is not an exact science and may vary depending on individual factors, such as age, overall health status, and response to treatment. Therefore, it should be used as a guide rather than a definitive forecast.

The Journal Impact Factor (JIF) is a measure of the frequency with which the "average article" in a journal has been cited in a particular year. It is calculated by dividing the number of current year citations to the source items published in that journal during the previous two years. For example, if a journal has an Impact Factor of 3 in 2020, that means articles published in 2018 and 2019 were cited 3 times on average in 2020. It is used to gauge the importance or rank of a journal by comparing the times it's articles are cited relative to other journals in the field. However, it has been criticized for various limitations such as being manipulated by editors and not reflecting the quality of individual articles.

Phase III clinical trials are a type of medical research study that involves testing the safety and efficacy of a new drug, device, or treatment in a large group of people. These studies typically enroll hundreds to thousands of participants, who are randomly assigned to receive either the experimental treatment or a standard of care comparison group.

The primary goal of Phase III clinical trials is to determine whether the new treatment works better than existing treatments and to assess its safety and side effects in a larger population. The data collected from these studies can help regulatory agencies like the U.S. Food and Drug Administration (FDA) decide whether to approve the new treatment for use in the general population.

Phase III clinical trials are usually conducted at multiple centers, often across different countries, to ensure that the results are generalizable to a wide range of patients. Participants may be followed for several years to assess long-term safety and efficacy outcomes.

Overall, Phase III clinical trials play a critical role in ensuring that new treatments are safe and effective before they become widely available to patients.

NIST: Selecting Sample Sizes ASTM E122-07: Standard Practice for Calculating Sample Size to Estimate, With Specified Precision ... ISBN 978-0-471-48900-9. Smith, Scott (8 April 2013). "Determining Sample Size: How to Ensure You Get the Correct Sample Size". ... With more complicated sampling techniques, such as stratified sampling, the sample can often be split up into sub-samples. ... that the total sample size is given by the sum of the sub-sample sizes). Selecting these nh optimally can be done in various ...
PASS is a computer program for estimating sample size or determining the power of a statistical test or confidence interval. ... NCSS LLC also produces NCSS (for statistical analysis). PASS includes over 920 documented sample size and power procedures. ...
"What sample size and power analysis procedures you get in nQuery , Sample Size Software , Power Analysis Software". Official ... and fixed sample size trials. It is most commonly used by biostatisticians to calculate sample size and statistical power for ... While at UCLA and Cedars-Sinai during the 1990s, she wrote the program nQuery Sample Size Software (then named nQuery Advisor ... The software includes calculations for over 1,000 sample sizes and power scenarios. Janet Dixon Elashoff, creator of nQuery, is ...
Wainer and Zwerlig argue that this is an artifact of sample size. Because of the small sample size, the incidence of a certain ... Insensitivity to sample size is a cognitive bias that occurs when people judge the probability of obtaining a sample statistic ... Insensitivity to sample size is a subtype of extension neglect. To illustrate this point, Howard Wainer and Harris L. Zwerling ... Relative neglect of sample size were obtained in a different study of statistically sophisticated psychologists. Tversky and ...
Power, sample size, and the detectable alternative hypothesis are interrelated. The user specifies any two of these three ... Dupont WD and Plummer WD: PS power and sample size program available for free on the Internet. Controlled Clin Trials,1997;18: ... McCrum-Gardner, E: "Sample size and power calculations made simple." International Journal of Therapy and Rehabilitation. 2010 ... PS is an interactive computer program for performing statistical power and sample size calculations. The P program can be used ...
The pps sampling results in a fixed sample size n (as opposed to Poisson sampling which is similar but results in a random ... In survey methodology, probability-proportional-to-size (pps) sampling is a sampling process where each element of the ... Model Assisted Survey Sampling. ISBN 978-0-387-97528-3. Skinner, Chris J. "Probability proportional to size (PPS) sampling." ... Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Nashville, TN: John Wiley & Sons. ISBN 978-0-471-16240-7 v t e (Sampling ...
... samples using local case-control sampling. It is possible to control the sample size by multiplying the acceptance probability ... For a smaller sample size, the same strategy applies. In cases where the number of samples desired is precise, a convenient ... For a larger sample size with c > 1 {\displaystyle c>1} , the factor 2 is improved to 1 + 1 c {\displaystyle 1+{\frac {1}{c ... When the pilot is consistent, the estimates using the samples from local case-control sampling is consistent even under model ...
Pseudo-random number sampling Sample size determination Sampling (case studies) Sampling bias Sampling distribution Sampling ... Steps for using sample size tables: Postulate the effect size of interest, α, and β. Check sample size table Select the table ... Simple Random Sampling, Systematic Sampling, Stratified Sampling, Probability Proportional to Size Sampling, and Cluster or ... The ratio of the size of this random selection (or sample) to the size of the population is called a sampling fraction. There ...
... multiple sampling plans use more than two samples to reach a conclusion. A shorter examination period and smaller sample sizes ... Suppose that we have a lot of sizes M {\displaystyle M} ; a random sample of size N < M {\displaystyle N. ... Although the samples are taken at random, the sampling procedure is still reliable. Acceptance sampling procedures became ... Acceptance sampling uses statistical sampling to determine whether to accept or reject a production lot of material. It has ...
Relying on the sample drawn from these options will yield an unbiased estimator. However, the sample size is no longer fixed ... Multistage sampling Sampling (statistics) Simple random sampling Stratified sampling David Brown, Study Claims Iraq's 'Excess' ... cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is ... In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected ...
Martino, Luca; Elvira, Víctor; Louzada, Francisco (2017). "Effective sample size for importance sampling based on discrepancy ... One related measure is the so-called Effective Sample Size (ESS). Variance is not the only possible cost function for a ... Importance sampling is a Monte Carlo method for evaluating properties of a particular distribution, while only having samples ... Importance sampling is also related to umbrella sampling in computational physics. Depending on the application, the term may ...
