The Cox-Aalen model for doubly censored data

Abstract

Double censored data often arise in medical and epidemiological studies when observations are subject to both left censoring and right censoring. In this article, based on doubly censored data, we consider maximum likelihood estimation for the Cox-Aalen model with fixed covariates. By treating left censored observations as missing, we propose expectation-maximization (EM) algorithms for obtaining the maximum likelihood estimators (MLE) of the regression coefficients for the Cox-Aalen model. We establish the asymptotic properties of the MLE. Simulation studies show that MLE via the EM algorithms performs well.

Keywords:

• Left censoring
• maximum likelihood estimation
• additive hazard model
• EM algorithm
• MLE