Semiparametric Inference of Competing Risks Data with Additive Hazards and Missing Cause of Failure Under MCAR or MAR Assumptions

• Activity Number: 370
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Risk Analysis
• Author(s): Jean-Yves Dauxois*+ and Laurent Bordes and Pierre Joly
• Companies: University of Toulouse-INSA and University of Pau and University of Bordeaux II
• Keywords: additive hazards ; competing risks ; missing at random ; missing completely at random ; semiparametric inference ; survival analysis

Abstract

In this paper, we consider a semiparametric model for lifetime data with competing risks and missing causes of death. We assume that an additive hazards model holds for each cause-specific hazard rate function and that a random right censoring occurs. Our goal is to estimate the regression parameters as well as the functional parameters such as the baseline and cause-specific cumulative hazard rate functions / cumulative incidence functions.

We first introduce preliminary estimators of the unknown (Euclidean and functional) parameters when cause of death indicators are missing completely at random (MCAR). These estimators are obtained using the observations with known cause of failure. The advantage of considering the MCAR model is that the information given by the observed lifetimes with unknown failure cause can be used to improve the preliminary estimates in order to attain an asymptotic optimality criterion. This is the main purpose of our work. However, since it is often more realistic to consider a missing at random (MAR) mechanism, we also derive estimators of the regression and functional parameters under the MAR model. We study the large sample properties of our es