The same process is continued until an appropriate sample size remains. Analyses are made with respect to the sample left ... representative sample is taken from a larger sample. Good sub-sampling technique becomes important when the large sample is not ... Coning and quartering is a method used by analytical chemists to reduce the sample size of a powder without creating a ... Riffle boxes are commonly used in mining to reduce the size of crushed rock samples prior to assaying. "IUPAC Gold Book". ...
Auditors can lower the sampling risk by increasing the sampling size. Although there are many types of risks associated with ... Selecting only large or small numbers could distort the sample which creates risk. Sample (statistics) Audit "Audit Sampling ... a firm would typically require a high sample size when selecting records. In order to successfully gather a sample, it is ... Sampling risk is one of the many types of risks an auditor may face when performing the necessary procedure of audit sampling. ...
... then his sample has a fair chance to represent the population. Larger sample size will reduce the chance of sampling error ... Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability ... Therefore, inferences based on convenience sampling should be made only about the sample itself. Power Convenience sampling is ... This type of sampling is most useful for pilot testing. Convenience sampling is not often recommended for research due to the ...
... the number of red elements in a sample of given size will vary by sample and hence is a random variable whose distribution can ... A simple random sample is an unbiased sampling technique. Simple random sampling is a basic type of sampling and can be a ... Multistage sampling Nonprobability sampling Opinion poll Quantitative marketing research Sampling design Bernoulli sampling ... Random sampling can also be accelerated by sampling from the distribution of gaps between samples and skipping over the gaps. ...
The likely size of the sampling error can generally be reduced by taking a larger sample. The cost of increasing a sample size ... Since the sample error can often be estimated beforehand as a function of the sample size, various methods of sample size ... will generally be subject to sample-to-sample variation. By comparing many samples, or splitting a larger sample up into ... The sampling error is the error caused by observing a sample instead of the whole population. The sampling error is the ...
Sample size determination Sampling (statistics) Total survey error "Non-Probability Sampling - AAPOR". www.aapor.org. Retrieved ... Sample Size in Survey Research Sample Design and Confidence Intervals Survey Sampling Methods Non-probability sampling ( ... probability samples and super samples. Probability-based samples implement a sampling plan with specified probabilities ( ... This type of sampling is common in non-probability market research surveys. Convenience Samples: The sample is composed of ...
Because each element of the population is considered separately for the sample, the sample size is not fixed but rather follows ... Bernoulli sampling is therefore a special case of Poisson sampling. In Poisson sampling each element of the population may have ... In the theory of finite population sampling, Bernoulli sampling is a sampling process where each element of the population is ... Faster Random Samples With Gap Sampling (Sampling techniques). ... After running the algorithm, a sample of size k will have been ...
Many of these studies have small sample sizes and could be considered exploratory. But some clearer findings, with replications ... But DES Sampling revealed that in a good deal of his samples he was angry, specifically at his children. Donald denied this ... After samples are collected, they can be coded. This sample, for example, could be coded as containing inner speaking (the " ... The goal of DES is not to force an accurate description for every sample. Often, samples are inconclusive. The goal is to train ...
Sample Sized. Pizzo, Mike "DJ" (2015-10-05). "Porter Robinson Reflects on "Worlds," One Year Later: The gifted young producer ...
... sampling variability). Sample size. Bigger samples are better because they provide a more accurate estimate of the population/ ... Estimate and draw a graph of a population based on a sample Compare two or more samples of data to infer whether there is a ... The use of random sampling to be sure not to introduce bias in the sampling process and thus increase the chance that the ... Tasks that involve "growing samples" are also fruitful for developing informal inferential reasoning Garfield, J.B., & Ben-Zvi ...
Sample Sized. Polonsky, Sarah (September 11, 2014). "Interview: Porter Robinson Shares His New World". Vibe. Retrieved July 2, ... Evans, Steph (September 13, 2018). "Mariah Carey samples Porter Robinson's 'Goodbye to a World' on latest single". Dancing ... Meadow, Matthew (September 14, 2018). "Mariah Carey Drops New Song Sampling Porter Robinson [MUST LISTEN]". Your EDM. Retrieved ... Mariah Carey sampled the song in her promotional single "GTFO". Robinson played the song in the Second Sky festival in 2019 and ...
Sample Sized. Stolman, Elissa (August 5, 2014). "Beat by Beat Review: Porter Robinson - Worlds". Vice. Retrieved December 22, ... Musically, the song contains elements of disco and hip-hop, as well as sampling of soul music. Vocally, the song contains a ... The pitch shifting of the samples was influenced by the works of Jay Dilla. The song's composition and arrangement was compared ... he wanted to experiment with samples of soul music, which he became a fan of ever since he listened to his favorite album, Daft ...
Sample Sized. Pizzo, Mike "DJ" (October 5, 2015). "Porter Robinson Reflects on "Worlds," One Year Later". Cuepoint. Archived ...
... is inevitable in social systems. Unknown sampling population size: There is no way to know the total size of ... In sociology and statistics research, snowball sampling (or chain sampling, chain-referral sampling, referral sampling) is a ... As sample members are not selected from a sampling frame, snowball samples are subject to numerous biases. For example, people ... It is effectively used to avoid bias in snowball sampling. Respondent-driven sampling involves both a field sampling technique ...
When the dispersion is known the required sample size ( n {\displaystyle n} ) is obtained from n = ( Z α + Z β Z A Q L − Z L Q ... The required sample size ( n {\displaystyle n} ) and the critical distance ( k {\displaystyle k} ) can be obtained as k = Z L Q ... Plans for variables may produce a similar OC curve to attribute plans with significantly less sample size. The decision ... the sample size is approximately n = ( Z α + Z β Z A Q L − Z L Q L ) 2 ( 1 + k 2 2 ) {\displaystyle n=\left({\frac {Z_{\alpha ...
