A simulation study to assess statistical methods for binary repeated measures data |
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| Authors: | Elmabrok Masaoud Henrik Stryhn |
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| Institution: | 1. Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PEI C1A 4P3, Canada;2. University of Seventh April, Faculty of Science, Zawia, Libya;1. Global Patient Outcomes and Real World Evidence of Eli Lilly and Co, Indianapolis, Indiana;2. Eli Lilly and Co, Windlesham Surrey, United Kingdom;3. Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana;4. HealthMetrics Outcomes Research LLC, Bonita Springs, FL;1. FOCAS Research Institute, Dublin, Ireland;2. Health & Biomedicine, LEITAT Technological Center, Barcelona, Spain;3. Unidad Analítica, Health Research Institute Hospital La Fe, Valencia, Spain;4. Neonatal Research Unit, Health Research Institute Hospital La Fe, Valencia, Spain;1. Department of Public Health Sciences, Henry Ford Health System, Detroit, Mich;2. Department of Internal Medicine, Henry Ford Health System, Detroit, Mich;3. Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Mich;4. Department of Pediatrics, The Ann and Robert H. Lurie Children''s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Ill;5. Department of Bioengineering & Therapeutic Sciences, University of California San Francisco, San Francisco, Calif;6. Department of Medicine, University of California San Francisco, San Francisco, Calif |
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| Abstract: | 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. |
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