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Updated: May 7, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Number needed to treat for time-to-event data with competing risks.
Natalia A Gouskova1, Suprateek Kundu, Peter B Imrey
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S.A.
Statistics in Medicine
|September 17, 2013
Summary
The number needed to treat (NNT) is a valuable tool for understanding treatment effects. This study defines NNT for time-to-event data with competing risks, improving risk communication for clinicians and patients.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Survival Analysis
Background:
- The number needed to treat (NNT) is a widely used metric for quantifying treatment efficacy.
- Existing NNT definitions are suitable for binary outcomes and time-to-event data without competing risks.
- A clear definition for NNT in the context of competing risks is currently lacking.
Purpose of the Study:
- To introduce a novel definition for the number needed to treat (NNT) specifically for time-to-event data with competing risks.
- To develop and propose statistical methods for estimating and inferring this new NNT definition.
Main Methods:
- Definition of NNT based on the cumulative incidence function for competing risks.
- Development of non-parametric and semi-parametric inferential methods.
- Application to right-censored time-to-event data.
Main Results:
- A formal definition of NNT for time-to-event data with competing risks is established.
- Valid statistical procedures for estimating and testing NNT are presented.
- The methods are demonstrated using a real-world breast cancer clinical trial dataset.
Conclusions:
- The proposed definition and methods extend the utility of NNT to complex survival data scenarios with competing risks.
- This advancement facilitates more accurate and understandable risk communication in clinical practice.
- The findings are particularly relevant for studies involving diseases with multiple potential causes of event occurrence.
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