Estimation of the additive hazards model based on case-cohort interval-censored data with dependent censoring

Abstract

The additive hazards model is one of the most commonly used models for regression analysis of failure time data, and many methods have been developed for its estimation. In this article, we consider the situation where one observes informatively interval-censored data arising from case-cohort studies where covariate information is collected only for a small subcohort of study subjects. By informative or dependent censoring, we mean that the failure time of interest and the censoring mechanism may be correlated. For estimation, we will develop a sieve inverse probability weighting estimation procedure with the use of Bernstein polynomials. The resulting estimators of regression parameters are shown to be consistent and asymptotically normal. An extensive simulation study is conducted and suggests that the proposed method works well in practical situations. An example is also provided.