Missing covariates in competing risks analysis

Jonathan W Bartlett, Jeremy M G Taylor

Research output: Contribution to journalArticlepeer-review

20 Citations (SciVal)


Studies often follow individuals until they fail from one of a number of competing failure types. One approach to analyzing such competing risks data involves modeling the cause-specific hazards as functions of baseline covariates. A common issue that arises in this context is missing values in covariates. In this setting, we first establish conditions under which complete case analysis (CCA) is valid. We then consider application of multiple imputation to handle missing covariate values, and extend the recently proposed substantive model compatible version of fully conditional specification (SMC-FCS) imputation to the competing risks setting. Through simulations and an illustrative data analysis, we compare CCA, SMC-FCS, and a recent proposal for imputing missing covariates in the competing risks setting.

Original languageEnglish
Pages (from-to)751-763
Number of pages13
Issue number4
Early online date13 May 2016
Publication statusPublished - 1 Oct 2016


  • Biostatistics
  • Data Interpretation, Statistical
  • Humans
  • Models, Statistical
  • Nutrition Surveys
  • Proportional Hazards Models
  • Risk Assessment
  • Journal Article


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