Lee Keunbaik, Daniels Michael J
Department of Statistics, Sungkyunkwan University, Seoul, 110-745, Korea.
Stat Med. 2013 Oct 30;32(24):4275-84. doi: 10.1002/sim.5857. Epub 2013 May 30.
In longitudinal clinical trials, if a subject drops out due to death, certain responses, such as those measuring quality of life (QoL), will not be defined after the time of death. Thus, standard missing data analyses, e.g., under ignorable dropout, are problematic because these approaches implicitly 'impute' values of the response after death. In this paper we define a new survivor average causal effect for a bivariate response in a longitudinal quality of life study that had a high dropout rate with the dropout often due to death (or tumor progression). We show how principal stratification, with a few sensitivity parameters, can be used to draw causal inferences about the joint distribution of these two ordinal quality of life measures.
在纵向临床试验中，如果受试者因死亡而退出，某些反应，如那些测量生活质量（QoL）的反应，在死亡时间之后将无法定义。因此，标准的缺失数据分析方法，例如在可忽略的退出假设下的方法，是有问题的，因为这些方法会隐含地“插补”死亡后的反应值。在本文中，我们为一项纵向生活质量研究中的二元反应定义了一种新的幸存者平均因果效应，该研究有很高的退出率，且退出通常是由于死亡（或肿瘤进展）。我们展示了如何使用带有一些敏感性参数的主分层来对这两个有序生活质量测量指标的联合分布进行因果推断。
