Song Xiao, Davidian Marie, Tsiatis Anastasios A
Department of Statistics, Box 8203, North Carolina State University, Raleigh NC 27695-8203, USA.
Biostatistics. 2002 Dec;3(4):511-28. doi: 10.1093/biostatistics/3.4.511.
In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and several time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependent covariate follows a linear mixed effects model and the hazard of failure depends on random effects and time-independent covariates via a proportional hazards relationship. Regression calibration and likelihood or Bayesian methods have been advocated for implementation; however, generalization to more than one time-dependent covariate may become prohibitive. For a single time-dependent covariate, Tsiatis and Davidian (2001) have proposed an approach that is easily implemented and does not require an assumption on the distribution of the random effects. This technique may be generalized to multiple, possibly correlated, time-dependent covariates, as we demonstrate. We illustrate the approach via simulation and by application to data from an HIV clinical trial.
在许多纵向研究中，刻画事件发生时间（如生存时间）与多个随时间变化和不随时间变化的协变量之间的关系很有意义。随时间变化的协变量通常是间歇性观测且存在误差。对于单个随时间变化的协变量，一种常用方法是假设一个联合纵向数据-生存模型，其中随时间变化的协变量遵循线性混合效应模型，且失败风险通过比例风险关系依赖于随机效应和不随时间变化的协变量。回归校准以及似然法或贝叶斯方法已被提倡用于实施；然而，推广到多个随时间变化的协变量可能会变得难以实现。对于单个随时间变化的协变量，Tsiatis和Davidian（2001年）提出了一种易于实施且不需要对随机效应分布进行假设的方法。正如我们所展示的，该技术可以推广到多个可能相关的随时间变化的协变量。我们通过模拟以及将其应用于一项HIV临床试验的数据来说明该方法。
