Semiparametric Regression Estimation in the Presence of Dependent Censoring
提出一种半参数估计方法，在给定基线协变量后存在相依删失时，估计固定随访期结束时结果变量对基线解释变量的回归，适用于随机试验中调整相依删失和非随机不依从性，并可在独立删失下利用时依协变量提高效率。
We propose a semiparametric estimation procedure for estimating the regression of an outcome Y, measured at the end of a fixed follow-up period, on baseline explanatory variables X, measured prior to start of follow-up, in the presence of dependent censoring given X. The proposed estimators are consistent when the data are ‘missing at random’ but not ‘missing completely at random’ (Rubin, 1976), and do not require full specification of the complete data likelihood. Specifically, we assume that the probability of censoring at time t is independent of the outcome Y conditional on the recorded history up to t of a vector of time-dependent covariates that are correlated with Y. Our estimators can be used to adjust for dependent censoring and nonrandom noncompliance in randomised trials studying the effect of a treatment on the mean of a response variable of interest. Even with independent censoring, our methods allow the investigator to increase efficiency by exploiting the correlation of the outcome with a vector of time-dependent covariates.