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Annual Review of Statistics and Its Application

Volume 11, 2024

Competing Risks: Concepts, Methods, and Software

Abstract

The role of competing risks in the analysis of time-to-event data is increasingly acknowledged. Software is readily available. However, confusion remains regarding the proper analysis: When and how do I need to take the presence of competing risks into account? Which quantities are relevant for my research question? How can they be estimated and what assumptions do I need to make? The main quantities in a competing risks analysis are the cause-specific cumulative incidence, the cause-specific hazard, and the subdistribution hazard. We describe their nonparametric estimation, give an overview of regression models for each of these quantities, and explain their difference in interpretation. We discuss the proper analysis in relation to the type of study question, and we suggest software in R and Stata. Our focus is on competing risks analysis in medical research, but methods can equally be applied in other fields like social science, engineering, and economics.

Keyword(s): causal versus predictive study questions, cause-specific cumulative incidence, cause-specific hazard, interventions versus biological events, subdistribution hazard
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/content/journals/10.1146/annurev-statistics-040522-094556
2024-04-22
2024-05-11
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/content/journals/10.1146/annurev-statistics-040522-094556
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Competing Risks: Concepts, Methods, and Software
Annual Review of Statistics and Its Application 11, 227 (2024); https://doi.org/10.1146/annurev-statistics-040522-094556
/content/journals/10.1146/annurev-statistics-040522-094556
/content/journals/10.1146/annurev-statistics-040522-094556
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