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Activity Number:
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135
- Multiplicity, Missing Data and Other Topics
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Type:
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Contributed
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Date/Time:
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Monday, August 9, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #318375
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Title:
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Analysis of Crossover Designs for Longitudinal Binary Data with Ignorable and Nonignorable Dropout
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Author(s):
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Xi Wang* and Vernon M. Chinchilli
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Companies:
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Pennsylvania State University College of Medicine and Pennsylvania State University College of Medicine
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Keywords:
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crossover trials;
ignorable missing;
nonignorable missing;
controlled multiple imputation;
longitudinal binary data
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Abstract:
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Longitudinal binary data in crossover designs with missing data due to ignorable and nonignorable dropout is common. This work evaluates available conditional and marginal models and establishes the relationship between the conditional and marginal parameters with the primary objective of comparing the treatment mean effects. We perform extensive simulation studies to investigate these models under complete data and the selection models under missing data with different parametric distributions and missingness patterns and mechanisms. The generalized estimating equations and the generalized linear mixed-effects models with pseudolikelihood estimation are advocated for valid and robust inference. According to the ICH E9 (R1) guideline, we also propose a controlled multiple imputation method as a sensitivity analysis of the missing data assumption. Lastly, we implement the proposed models and the sensitivity analysis in two real data examples with binary data.
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Authors who are presenting talks have a * after their name.
