Author + information
- Carlotta Perego, MDa,b,
- Marco Sbolli, MDa,b,
- Claudia Specchia, PhDc,
- Mona Fiuzat, PharmDd,
- Zachary R. McCaw, PhDe,
- Marco Metra, MDb,
- Chiara Oriecuia, MScc,
- Giulia Peveri, MScf,
- Lee-Jen Wei, PhDg,
- Christopher M. O’Connor, MDa,d and
- Mitchell A. Psotka, MD, PhDa,∗ (, )@mpsotka
- aDepartment of Heart Failure and Transplant, Inova Heart and Vascular Institute, Falls Church, Virginia
- bDepartment of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
- cDepartment of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
- dDepartment of Medicine, Duke University, Durham, North Carolina
- eGoogle LLC, Mountain View, California
- fDepartment of Clinical Science and Community Health, University of Milan, Milan, Italy
- gDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- ↵∗Address for correspondence:
Dr. Mitchell A. Psotka, Inova Heart and Vascular Institute, Heart Failure and Transplant, 3300 Gallows Road, Falls Church, Virginia 22042.
Objectives The 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.
Dr. O’Connor has received research support from Roche Diagnostics and Merck; has served as a consultant for Merck, Bristol-Myers Squibb, Windtree, and Neurotronik; and is a co-owner of Biscardia. Dr. Psotka has received consulting fees from Amgen, Cytokinetics, and Windtree; and research funding from the U.S. Food and Drug Administration. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Barry Greenberg, MD, was Guest Editor on this paper.
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Heart Failure author instructions page.
- Received April 30, 2020.
- Revision received June 24, 2020.
- Accepted July 27, 2020.
- 2020 The Authors