Analysis of Informatively Interval-Censored Case–Cohort Studies with the Application to HIV Vaccine Trials

Abstract

Case–cohort studies are commonly used in various investigations, and many methods have been proposed for their analyses. However, most of the available methods are for right-censored data or assume that the censoring is independent of the underlying failure time of interest. In addition, they usually apply only to a specific model such as the Cox model that may often be restrictive or violated in practice. To relax these assumptions, we discuss regression analysis of interval-censored data, which arise more naturally in case–cohort studies than and include right-censored data as a special case, and propose a two-step inverse probability weighting estimation procedure under a general class of semiparametric transformation models. Among other features, the approach allows for informative censoring. In addition, an EM algorithm is developed for the determination of the proposed estimators and the asymptotic properties of the proposed estimators are established. Simulation results indicate that the approach works well for practical situations and it is applied to a HIV vaccine trial that motivated this investigation.

Keywords

EM algorithm / Informative censoring / Inverse probability weighting / Joint modeling / Transformation model