Semiparametric methods for survival analysis of case-control data subject to dependent censoring

Case-control sampling can be an efficient and cost-saving study design, wherein subjects are selected into the study based on the outcome of interest. It was established long ago that proportional hazards regression can be applied to case-control data. However, each of the various estimation techniques available assumes that failure times are independently censored. Since independent censoring is often violated in observational studies, we propose methods for Cox regression analysis of survival data obtained through case-control sampling, but subject to dependent censoring. The proposed methods are based on weighted estimating equations, with separate inverse weights used to account for the case-control sampling and to correct for dependent censoring. The proposed estimators are shown to be consistent and asymptotically normal, and consistent estimators of the asymptotic covariance matrices are derived. Finite-sample properties of the proposed estimators are examined through simulation studies. The methods are illustrated through an analysis of pre-transplant mortality among end-stage liver disease patients obtained from a national organ failure registry. (C) 2014 Statistical Society of Canada