TL;DR
This paper introduces the restricted mean time lost (RMTL) as an interpretable alternative to hazard ratios for competing risks data, providing new estimators, tests, and sample size formulas with validated simulation and real data results.
Contribution
It proposes the RMTLd estimator and hypothesis test, along with a sample size formula, enhancing analysis of competing risks data especially when proportional hazards assumptions are violated.
Findings
RMTLd estimates are accurate in simulations
RMTLd test maintains robust type I error and power
Application examples confirm practical utility
Abstract
In clinical and epidemiological studies, hazard ratios are often applied to compare treatment effects between two groups for survival data. For competing risks data, the corresponding quantities of interest are cause-specific hazard ratios (cHRs) and subdistribution hazard ratios (sHRs). However, they both have some limitations related to model assumptions and clinical interpretation. Therefore, we recommend restricted mean time lost (RMTL) as an alternative that is easy to interpret in a competing risks framework. Based on the difference in restricted mean time lost (RMTLd), we propose a new estimator, hypothetical test and sample size formula. The simulation results show that the estimation of the RMTLd is accurate and that the RMTLd test has robust statistical performance (both type I error and power). The results of three example analyses also verify the performance of the RMTLd…
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