Sample size: 1,316. Drowning: 29.9%, motor vehicle traffic accidents: 24.8%, suffocation: 12.2%, fire/burns: 9.8%, etc. ...
... sample sizes proportional to the amount of data available from the subgroups, rather than scaling sample sizes to subgroup ... Another easy way without having to calculate the percentage is to multiply each group size by the sample size and divide by the ... Both mean and variance can be corrected for disproportionate sampling costs using stratified sample sizes. The reasons to use ... the size of the sample in each stratum is taken in proportion to the size of the stratum. Suppose that in a company there are ...
Sample size: 1,530 (1901). It is likely that these figures have changed significantly. In recent times, the religious adherence ...
"Resizing the Sample Size". Council of Fashion Designers of America. Archived from the original on 2011-06-04. Retrieved 2019-08 ... to revise the model sample size. Given the competitive nature of the global fashion industry, particularly in relation to model ... "Coco Rocha, Size 4: 'I'm Not In Demand For The Shows Anymore'". HuffPost. 2010-04-18. Retrieved 2019-08-31. Diluna, Amy (2010- ... 02-16). "At size 4, Fashion Week model Coco Rocha, 21, is latest of many women considered fat by industry". nydailynews.com. ...
Our sample size calculator can help determine if you have a statistically significant sample size. ... Sample size calculator. Curious about how to calculate your sample size? Use our sample size calculator for effortless results ... Get familiar with sample bias, sample size, statistically significant sample sizes, and how to get more responses. Soon youll ... Sample size is the number of completed responses your survey receives. Its called a sample because it only represents part of ...
Student sample sizes and target populations in NAEP mathematics at grade 12, by state/jurisdiction: 2009 State/jurisdiction. ... NOTE: The sample size is rounded to the nearest hundred. The target population is rounded to the nearest thousand. State ... More information on sampling can be found in NAEP Technical Documentation.. The sample of students in the participating Trial ... About the Assessment: Target Population and Sample Size. The schools and students participating in NAEP assessments are ...
NIST: Selecting Sample Sizes ASTM E122-07: Standard Practice for Calculating Sample Size to Estimate, With Specified Precision ... ISBN 978-0-471-48900-9. Smith, Scott (8 April 2013). "Determining Sample Size: How to Ensure You Get the Correct Sample Size". ... With more complicated sampling techniques, such as stratified sampling, the sample can often be split up into sub-samples. ... that the total sample size is given by the sum of the sub-sample sizes). Selecting these nh optimally can be done in various ...
The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. The ... literature may lack the power to detect predictive ability and might be subject to data snooping across different window sizes ... This paper proposes new methodologies for evaluating out-of-sample forecasting performance that are robust to the choice of the ... "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," CEPR Discussion Papers 8542, C.E.P.R. Discussion Papers. * ...
This free sample size calculator determines the sample size required to meet a given set of constraints. Also, learn more about ... Sample Size Calculator. Find Out The Sample Size. This calculator computes the minimum number of necessary samples to meet the ... Sample Size Calculation. Sample size is a statistical concept that involves determining the number of observations or ... of the random samples that could be taken. The confidence interval depends on the sample size, n (the variance of the sample ...
... but the population size remains constant, and "outfill" in which the sample size and population size grow together. Statistical ... for estimating the size of a hidden set. Some methods make use of random sampling with known or estimable sampling ... Si Cheng, Daniel J. Eck, Forrest W. Crawford "Estimating the size of a hidden finite set: Large-sample behavior of estimators ... We study the properties of these methods under two asymptotic regimes, "infill" in which the number of fixed-size samples ...
How to calculate sample size for each stratum of a stratified sample. Covers optimal allocation and Neyman allocation. Sample ... where nh is the sample size for stratum h, n is total sample size, Nh is the population size for stratum h, σh is the standard ... where nh is the sample size for stratum h, n is total sample size, Nh is the population size for stratum h, and σh is the ... where nh is the sample size for stratum h, n is total sample size, Nh is the population size for stratum h, and σh is the ...
Can anyone help me in detrmining what should be a sample size for performing Average-variance control charts, while logging 100 ... Can anyone help me in detrmining what should be a sample size for performing Average-variance control charts, while logging 100 ... I am not sure if this will help, but I went into a detailed discussion of sampling below:. http://Elsmar.com/ubb/Forum10/HTML/ ...
One may think to replace the minimum effect size with the observed effect sizes in the power calculation. However, this ... Besides, they need to specify a minimum detectable effect size, which may be subjective. ... The sample sizes needed for other values of average power are listed in Table 9. These determined sample sizes are much smaller ... This is used to determine the sample size of replication study. Sample sizes are estimated on 6 diseases from Wellcome Trust ...
Watch this brief video about sample size for GEE Tests for Two Means in a Stratified Cluster-Randomized Design in PASS sample ... The sample size plot gives a visual representation of how the intra-cluster correlation and the underlying true blood-pressure ... difference affect sample size. To illustrate how the total sample size relates to the expected number of clusters, we will ... The researchers wish to compare the necessary sample sizes for a range of assumed blood pressure differences between -10 and -6 ...
window->set_size_request(400, 100);. Sample Code. 1 2 3 4 5 6 7 8 9 10 ,?php. $window = new GtkWindow();. $window-,set_size_ ... window->set_size_request(400,100). sets the window size to width=400, height=100. ... label->set_size_request(48,36). to set the size of a label. ... How to change the size of GtkWindow? * Last Updated: 20 June ... set_size_request works on most other widgets, including hbox, vbox, buttons, labels, etc. ...
