Modeling the Drop-Out Mechanism in Repeated-Measures Studies
本文讨论了在重复测量研究中，如何同时建模数据和退出过程，以避免因数据不平衡导致的推断偏差，并介绍了随机系数选择模型和随机系数模式混合模型两类方法。
Abstract Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with unequal numbers of measures for each subject. Modern software programs for handling unbalanced longitudinal data improve on methods that discard the incomplete cases by including all the data, but also yield biased inferences under plausible models for the drop-out process. This article discusses methods that simultaneously model the data and the drop-out process within a unified model-based framework. Models are classified into two broad classes—random-coefficient selection models and random-coefficient pattern-mixture models—depending on how the joint distribution of the data and drop-out mechanism is factored. Inference is likelihood-based, via maximum likelihood or Bayesian methods. A number of examples in the literature are placed in this framework, and possible extensions outlined. Data collection on the nature of the drop-out process is advocated to guide the choice of model. In cases where the drop-out mechanism is not well understood, sensitivity analyses are suggested to assess the effect on inferences about target quantities of alternative assumptions about the drop-out process.