2009/07/15 by Bin Nan, John D. Kalbfleisch, Menggang Yu
Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Health disparities and outcomes #Statistical Methods and Inference #math.ST #msc:62D05 #msc:62E20 #msc:62N01 #stat.TH
paper · pdf · doi:10.1214/08-aos657
published as Annals of Statistics 2009, Vol. 37, No. 5A, 2351-2376 · Published in at http://dx.doi.org/10.1214/08-AOS657 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2009/07/15 · arxiv created 2009/08/21 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider a class of doubly weighted rank-based estimating methods for the transformation (or accelerated failure time) model with missing data as arise, for example, in case-cohort studies. The weights considered may not be predictable as required in a martingale stochastic process formulation. We treat the general problem as a semiparametric estimating equation problem and provide proofs of asymptotic properties for the weighted estimators, with either true weights or estimated weights, by using empirical process theory where martingale theory may fail. Simulations show that the outcome-dependent weighted method works well for finite samples in case-cohort studies and improves efficiency compared to methods based on predictable weights. Further, it is seen that the method is even more efficient when estimated weights are used, as is commonly the case in the missing data literature. The Gehan censored data Wilcoxon weights are found to be surprisingly efficient in a wide class of problems.