... we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new ... Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range BMC Med Res ... we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and ... First, we show that the sample standard deviation estimation in Hozo et al.s method (BMC Med Res Methodol 5:13, 2005) has some ...
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Calculator to Determine Minimum Sample Size for Special Education Indicators ... Calculator to Determine Minimum Sample Size. Calculator to Determine Minimum Sample Size. Indicator 7: Preschool Outcomes ... Minimum Number Required for Sample. 7: Preschool Outcomes Margin of Error: 5%. Entry level: Enter the number of eligible ... The number displayed in the column Minimum Number Required for Sample will be the minimum number of students for whom data must ...
Laurie Browne explore the concept of sample size and how to use that understanding to better interpret survey findings. ... So, what is sample size? Sample size is one of several criteria we use to plan for and to interpret research. Through sampling ... Using an online sample size calculator we get a target sample size of 374. Not much more, right? ... This number can be high or low depending on the sample size - in some cases, a low response rate is fine as long as the sample ...
Sampling (sample size and sampling technique) and quantitative data collection. Training course in research methodology and ... 1.2 What type of sampling method was used in this study?. 1.3 Do you think the sampling method used in this study could measure ... What type of sampling method was used in this study? Mention all stages and the sampling technic in each stage. ... 4.1 What sampling methods were used in this study? Describe how the participants were recruited. ...
... James Pustejovsky jepu@to @end,ng ... I am trying to estimate the sample error variance of an effect size , (reported as response ratio) based on the confidence ... Previous message (by thread): [R-meta] sample variance estimation of an effect size (reponse ratio) using confidence limits ... Next message (by thread): [R-meta] sample variance estimation of an effect size (reponse ratio) using confidence limits ...
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  • where n h is the sample size for stratum h , n is total sample size, N h is the population size for stratum h , σ h is the standard deviation of stratum h , and c h is the direct cost to sample an individual element from stratum h . (stattrek.com)
  • For the small clinics, which cover about 15% of eligible subjects for treatment, the average number of subjects will be 7, with a clinic-size standard deviation of 3.1. (ncss.com)
  • Medium-sized clinics account for roughly 25% of patients, and have an average of 18 patients per clinic, with a clinic-size standard deviation of 5.2. (ncss.com)
  • The large clinics service the remaining 60% of eligible subjects, and average 54 eligible subjects per clinic, with a clinic-size standard deviation of 18.6. (ncss.com)
  • In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. (nih.gov)
  • Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. (nih.gov)
  • First, we show that the sample standard deviation estimation in Hozo et al. (nih.gov)
  • Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. (nih.gov)
  • For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. (nih.gov)
  • Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. (nih.gov)
  • In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. (nih.gov)
  • Note that we are calculating the power or likelihood of detection given that the maximum difference between group means = 1, with sample size for each group = 30, 3 groups, standard deviation = 1, significance level = .05, and Ha: Not Equal To (two sided test). (sigmaxl.com)
  • a calculation known as "sample size calculation. (calculator.net)
  • One may think to replace the minimum effect size with the observed effect sizes in the power calculation. (springer.com)
  • They are higher than sample sizes estimated by plugging observed effect sizes in power calculation. (springer.com)
  • The major limitation of this traditional power calculation method is that the specification of the effect size is subjective and may cause bias. (springer.com)
  • One may think to plug the observed effect sizes from the primary study in the power calculation of the replication study. (springer.com)
  • Is sample size orthogonal of what test you are using, or the test you are using an input to the sample size calculation? (stackexchange.com)
  • Because there are two tests, does this impact the sample size calculation? (stackexchange.com)
  • Is a hypothetical scenario to demonstrate have understood the basics of sample size calculation. (stackexchange.com)
  • As there is no single correct or universally-accepted calculation or method for determining sample size for SEM, researchers and students alike often rely on "rules of thumb. (statisticssolutions.com)
  • So, what if your reviewers require some kind of hard calculation (rather than rules of thumb) to determine your sample size? (statisticssolutions.com)
  • There are some easy-to-use online tools that have academic support (for an example see Daniel Soper's sample size calculation tool based on the work of Westland, 2010). (statisticssolutions.com)
  • This calculator computes the minimum number of necessary samples to meet the desired statistical constraints. (calculator.net)
  • Use our sample size calculator for effortless results and explore how SurveyMonkey can assist in targeting your desired audience. (surveymonkey.com)
  • Our sample size calculator makes it easy. (surveymonkey.com)
  • To answer the other questions, as well as the first two questions, consider using the Sample Size Calculator . (stattrek.com)
  • Stat Trek's Sample Size Calculator can help you find the right sample allocation plan for your stratified design. (stattrek.com)
  • You can find the Sample Size Calculator in Stat Trek's main menu under the Stat Tools tab. (stattrek.com)
  • The calculator will display below the minimum number of students required for the sample. (nysed.gov)
  • The calculator will determine the minimum number of surveys required for the sample when a user enters the number of eligible children. (nysed.gov)
  • Using an online sample size calculator we get a target sample size of 374. (acacamps.org)
  • and desired power is 95%, then for a pooled t test $n \approx 650$ is required in each group (version), according to an on-line calculator here for pooled 2-sample t tests on normal data. (stackexchange.com)
  • To determine Power & Sample Size for a 1 Proportion Test, you can use the Power & Sample Size Calculator or Power & Sample Size with Worksheet. (sigmaxl.com)
  • In public health, epidemiology, demography, ecology and intelligence analysis, researchers have developed a wide variety of indirect statistical approaches, under different models for sampling and observation, for estimating the size of a hidden set. (projecteuclid.org)
