A Pseudo-partial-likelihood Method for Semiparametric Survival Regression

• Activity Number: 228
• Type: Contributed
• Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): David M. Zucker*+
• Companies: Hebrew University
• Keywords: Cox regression model ; proportional hazards ; errors in variables

Abstract

An estimator is presented for the regression coefficient vector in the Cox proportional hazards model with covariate error. The estimator is obtained by maximizing a likelihood-type function similar to the Cox partial likelihood. The likelihood function involves the cumulative baseline hazard function, for which a simple estimator is substituted. The method is capable of handling general covariate error structures and can be applied to studies with either an external or internal validation sample. The estimator is shown to be consistent and asymptotically normal, and an estimate of the asymptotic covariance matrix is derived. Some extensions to general transformation survival models are indicated. Two simulation studies are presented: one with a single error-prone binary covariate and one with a single error-prone, normally distributed covariate. These simulation studies show that typically produces estimates with low bias and confidence intervals with accurate coverage rates. Efficiency results relative to fully parametric maximum likelihood are also presented.