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Updated: Dec 13, 2025
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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The use of restricted mean time lost under competing risks data.
Jingjing Lyu1, Yawen Hou2, Zheng Chen3
1Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, No. 1023, South Shatai Road, Baiyun District, Guangzhou, 510515, China.
BMC Medical Research Methodology
|July 27, 2020
Summary
This study introduces restricted mean time lost (RMTL) as a clinically interpretable alternative to the sub-distribution hazard ratio for competing risks data. The proposed supremum difference test (sDiff) demonstrates good performance for analysis and sample size calculation.
Area of Science:
- Biostatistics
- Survival Analysis
- Clinical Trials
Background:
- Sub-distribution hazard ratio (SHR) for competing risks is difficult to interpret clinically and relies on proportional sub-distribution hazard (SDH) assumptions.
- Restricted mean time lost (RMTL) is proposed as a more interpretable statistical measure.
Purpose of the Study:
- Introduce RMTL and its estimation methods.
- Develop statistical tests (Diff and sDiff) based on RMTL differences.
- Propose sample size estimation methods for these tests.
Main Methods:
- Definition and estimation of RMTL.
- Construction of difference tests (Diff and sDiff).
- Monte Carlo simulations to evaluate statistical properties and sample size calculations.
Main Results:
- The supremum difference test (sDiff) shows good performance and high test efficiency.
- sDiff demonstrates robust performance for sample size calculations across various scenarios.
- Methods are illustrated with two real-world examples.
Conclusions:
- RMTL provides meaningful summaries of treatment effects for clinical decision-making.
- The sDiff test and proposed sample size formulas are broadly applicable in competing risks data analysis and trial design.
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