  • Sometimes, researchers want to find the sample allocation plan that provides the most precision, given a fixed sample size. (stattrek.com)
  • The researchers wish to compare the necessary sample sizes for a range of assumed blood pressure differences between -10 and -6. (ncss.com)
  • 4.2 Why do you think the researchers used these sampling methods in this study? (gfmer.ch)
  • Each chapter illustrates statistical methods that allow researchers and analysts to apply the optimal statistical model for their research question when the sample is too small. (r-bloggers.com)
  • Our sample reports are created by a team of proficient researchers located globally. (thebusinessresearchcompany.com)
  • For simple analyses like t -tests, ANOVAs, or regressions, reputable power analysis tools such as G*Power allow researchers to calculate an appropriate sample size using only a few basic parameters (i.e., power level, significance level, and effect size ). (statisticssolutions.com)
  • Most researchers agree that SEM requires "large" sample sizes, but what exactly does this mean? (statisticssolutions.com)
  • I group these not because they are the same thing, but because they are factors that statisticians consider when calculating sample size by you probably will not. (acacamps.org)
  • But for calculating the sample size you need effect size as an input? (stackexchange.com)
  • Calculating the sample size of the Wilcoxon-Mann-Whitney via an incrementation of the pooled t-test, and then verify robustness using simulation as per your answer is a good idea and helps with understanding. (stackexchange.com)
  • Although available formulas for calculating sample size for cluster randomized trials can be derived by assuming an exchangeable correlation structure within clusters, we show that deviations from this assumption do not generally affect the validity of such formulas. (bvsalud.org)
  • For example, if we wish to know the proportion of a certain species of fish that is infected with a pathogen, we would generally have a more precise estimate of this proportion if we sampled and examined 200 rather than 100 fish. (wikipedia.org)
  • The estimator of a proportion is p ^ = X / n {\displaystyle {\hat {p}}=X/n} , where X is the number of 'positive' e.g., the number of people out of the n sampled people who are at least 65 years old). (wikipedia.org)
  • For example, if we are interested in estimating the proportion of the US population who supports a particular presidential candidate, and we want the width of 95% confidence interval to be at most 2 percentage points (0.02), then we would need a sample size of (1.96)2/ (0.022) = 9604. (wikipedia.org)
  • Thus, to estimate p in the population, a sample of n individuals could be taken from the population, and the sample proportion, pÌ‚ , calculated for sampled individuals who have brown hair. (calculator.net)
  • The uncertainty in a given random sample (namely that is expected that the proportion estimate, pÌ‚ , is a good, but not perfect, approximation for the true proportion p ) can be summarized by saying that the estimate pÌ‚ is normally distributed with mean p and variance p(1-p)/n . (calculator.net)
  • Use the given data to find the minimum sample size required to estimate a population proportion or percentage. (quizlet.com)
  • Assume that we want 95% confidence that the proportion from the sample is within four percentage points of the true population percentage. (quizlet.com)
  • however, the distribution was non-random, with a statistically significant proportion of studies, presenting sample sizes that were multiples of ten. (qualitative-research.net)
  • How Do I Perform Power and Sample Size Calculations for a One Proportion Test? (sigmaxl.com)
  • Sample sizes may be evaluated by the quality of the resulting estimates. (wikipedia.org)
  • using a target variance for an estimate to be derived from the sample eventually obtained, i.e., if a high precision is required (narrow confidence interval) this translates to a low target variance of the estimator. (wikipedia.org)
  • In practice, since p is unknown, the maximum variance is often used for sample size assessments. (wikipedia.org)
  • Can anyone help me in detrmining what should be a sample size for performing Average-variance control charts, while logging 100% precent of the population by automatic test during production? (elsmar.com)
  • Any clues about variance & effect size? (stackexchange.com)
  • This paper proposes new methodologies for evaluating out-of-sample forecasting performance that are robust to the choice of the estimation window size. (repec.org)
  • Inspired by this, we propose a new estimation method by incorporating the sample size. (nih.gov)
  • This study investigates the effects of sample size and test length on item-parameter estimation in test development utilizing three unidimensional dichotomous models of item response theory (IRT). (ed.gov)
  • These data sets were then used to create various research conditions in which test length, sample size, and IRT model variables were manipulated to investigate item parameter estimation accuracy under different conditions. (ed.gov)
  • Item Response Theory (IRT) has been considered an important development for the modern psychometrics because of its several advantages compared to Classic Test Theory (CTT), such as: the virtual invariance of item parameters in respect to the sample used in their estimation, more reliable and interpretable identification of person`s ability and more efficient procedures for test equating. (bvsalud.org)
  • The probability that your sample accurately reflects the attitudes of your population. (surveymonkey.com)
  • It's called a sample because it only represents part of the group of people (or target population ) whose opinions or behavior you care about. (surveymonkey.com)
  • For example, one way of sampling is to use a "random sample," where respondents are chosen entirely by chance from the population at large. (surveymonkey.com)
  • If you were taking a random sample of people across the U.S., then your population size would be about 317 million. (surveymonkey.com)
  • Similarly, if you are surveying your company, the size of the population is the total number of employees. (surveymonkey.com)
  • If you want a smaller margin of error, you must have a larger sample size given the same population. (surveymonkey.com)
  • Survey sampling can still give you valuable answers without having a sample size that represents the general population. (surveymonkey.com)
  • On the other hand, political pollsters have to be extremely careful about surveying the right sample size-they need to make sure it's balanced to reflect the overall population. (surveymonkey.com)
  • The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. (wikipedia.org)
