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Curr Epidemiol Rep. 2018 Jun;5(2):153-159. doi: 10.1007/s40471-018-0142-3. Epub 2018 Mar 19.

Missingness in the Setting of Competing Risks: from missing values to missing potential outcomes.

Author information

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD.


Purpose of review:

The setting of competing risks in which there is an event that precludes the event of interest from occurring is prevalent in epidemiological research. Unless studying all-cause mortality, any study following up individuals is subject to having a competing risk should individuals die during time period that the study covers. While there are prior papers discussing the need for competing risk methods in epidemiologic research, we are not aware of any review that discusses issues of missing data in a competing risk setting.

Recent Findings:

We provide an overview of causal inference in competing risks as potential outcomes are missing, provide some strategies in dealing with missing (or misclassified) event type, and missing covariate data in competing risks. The strategies presented are specifically focused on those that may easily be implemented in standard statistical packages. There is ongoing work in terms of causal analyses, dealing with missing event type information, and missing covariate values specific to competing risk analyses.


Competing events are common in epidemiologic research. While there has been a focus on why one should conduct a proper competing risk analysis, a perhaps unrecognized issue is in terms of missingness. Strategies exist to minimize the impact of missingness in analyses of competing risks.


Competing risks; causality; cause-specific hazard; imputation; missing data; subdistribution hazard

[Available on 2019-06-01]

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