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Comparison of methods for repeated measures binary data with missing values Open Access


Other title
Missing values
Repeated measures
Type of item
Degree grantor
University of Alberta
Author or creator
Mohammadi, Farhood
Supervisor and department
Keumhee Carriere Chough (Mathematical and Statistical Sciences)
Examining committee member and department
Keumhee Carriere Chough (Mathematical and Statistical Sciences)
Narasimha Prasad (Mathematical and Statistical Sciences)
Linglong Kong (Mathematical and Statistical Sciences)
Ivor Cribben (Alberta School of Business)
Department of Mathematical and Statistical Sciences
Date accepted
Graduation date
Master of Science
Degree level
Missing data is common in health and medical experiments including con- trolled clinical trials. Using only the complete cases for analysis may cause biased inferences or even controversial results. Assuming that the data are missing at random (MAR), various methods have been developed to handle missing data. Among them, GEE, non-linear mixed effects and multiple im- putation (MI-GEE and MI-NLME) methods based on GEEs and NLMEs are considered the most efficient methods. However, these guidelines are too lim- ited to apply generally. We evaluated their performance on various missing data mechanisms with repeated measurement binary data using a simulation study. We considered two different levels of correlation (ρ=0.3, 0.7) with three cases of repeated measures (T=2, 4, 6) with sample sizes of 40, 80, 200 under different missing data mechanisms, MCAR (Missing Completely at Random), MAR (Missing At Random) and MNAR (Missing Not At Random). Based on obtained empirical size control, power level and bias of each method, we conclude that the NLME based multiple imputation (MI-NLME) method performs the best.
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File title: Comparison of methods for repeated measures binary data with missing values
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