  • In a census, data is sought for an entire population, hence the intended sample size is equal to the population. (wikipedia.org)
  • This vignette provides an overview of the functions that can be used to estimate the sample size needed to detect a pathogen variant in a population, given a periodic sampling scheme. (ethz.ch)
  • function can be used to calculate the sample size needed to detect a particular variant in the population within a specific number of days since its introduction, OR by the time a variant reaches a specific frequency. (ethz.ch)
  • function to calculate a sample size assuming periodic sampling ( Figure 1 ) requires knowledge of the coefficient of detection ratio between two pathogen variants (or, more commonly, one variant and the rest of the pathogen population). (ethz.ch)
  • In other words, 26 samples per day are needed to detect a variant at 1% (or higher) in a population with 95% probability of detection, given a coefficient of detection ratio ( \(\frac{C_{V_1}}{C_{V_2}}\) ) of 1.368 (as calculated from parameters listed above). (ethz.ch)
  • If sequencing will occur on a weekly basis, this means we need to process \(26*7=182\) samples per week (ideally from infections spread evenly over the 7 days) to ensure we detect a variant by the time it reaches 1% frequency in the population. (ethz.ch)
  • Leave blank if unlimited population size. (calculator.net)
  • In statistics, information is often inferred about a population by studying a finite number of individuals from that population, i.e. the population is sampled, and it is assumed that characteristics of the sample are representative of the overall population. (calculator.net)
  • Unfortunately, unless the full population is sampled, the estimate pÌ‚ most likely won't equal the true value p , since pÌ‚ suffers from sampling noise, i.e. it depends on the particular individuals that were sampled. (calculator.net)
  • The confidence level is a measure of certainty regarding how accurately a sample reflects the population being studied within a chosen confidence interval. (calculator.net)
  • Taking the commonly used 95% confidence level as an example, if the same population were sampled multiple times, and interval estimates made on each occasion, in approximately 95% of the cases, the true population parameter would be contained within the interval. (calculator.net)
  • We study the properties of these methods under two asymptotic regimes, "infill" in which the number of fixed-size samples increases, but the population size remains constant, and "outfill" in which the sample size and population size grow together. (projecteuclid.org)
  • With proportionate stratification, the sample size of each stratum is proportionate to the population size of the stratum. (stattrek.com)
  • where n h is the sample size for stratum h , N h is the population size for stratum h , N is total population size, and n is total sample size. (stattrek.com)
  • The population size of the stratum is large. (stattrek.com)
  • Ideally that sample is selected such that it represents the larger population, not just a specific segment of the population (a sample of all green dots, for example, per the image below). (acacamps.org)
  • Ideal sample size based on a population of 2,844, a confidence level of 95 percent, and a +/- 5 percent margin of error would be 339. (acacamps.org)
  • 1.1 Who were (a) the target population, (b) the study population and (c) the sample unit? (gfmer.ch)
  • begingroup$ Suppose you're testing at 5% level and can estimate population variability fairly accurately, then (1) sample sizes, (2) desired effect size (difference in locations) to detect, and (3) power of the test (probability of detecting effect if true) need to be balanced. (stackexchange.com)
  • 3) We estimated a total population size of 80 million (90% CI: 64-97 million) for the three most common species of lizards across this 66,830 km2 ecoregion. (datadryad.org)
  • To characterize the patterns of attempting to quit smoking and smoking cessation among U.S. adults during 1990 and 1991, CDC's National Health Interview Survey-Health Promotion and Disease Prevention (NHIS-HPDP) supplement collected self-reported information on cigarette smoking from a representative sample of the U.S. civilian, noninstitutionalized population aged greater than or equal to 18 years. (cdc.gov)
  • Market related factors such as increasing preference for technologically advanced services, product innovation, and historical year-on-year growth have been taken into consideration while estimating the market size. (prnewswire.com)
  • Larger sample sizes generally lead to increased precision when estimating unknown parameters. (wikipedia.org)
  • Si Cheng, Daniel J. Eck, Forrest W. Crawford "Estimating the size of a hidden finite set: Large-sample behavior of estimators," Statistics Surveys, Statist. (projecteuclid.org)
  • Although diverse methodologies related to this distribution have been proposed, the problem of determining the optimal sample size when estimating its mean has not yet been studied. (mdpi.com)
  • Does having a statistically significant sample size matter? (surveymonkey.com)
  • Generally, the rule of thumb is that the larger the sample size, the more statistically significant it is-meaning there's less of a chance that your results happened by coincidence. (surveymonkey.com)
  • But you might be wondering whether or not a statistically significant sample size matters. (surveymonkey.com)
  • Customer feedback is one of the surveys that does so, regardless of whether or not you have a statistically significant sample size. (surveymonkey.com)
  • Here are some specific use cases to help you figure out whether a statistically significant sample size makes a difference. (surveymonkey.com)
  • To calculate the sample size needed for detection assuming periodic sampling, we must provide either the number of days after introduction a variant should be detected by ( \(t\) ) OR the desired prevalence to detect a variant by ( \(P_{V_1}\) ), but not both. (ethz.ch)
  • package can be used to determine the sample size needed to accurately monitor variant prevalence given a periodic sampling strategy. (ethz.ch)
  • 1.3 Do you think the sampling method used in this study could measure the prevalence properly? (gfmer.ch)
  • Out-of-sample forecast tests robust to the choice of window size ," Working Papers 11-31, Federal Reserve Bank of Philadelphia. (repec.org)
  • Out-of-Sample Forecast Tests Robust to the Choice of Window Size ," Journal of Business & Economic Statistics , Taylor & Francis Journals, vol. 30(3), pages 432-453, April. (repec.org)
  • Out-of-Sample Forecast Tests Robust to the Choice of Window Size ," CEPR Discussion Papers 8542, C.E.P.R. Discussion Papers. (repec.org)
  • Out-of-sample forecast tests robust to the choice of window size ," Economics Working Papers 1404, Department of Economics and Business, Universitat Pompeu Fabra. (repec.org)
  • Or by using one of many online sample size calculators . (acacamps.org)
