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Published on: June 10, 2025
Utility of Restricted Mean Survival Time Analysis for Heart Failure Clinical Trial Evaluation and Interpretation
Carlotta Perego1, Marco Sbolli1, Claudia Specchia2
1Department of Heart Failure and Transplant, Inova Heart and Vascular Institute, Falls Church, Virginia, USA; Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy.
Objectives:
This study sought to demonstrate the statistical and utilitarian properties of restricted mean survival time (RMST) and restricted mean time lost (RMTL) for assessing treatments for heart failure (HF) with reduced ejection fraction.
Background:
Although the hazard ratio (HR) is the most commonly used measure to quantify treatment effects in HF clinical trials, HRs may be difficult to interpret and require the proportional hazards assumption to be valid. RMST and RMTL are intuitive summaries of groupwise survival that measure treatment effects without model assumptions.
Methods:
Patient time-to-event data were reconstructed from published landmark HF clinical trial Kaplan-Meier curves. We estimated RMST differences (ΔRMSTs) and RMTL ratios between treatment groups for primary and secondary outcomes, and compared test statistics and effect sizes with proportional hazards models. We fit Weibull estimations to extrapolate trial data to 5 years of treatment.
Results:
Using RMSTs and RMTLs yielded similar statistical conclusions as HR analysis for a compendium of 16 HF clinical trials including 48,581 patients. RMTL ratios approximated HRs for each trial, but ΔRMSTs provided absolute effect sizes unavailable with HRs. For instance, spironolactone added 2.2 months of life over 34 months of treatment, and dapagliflozin added 0.3 months of life over 24 months of treatment. When normalized to 5-years follow-up with Weibull estimation, spironolactone and dapagliflozin added 6.0 months and 1.8 months of life for patients, respectively.
Conclusions:
Statistically, RMST and RMTL perform similarly to proportional hazards modeling but may help patients by providing clinically relevant intuitive estimates of treatment effects without prohibitive assumptions.
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