Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA, USA.
Stat Methods Med Res. 2022 Jan;31(1):119-138. doi: 10.1177/09622802211047177. Epub 2021 Nov 15.
Longitudinal binary data in crossover designs with missing data due to ignorable and nonignorable dropout is common. This paper 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 pseudo-likelihood estimation are advocated for valid and robust inference. 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.
交叉设计中由于可忽略和不可忽略的脱落而导致缺失的纵向二分类数据很常见。本文评估了现有的条件和边缘模型，并建立了条件和边缘参数之间的关系，主要目的是比较治疗均值效应。我们进行了广泛的模拟研究，以在完整数据下和缺失数据下的选择模型下，针对不同的参数分布和缺失模式和机制，研究这些模型。我们提倡使用广义估计方程和基于伪似然估计的广义线性混合效应模型进行有效和稳健的推断。我们还提出了一种受控多重插补方法作为对缺失数据假设的敏感性分析。最后，我们在两个具有二分类数据的真实数据示例中实现了所提出的模型和敏感性分析。
