Estimating the parameters in the Cox model when covariate variables are measured with error.

Published

Journal Article

The Cox proportional hazards model is commonly used to model survival data as a function of covariates. Because of the measuring mechanism or the nature of the environment, covariates are often measured with error and are not directly observable. A naive approach is to use the observed values of the covariates in the Cox model, which usually produces biased estimates of the true association of interest. 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, and show that this method is an appealing alternative. The methods are applied to analyze the relationship between survival and CD4 count in patients with AIDS.

Full Text

Duke Authors

Cited Authors

  • Hu, P; Tsiatis, AA; Davidian, M

Published Date

  • December 1998

Published In

Volume / Issue

  • 54 / 4

Start / End Page

  • 1407 - 1419

PubMed ID

  • 9883541

Pubmed Central ID

  • 9883541

International Standard Serial Number (ISSN)

  • 0006-341X

Language

  • eng

Conference Location

  • United States