Biomaterial Database

A simulation study to assess statistical methods for binary repeated measures data. 2010

Elmabrok Masaoud, and Henrik Stryhn

Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada. emasaoud@upei.ca

Binary repeated measures data are commonly encountered in both experimental and observational veterinary studies. Among the wide range of statistical methods and software applicable to such data one major distinction is between marginal and random effects procedures. The objective of the study was to review and assess the performance of marginal and random effects estimation procedures for the analysis of binary repeated measures data. Two simulation studies were carried out, using relatively small, balanced, two-level (time within subjects) datasets. The first study was based on data generated from a marginal model with first order autocorrelation, the second on a random effects model with autocorrelated random effects within subjects. Three versions of the models were considered in which a dichotomous treatment was modelled additively, either between or within subjects, or modelled by a time interaction. Among the studied statistical procedures were: generalized estimating equations (GEE), Marginal Quasi Likelihood, likelihood based on numerical integration, penalized quasi-likelihood, restricted pseudo likelihood and Bayesian Markov Chain Monte Carlo. Results for data generated by the marginal model showed autoregressive GEE to be highly efficient when treatment was within subjects, even with strongly correlated responses. For treatment between subjects, random effects procedures also performed well in some situations; however, a relatively small number of subjects with a short time series proved a challenge for both marginal and random effects procedures. Results for data generated by the random effects model showed bias in estimates from random effects procedures when autocorrelation was present in the data, while the marginal procedures generally gave estimates close to the marginal parameters.

UI	MeSH Term	Description	Entries
D008390	Markov Chains	A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.	Markov Process,Markov Chain,Chain, Markov,Chains, Markov,Markov Processes,Process, Markov,Processes, Markov
D008892	Milk	The off-white liquid secreted by the mammary glands of humans and other mammals. It contains proteins, sugar, lipids, vitamins, and minerals.	Cow Milk,Cow's Milk,Milk, Cow,Milk, Cow's
D008954	Models, Biological	Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.	Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D009010	Monte Carlo Method	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)	Method, Monte Carlo
D002452	Cell Count	The number of CELLS of a specific kind, usually measured per unit volume or area of sample.	Cell Density,Cell Number,Cell Counts,Cell Densities,Cell Numbers,Count, Cell,Counts, Cell,Densities, Cell,Density, Cell,Number, Cell,Numbers, Cell
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D003627	Data Interpretation, Statistical	Application of statistical procedures to analyze specific observed or assumed facts from a particular study.	Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D001499	Bayes Theorem	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.	Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D015233	Models, Statistical	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.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model