Semiparametric Additive Hazards Model with Doubly Truncated Data

Abstract

Doubly truncated data arise when the survival times of interest are observed only if they fall within certain random intervals. In this paper, we consider a semiparametric additive hazards model with doubly truncated data, and propose a weighted estimating equation approach to estimate the regression coefficients, where the weights are estimated both parametrically and nonparametrically. The asymptotic properties of the resulting estimators are established. Simulation studies demonstrate that the proposed estimators perform well in a finite sample. An application to Parkinson’s disease data is provided.