Linear Regression Analysis for Highly Stratified Failure Time Data
针对由许多小且独立的组构成的删失失效时间数据，提出一种群体平均模型方法，在不指定组内联合分布的情况下估计协变量对失效时间的影响，避免复杂的非参数估计。
In this article we consider cases where data consist of many small and independent groups of correlated failure time observations. For each failure time, which may be censored, some important covariates are also recorded. Our goal is to examine the covariate effects on the individual failure time observations. We assume that the logarithm of each failure time is linearly related to its covariates. We then take a population-averaged model approach to obtain inference procedures for the regression parameters without specifying the joint distribution of the observations within the group. The new proposals do not need complicated and unstable nonparametric estimates for the hazard function of the error term. Their properties are extensively examined for practical sample sizes. Comparisons among various procedures are also performed. All the methods studied in this article are illustrated with examples.