Multiple imputation of missing covariates for the Cox proportional hazards cure model

Lauren J Beesley, Jonathan W Bartlett, Gregory T Wolf, Jeremy M G Taylor

Research output: Contribution to journalArticlepeer-review

17 Citations (SciVal)


We explore several approaches for imputing partially observed covariates when the outcome of interest is a censored event time and when there is an underlying subset of the population that will never experience the event of interest. We call these subjects 'cured', and we consider the case where the data are modeled using a Cox proportional hazards (CPH) mixture cure model. We study covariate imputation approaches using fully conditional specification. We derive the exact conditional distribution and suggest a sampling scheme for imputing partially observed covariates in the CPH cure model setting. We also propose several approximations to the exact distribution that are simpler and more convenient to use for imputation. A simulation study demonstrates that the proposed imputation approaches outperform existing imputation approaches for survival data without a cure fraction in terms of bias in estimating CPH cure model parameters. We apply our multiple imputation techniques to a study of patients with head and neck cancer.

Original languageEnglish
Pages (from-to)4701-4717
Number of pages17
JournalStatistics in medicine
Issue number26
Early online date21 Jul 2016
Publication statusPublished - 20 Nov 2016


  • Bias
  • Data Interpretation, Statistical
  • Head and Neck Neoplasms
  • Humans
  • Proportional Hazards Models
  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural


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