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Updated: Mar 8, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.7K
Accounting for competing risks in randomized controlled trials: a review and recommendations for improvement
Peter C Austin1,2,3, Jason P Fine4,5
1Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.
Statistics in Medicine
|January 20, 2017
Summary
Many medical studies ignore competing risks, potentially overestimating outcomes. Specialized statistical methods like cumulative incidence functions are recommended for accurate survival analysis in randomized controlled trials.
Area of Science:
- Biostatistics
- Clinical Trials
- Epidemiology
Background:
- Competing risks are events that preclude the primary outcome of interest in survival studies.
- Accurate statistical analysis is crucial for interpreting time-to-event data.
Purpose of the Study:
- To review the handling of competing risks in randomized controlled trials (RCTs) published in high-impact general medical journals.
- To assess the appropriateness of statistical methods used for survival outcomes in the presence of competing risks.
Main Methods:
- Systematic review of RCTs with survival outcomes.
- Analysis of statistical methods employed, focusing on the acknowledgment and analysis of competing risks.
- Comparison of Kaplan-Meier analysis versus cumulative incidence functions.
Main Results:
- 31 out of 40 (77.5%) reviewed RCTs were potentially susceptible to competing risks.
- The majority of these studies did not account for competing risks in their statistical analyses.
- Kaplan-Meier analysis was used in 77.4% of susceptible studies, potentially overestimating event incidence, while only 16.1% used cumulative incidence functions for unbiased estimation.
Conclusions:
- There is a significant gap in the appropriate statistical analysis of competing risks in high-impact medical RCTs.
- Recommendations are provided for the analysis and reporting of survival outcomes in RCTs to address competing risks accurately.
- Adoption of methods like cumulative incidence functions is crucial for unbiased estimation of event rates over time.
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