Likelihood-based methods for missing covariates in the cox proportional hazards model

Published

Journal Article

Problems associated with missing covariate data are well known but often ignored. We present a method for estimating the parameters in the Cox proportional hazards model when the missing data are missing at random (MAR) and censoring is noninformative. Due to the computational burden of this method, we introduce an approximation that allows us to use a weighted expectation-maximization (EM) algorithm to estimate the parameters more easily. When the missing covariates are continuous rather than categorical, we implement a Monte Carlo version of the EM algorithm along with the Gibbs sampler to obtain parameter estimates. We also give the asymptotic distribution of these estimates. The primary advantage of this method over complete case analysis is that it produces more efficient parameter estimates and corrects for bias in the MAR setting. To motivate the methodology, we present an analysis of a phase III melanoma clinical trial conducted by the Eastern Cooperative Oncology Group. © 2001 American Statistical Association.

Full Text

Duke Authors

Cited Authors

  • Herring, AH; Ibrahim, JG

Published Date

  • March 1, 2001

Published In

Volume / Issue

  • 96 / 453

Start / End Page

  • 292 - 302

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1198/016214501750332866

Citation Source

  • Scopus