  • The results suggest that rather than sample size or test length, the combination of these two variables is important and samples of 150, 250, 350, 500, and 750 examinees can be used to estimate item parameters accurately in three unidimensional dichotomous IRT models, depending on test length and model employed. (ed.gov)
  • Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. (wikipedia.org)
  • In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. (wikipedia.org)
  • Sample sizes may be chosen in several ways: using experience - small samples, though sometimes unavoidable, can result in wide confidence intervals and risk of errors in statistical hypothesis testing. (wikipedia.org)
  • using a target for the power of a statistical test to be applied once the sample is collected. (wikipedia.org)
  • This book will enable anyone working with data to test their hypotheses even when the statistical model required for answering their questions are too complex for the sample sizes they can collect. (r-bloggers.com)
  • In this course, learners identify and correct misconceptions about data visualization and statistical analysis that are common in the basic biomedical sciences and other disciplines using small sample size studies. (mayo.edu)
  • This lets you see approximately how often the effects in your model will be significant (i.e., statistical power) in a sample of any given size. (statisticssolutions.com)
  • The total number of people whose opinion or behavior your sample will represent. (surveymonkey.com)
  • Unlike many species of birds, mammals, and amphibians which can be efficiently sampled using automated sensors including cameras and sound recorders, reptiles are often much more challenging to detect, in part because of their typically cryptic behavior and generally small body sizes. (datadryad.org)
  • Some methods make use of random sampling with known or estimable sampling probabilities, and others make structural assumptions about relationships (e.g. ordering or network information) between the elements that comprise the hidden set. (projecteuclid.org)
  • In this review, we describe models and methods for learning about the size of a hidden finite set, with special attention to asymptotic properties of estimators. (projecteuclid.org)
  • In a subsequent lesson , we re-visit this problem and see how stratified sampling compares to other sampling methods. (stattrek.com)
  • 4.1 What sampling methods were used in this study? (gfmer.ch)
  • To illustrate how the total sample size relates to the expected number of clusters, we will examine the final scenario, corresponding to a blood pressure decrease of 6, and an intra-cluster correlation coefficient of 0.06. (ncss.com)
  • When the observations are independent, this estimator has a (scaled) binomial distribution (and is also the sample mean of data from a Bernoulli distribution). (wikipedia.org)
  • The authors show that the tests proposed in the literature may lack the power to detect predictive ability and might be subject to data snooping across different window sizes if used repeatedly. (repec.org)
  • We assume an 80% success rate ( \(\omega = 0.8\) ), which ensures the 21 high quality data points that can be used to detect the presence of a pathogen variant from a selection of 27 samples. (ethz.ch)
  • The number displayed in the column Minimum Number Required for Sample will be the minimum number of students for whom data must be collected and submitted to the Department. (nysed.gov)
  • Please see https://www.p12.nysed.gov/sedcar/ for information regarding how to select students with disabilities to be included in the sample of students for whom data will be provided on these indicators. (nysed.gov)
  • A sample of PhD studies using qualitative approaches, and qualitative interviews as the method of data collection was taken from theses.com and contents analysed for their sample sizes. (qualitative-research.net)
  • The sample size requirements for discrete data are much higher than those for continuous data. (sigmaxl.com)
  • The data come from France's 2021 national perinatal survey (ENP 2021), which was carried out on a representative sample. (medscape.com)
  • Network data from Botswana and larger sample sizes to estimate rates of disease progression would be useful in assessing the robustness of our model results. (bvsalud.org)
  • The nQuery April 2018 release will add a wide range of sample size tables to across a range of areas. (statcon.de)
  • I associated bridal bargains with smaller sizes. (robinsonsbridal.com)
  • Smaller sample size produces greater instability with the three-parameter model. (bvsalud.org)
  • In complicated studies there may be several different sample sizes: for example, in a stratified survey there would be different sizes for each stratum. (wikipedia.org)
  • It tells you the best sample size for each stratum. (stattrek.com)
  • The cost to sample an element from the stratum is low. (stattrek.com)
  • In the figure below one can observe how sample sizes for binomial proportions change given different confidence levels and margins of error. (wikipedia.org)
  • The aim of the study was to investigate the effect of sample size in the fluctuations of item and person parameters. (bvsalud.org)
  • Results indicated that item and person parameters can be adequately estimated from samples starting form 200 subjects. (bvsalud.org)
  • This assumes that all samples sequenced (or otherwise characterized) will be successful. (ethz.ch)
  • Note that using z-scores assumes that the sampling distribution is normally distributed, as described above in "Statistics of a Random Sample. (calculator.net)
  • Although many lizard species are more active during the day which makes them easier to detect using visual encounter surveys, they may be unavailable for sampling during certain periods of the day or year due to their sensitivity to temperature. (datadryad.org)
  • The confidence level gives just how "likely" this is - e.g., a 95% confidence level indicates that it is expected that an estimate pÌ‚ lies in the confidence interval for 95% of the random samples that could be taken. (calculator.net)
  • 2) In recognition of these sampling challenges, we demonstrate application of a recent innovation in distance sampling that adjusts for temporary emigration between repeat survey visits. (datadryad.org)
  • This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample studies. (r-bloggers.com)
  • nQuery is a great software that fills the very specialized need for power and sample size studies. (statcon.de)
  • Some factors that affect the width of a confidence interval include: size of the sample, confidence level, and variability within the sample. (calculator.net)
  • In experimental design, where a study may be divided into different treatment groups, there may be different sample sizes for each group. (wikipedia.org)
