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Abstract: We consider a class of doubly weighted rank-based estimating methods for thetransformation or accelerated failure time model with missing data as arise,for example, in case-cohort studies. The weights considered may not bepredictable as required in a martingale stochastic process formulation. Wetreat the general problem as a semiparametric estimating equation problem andprovide proofs of asymptotic properties for the weighted estimators, witheither true weights or estimated weights, by using empirical process theorywhere martingale theory may fail. Simulations show that the outcome-dependentweighted method works well for finite samples in case-cohort studies andimproves efficiency compared to methods based on predictable weights. Further,it is seen that the method is even more efficient when estimated weights areused, as is commonly the case in the missing data literature. The Gehancensored data Wilcoxon weights are found to be surprisingly efficient in a wideclass of problems.



Author: Bin Nan, John D. Kalbfleisch, Menggang Yu

Source: https://arxiv.org/







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