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.
Highlights
In competing risks analysis, individuals are followed up until they “fail” from one of a set of possible causes of failure, e.g. cause-specific death
A common issue that arises in this context is missing values in covariates
While extensive research has been conducted into missing covariates in the context of generalized linear models (Ibrahim and others, 2005) and the Cox model for single failure type data (Herring and Ibrahim, 2001; White and Royston, 2009), little has been done on competing risks
Summary
Individuals are followed up until they “fail” from one of a set of possible causes of failure, e.g. cause-specific death In such situations, it is often of interest to model how the hazard of failure from the different causes depends on a set of covariates recorded at cohort entry. Escarela and others (2016) proposed a likelihood-based approach for handling incomplete covariates in competing risks analysis, based on models for the conditional survival distributions. They focused on the case of two partially observed discrete covariates, and developed a copula-based approach to model specification, under both missing at random (MAR) and missing not at random (MNAR) mechanisms (Rubin, 1976)
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