Cox regression model with randomly censored covariates.

Published

Journal Article

This paper deals with a Cox proportional hazards regression model, where some covariates of interest are randomly right-censored. While methods for censored outcomes have become ubiquitous in the literature, methods for censored covariates have thus far received little attention and, for the most part, dealt with the issue of limit-of-detection. For randomly censored covariates, an often-used method is the inefficient complete-case analysis (CCA) which consists in deleting censored observations in the data analysis. When censoring is not completely independent, the CCA leads to biased and spurious results. Methods for missing covariate data, including type I and type II covariate censoring as well as limit-of-detection do not readily apply due to the fundamentally different nature of randomly censored covariates. We develop a novel method for censored covariates using a conditional mean imputation based on either Kaplan-Meier estimates or a Cox proportional hazards model to estimate the effects of these covariates on a time-to-event outcome. We evaluate the performance of the proposed method through simulation studies and show that it provides good bias reduction and statistical efficiency. Finally, we illustrate the method using data from the Framingham Heart Study to assess the relationship between offspring and parental age of onset of cardiovascular events.

Full Text

Duke Authors

Cited Authors

  • Atem, FD; Matsouaka, RA; Zimmern, VE

Published Date

  • July 2019

Published In

Volume / Issue

  • 61 / 4

Start / End Page

  • 1020 - 1032

PubMed ID

  • 30908720

Pubmed Central ID

  • 30908720

Electronic International Standard Serial Number (EISSN)

  • 1521-4036

Digital Object Identifier (DOI)

  • 10.1002/bimj.201800275

Language

  • eng

Conference Location

  • Germany