title: Missing covariates in competing risks analysis.
abstract: 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.
authors.affiliation: Statistical Innovation Group, AstraZeneca Cambridge, UK jwb133@googlemail.com.
authors.forename: Jonathan W
authors.initials: JW
authors.lastname: Bartlett
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
mesh_terms: D056808:Biostatistics / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009749:Nutrition Surveys / Q000706:statistics & numerical data*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*
keywords: Competing risks; Missing at random; Missing covariates; Multiple imputation