Hogan J W, Laird N M
Center for Statistical Sciences, Brown University, Providence, RI 02192, USA.
Stat Med. 1997;16(1-3):259-72. doi: 10.1002/(sici)1097-0258(19970215)16:3<259::aid-sim484>3.0.co;2-s.
Since Wu and Carroll (Biometrics 44, 175-188) proposed a model for longitudinal progression in the presence of informative dropout, several researchers have developed and studied models for situations where both a vector of repeated outcomes and an event time is available for each subject. These models have been developed for either longitudinal studies with dropout or for survival studies in which a random, time-varying covariate is measured repeatedly across time. When inference about the longitudinal variable is of interest, event times are treated as covariates and are often incomplete due to censoring. If survival or event time is the primary endpoint, repeated outcomes observed prior to the event are viewed as covariates; this covariate process is often incomplete, measured with error, or observed at unscheduled times during the study. We review several models which are used to handle incomplete response and covariate data in both survival and longitudinal studies.
自从吴和卡罗尔（《生物统计学》44卷，第175 - 188页）提出了一个用于处理存在信息性失访情况下纵向进展的模型后，几位研究人员针对每个受试者都有重复测量结果向量和事件时间的情形，开发并研究了相关模型。这些模型要么是为有失访情况的纵向研究而开发，要么是为生存研究而开发，在生存研究中，一个随机的、随时间变化的协变量会随时间进行重复测量。当对纵向变量进行推断时，事件时间被当作协变量处理，并且由于删失往往是不完整的。如果生存或事件时间是主要终点，那么在事件发生之前观察到的重复测量结果被视为协变量；这个协变量过程往往是不完整的，存在测量误差，或者是在研究期间的非预定时间进行观察的。我们回顾了几种用于处理生存研究和纵向研究中不完整反应及协变量数据的模型。
