Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
提出一类逆概率加权估计量，用于处理重复结局数据中响应变量缺失的情况，无需完全指定似然函数，可纠正随机临床试验中的依赖删失和非随机不依从问题。
Abstract We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data. The proposed estimators do not require full specification of the likelihood. They can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not missing completely at random. These estimators can be used to correct for dependent censoring and nonrandom noncompliance in randomized clinical trials studying the effect of a treatment on the evolution over time of the mean of a response variable. The likelihood-based parametric G-computation algorithm estimator may also be used to attempt to correct for dependent censoring and nonrandom noncompliance. But because of possible model misspecification, the parametric G-computation algorithm estimator, in contrast with the proposed weighted estimators, may be inconsistent for the difference in treatment-arm-specific means, even when compliance is completely at random and censoring is independent. We illustrate our methods with the analysis of the effect of zidovudine (AZT) treatment on the evolution of mean CD4 count with data from an AIDS clinical trial.