An approach to explore for a sweet spot in randomized trials.


Journal

Journal of clinical epidemiology
ISSN: 1878-5921
Titre abrégé: J Clin Epidemiol
Pays: United States
ID NLM: 8801383

Informations de publication

Date de publication:
04 2020
Historique:
received: 16 05 2019
revised: 16 11 2019
accepted: 12 12 2019
pubmed: 25 12 2019
medline: 15 12 2020
entrez: 25 12 2019
Statut: ppublish

Résumé

The objective of the study was to demonstrate how a conventional randomized trial can be analyzed through a stratified or a matched approach to identify a potential sweet spot where observed differences might be accentuated in the mid range of disease severity. We review a landmark randomized trial of heart failure patients that tested whether implantable defibrillators reduce mortality (n = 2,521). Overall, 22% (182/829) of the patients in the defibrillator group died compared with 29% (484/1,692) of patients in the control group. Proportional hazards analysis yielded a modest 25% survival benefit (hazard ratio = 0.75, 95% confidence interval: 0.63 to 0.89). Stratified analysis of the trial yielded a larger 52% survival benefit for those in the middle quintile of disease severity (hazard ratio = 0.48, 95% confidence interval: 0.29 to 0.79). In contrast, little of the survival benefit was explained by patients with the greatest disease severity (hazard ratio = 0.89, 95% confidence interval: 0.69 to 1.15). The discrepancy between crude and stratified analyses could be visualized by graphical displays and replicated with matched comparisons. Our approach for analyzing a randomized trial could help identify a potential sweet spot of an accentuated treatment effect.

Identifiants

pubmed: 31874202
pii: S0895-4356(19)30438-X
doi: 10.1016/j.jclinepi.2019.12.012
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

59-66

Subventions

Organisme : CIHR
Pays : Canada

Informations de copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Auteurs

Donald A Redelmeier (DA)

Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Evaluative Clinical Sciences Department, Sunnybrook Research Institute, Toronto, Ontario, Canada; Population and Global Health Department, Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada. Electronic address: dar@ices.on.ca.

Robert J Tibshirani (RJ)

Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA.

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Classifications MeSH