Semiparametric regression estimation in the presence of dependent censoring
Semiparametric regression estimation in the presence of dependent censoring
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DOI：
10.2307/2337346
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发表时间：
1995-12-01
期刊：
影响因子：
2.7
通讯作者：
Robins, JM
中科院分区：
文献类型：
--
作者：
Rotnitzky, A;Robins, JM
我们提出一种半参数估计方法，用于在给定基线解释变量\(X\)（在随访开始前测量）的情况下，估计在固定随访期结束时测量的结果\(Y\)关于\(X\)的回归，同时存在与\(X\)相关的相依删失情况。当数据是“随机缺失”但不是“完全随机缺失”（鲁宾，1976）时，所提出的估计量是一致的，并且不需要完全指定完整数据的似然函数。具体而言，我们假设在时间\(t\)删失的概率在给定与\(Y\)相关的时变协变量向量直到\(t\)的记录历史的条件下与结果\(Y\)无关。我们的估计量可用于在研究治疗对感兴趣的响应变量均值影响的随机试验中，对相依删失和非随机不依从进行调整。即使存在独立删失，我们的方法也允许研究者通过利用结果与时变协变量向量的相关性来提高效率。
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.