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Updated: Feb 3, 2026

An R-Based Landscape Validation of a Competing Risk Model
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Missingness in the Setting of Competing Risks: from missing values to missing potential outcomes.

Bryan Lau1,2, Catherine Lesko1

  • 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Current Epidemiology Reports
|November 3, 2018
PubMed
Summary

Competing risks are common in epidemiology. This review covers strategies for handling missing data in competing risk analyses, crucial for accurate research findings.

Keywords:
Competing riskscausalitycause-specific hazardimputationmissing datasubdistribution hazard

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Area of Science:

  • Epidemiological research
  • Biostatistics
  • Causal inference

Background:

  • Competing risks, where one event prevents another of interest, are frequent in epidemiological studies.
  • Studies involving follow-up are susceptible to competing risks, especially if participants die during the study period.
  • Existing literature addresses competing risk methods but often overlooks missing data challenges.

Purpose of the Study:

  • To review causal inference in competing risk settings with missing potential outcomes.
  • To present practical strategies for managing missing or misclassified event types and covariate data.
  • To highlight the importance of addressing missingness in competing risk analyses.

Main Methods:

  • Overview of causal inference frameworks for competing risks.
  • Discussion of strategies for handling missing event type data.
  • Exploration of methods for addressing missing covariate data in competing risk models.

Main Results:

  • Strategies are provided for dealing with missing event types and covariate data in competing risk scenarios.
  • The presented methods are designed for straightforward implementation in standard statistical software.
  • Ongoing research continues to advance causal analyses and missing data techniques specific to competing risks.

Conclusions:

  • Competing events are a pervasive aspect of epidemiological research.
  • Missing data represents a significant, though often unrecognized, challenge in competing risk analyses.
  • Effective strategies are available to mitigate the impact of missingness, improving the reliability of competing risk studies.