  • For an explanation of why the sample estimate is normally distributed, study the Central Limit Theorem . (calculator.net)
  • This is used to determine the sample size of replication study. (springer.com)
  • Our new method can objectively determine replication study's sample size by using information extracted from primary study. (springer.com)
  • For the associations identified in the primary study, a minimum effect size needs to be specified. (springer.com)
  • 1.2 What type of sampling method was used in this study? (gfmer.ch)
  • Purpose To discuss power and sample size considerations for cluster randomized trials of combination HIV prevention, using an HIV prevention study in Botswana as an illustration. (bvsalud.org)
  • The percentage of the sample that responds to the survey. (acacamps.org)
  • Find the sample size required to estimate the percentage of college students who take a statistics course. (quizlet.com)
  • Find the sample size needed to estimate the percentage of California residents who are left-handed. (quizlet.com)
  • It looks at the size, shape, and number of chromosomes in a sample of cells from your body. (medlineplus.gov)
  • The equation for Neyman allocation can be derived from the equation for optimal allocation by assuming that the direct cost to sample an individual element is equal across strata. (stattrek.com)
  • One of the most troublesome issues students face is determining an appropriate sample size for structural equation modeling. (statisticssolutions.com)
  • Lower bounds on sample size in structural equation modeling. (statisticssolutions.com)
  • Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. (statisticssolutions.com)
  • Repeat CT scanning at 6-12 months is recommended, and for lesions that do not increase in size, further testing is generally not warranted. (medscape.com)
  • Ordering 10 times the minimum number ensures that districts with large populations of students with disabilities with an IEP are over-sampling to achieve an acceptable return rate. (nysed.gov)
  • The higher the sampling confidence level you want to have, the larger your sample size will need to be. (surveymonkey.com)
  • However, sampling statistics can be used to calculate what are called confidence intervals, which are an indication of how close the estimate pÌ‚ is to the true value p . (calculator.net)
  • As defined below, confidence level, confidence intervals, and sample sizes are all calculated with respect to this sampling distribution. (calculator.net)
  • We understand that purchasing color items online via a screen can be difficult so our affordably priced samples are a way to reduce full size product returns and increase customer confidence. (honeybeegardens.com)
  • Growth rates for each segment within the global sample preparation market have been determined after a thorough analysis of past trends, demographics, future trends, technological developments, and regulatory requirements. (prnewswire.com)
  • The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. (repec.org)
  • The Out-of-Sample Failure of Empirical Exchange Rate Models: Sampling Error or Misspecification? (repec.org)
  • Looking at these photos you are probably struck by two things: the gowns are beautiful, and the models look like average sized women. (robinsonsbridal.com)
  • Experience has shown that the clinics can be separated according to size - as small, medium, and large. (ncss.com)
  • If we're still talking real talk, then it's important to know that bridal runs small, and plus size is a label that applies to a lot of brides. (robinsonsbridal.com)
  • Challenges With Small Sample Sizes. (mayo.edu)
  • Cutaneous vasculitis refers to vasculitis affecting small- or medium-sized vessels in the skin and subcutaneous tissue but not the internal organs. (msdmanuals.com)
  • Vasculitis can affect the small- or medium-sized vessels of the skin. (msdmanuals.com)
  • Unsure if Sample Size Matters is for you? (mayo.edu)
  • The Sample Size Matters course is approximately 10 hours of instruction. (mayo.edu)
  • Sample Size Matters is available to all Mayo Clinic employees and the general public. (mayo.edu)
  • The online Sample Size Matters self-paced course can be completed in approximately 10 hours. (mayo.edu)
  • Besides, they need to specify a minimum detectable effect size, which may be subjective. (springer.com)
  • Then, the underlying alternative distribution of test statistics is assumed to have specified effect size. (springer.com)
  • Implications of Power and Effect Size. (mayo.edu)
  • Power and Effect Size. (mayo.edu)
  • Simulator Activity: Effect Size. (mayo.edu)
  • nQuery Advisor + nTerim has easy-to-use, customizable plotting functionality allowing you to quickly produce compelling plots that compare, power, sample size, effect size & more. (statcon.de)
  • For a pooled 2-sample t test, there is a formula implemented in various computer programs. (stackexchange.com)
  • Alternatively, sample size may be assessed based on the power of a hypothesis test. (wikipedia.org)
  • In the latest Research 360 blog post, ACA's Director of Research Dr. Laurie Browne explore the concept of sample size and how to use that understanding to better interpret survey findings. (acacamps.org)
  • Sample sizes are estimated on 6 diseases from Wellcome Trust Case Control Consortium (WTCCC) using our method. (springer.com)
  • 2013). In short, the Monte Carlo simulation method allows you to construct a model to your exact specifications and then test the model on thousands of "random" datasets of varying sample sizes. (statisticssolutions.com)
  • The main advantage of this method is that it allows you to determine an appropriate sample size for the specific model you are testing. (statisticssolutions.com)
  • The resulting total sample size is 2,151 subjects. (ncss.com)
  • Spend $5 or less total on individual color cosmetic samples and your domestic shipping charge will only be $3.49! (honeybeegardens.com)
  • Does not apply to remover samples or sample packs unless the total value of your order is less than $5.00). (honeybeegardens.com)
  • A total of 53,541 samples were collected during these 141 months of consecutive sampling. (cdc.gov)
  • NCSS PASS gives you the right sample size for your clinical trial! (statcon.de)
  • In some situations, the increase in precision for larger sample sizes is minimal, or even non-existent. (wikipedia.org)
  • The precision and cost of a stratified design are influenced by the way that sample elements are allocated to strata. (stattrek.com)
  • Another approach is disproportionate stratification , which can be a better choice (e.g., less cost, more precision) if sample elements are assigned correctly to strata. (stattrek.com)
  • The ideal sample allocation plan would provide the most precision for the least cost. (stattrek.com)
  • For patients who do not have an adrenalectomy, follow-up is designed to detect interval changes in tumor size or the development of hormonal overproduction. (medscape.com)