Quantifying treatment effects in trials with multiple event-time outcomes

Claggett, B. L., McCaw, Z. R., Tian, L., McMurray, J. J.V. , Jhund, P. S. , Uno, H., Pfeffer, M. A., Solomon, S. D. and Wei, L.-J. (2022) Quantifying treatment effects in trials with multiple event-time outcomes. NEJM Evidence, 1(10), (doi: 10.1056/EVIDoa2200047)

Abstract

Background: Data on the occurrence times of multiple outcomes, reflecting the temporal profile of disease burden/progression, have been used to estimate treatment effects in various recent randomized trials. Most procedures for analyzing these data require specific model assumptions. When the assumptions are not met, the results may be misleading. Robust, model-free procedures for study design and analysis that enable clinically meaningful interpretations are warranted. Methods: For each treatment group, we constructed and summarized the estimated mean cumulative count of events over time by the area under the curve (AUC), which can be interpreted as the mean total event-free time lost from multiple undesirable outcomes. A higher curve, and resulting larger AUC, implies a worse treatment. The treatment effect is quantified by the ratio and/or difference of AUCs. The timing and occurrence of recurrent heart failure hospitalizations (HFHs) and cardiovascular (CV) death from Prospective Comparison of ARNI with ARB Global Outcomes in HF with Preserved Ejection Fraction (PARAGON-HF), comparing sacubitril/valsartan with valsartan, are presented for illustration. We also discuss the design of future studies on the basis of the proposed method. Results: With 48 months of follow-up, estimated AUCs, representing the total event-free time lost to HFHs and CV death, were 11.3 and 13.1 event-months for sacubitril/valsartan and valsartan, respectively. The ratio of these AUCs was 0.86 (95% confidence interval, 0.75 to 1.00; P=0.049), a 14% reduction of disease burden favoring combination therapy. A future study, similar to PARAGON-HF, designed using the new proposal would require fewer patients would than a conventional time-to-first-event analysis. Conclusions: The proposed method is robust and model-free and provides a clinically interpretable, time-scale summary of the treatment effect. (Funded by National Institutes of Health.)

• Item Type: Articles
• Additional Information: Supported by National Institutes of Health Grant R01HL089778 (to Dr. Tian).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jhund, Professor Pardeep and McMurray, Professor John
• Authors: Claggett, B. L., McCaw, Z. R., Tian, L., McMurray, J. J.V., Jhund, P. S., Uno, H., Pfeffer, M. A., Solomon, S. D., and Wei, L.-J.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
• Journal Name: NEJM Evidence
• Publisher: Massachusetts Medical Society
• ISSN: 2766-5526
• ISSN (Online): 2766-5526
• Published Online: 30 June 2022
• Copyright Holders: Copyright © 2022 Massachusetts Medical Society
• First Published: First published in NEJM Evidence 1(10)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy