Missing covariates in competing risks analysis.

Bartlett, JW; Taylor, JM; (2016) Missing covariates in competing risks analysis. Biostatistics (Oxford, England). ISSN 1465-4644 DOI: https://doi.org/10.1093/biostatistics/kxw019

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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.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Research Centre: Centre for Statistical Methodology
PubMed ID: 27179002
Web of Science ID: 386970600011
URI: http://researchonline.lshtm.ac.uk/id/eprint/2550816


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