Title
Estimating the Parameters in the Cox Model when Covariate Variables are Measured with Error
Speaker
Dr. Ping Hu, Biometry Research Group, National Cancer Institute
Abstract
The Cox proportional hazards model is commonly used to evaluate the effects of treatment and prognostic factors on survival. However, prognostic factors are often measured with error. If we use the traditional partial likelihood method to estimate the parameters without taking into account the effect of measurement errors of covariates, we risk estimating parameters which will be biased towards the null. An alternative strategy is to take into account the error in measurement, which may be carried out for the Cox model in a number of ways. We examine several such approaches and compare and contrast them through several simulation studies. We introduce a likelihood-based approach, which we refer to as the semiparametric method. In this method, we do not make parametric assumptions on the unobservable true covariates X, while we assume that the densities of X belong to a class of smooth and less restrictive densities. We show that this method is an appealing alternative to other methods. The methods are applied to analyze the relationship between survival and CD4 count in patients with AIDS.
