Literature DB >> 22855368

Statistical considerations when using a composite endpoint for comparing treatment groups.

Guadalupe Gómez1, Stephen W Lagakos.   

Abstract

When comparing two treatment groups in a time-to-event analysis, it is common to use a composite event consisting of two or more distinct outcomes. The goal of this paper is to develop a statistical methodology to derive efficiency guidelines for deciding whether to expand a study primary endpoint from E1 (for example, non-fatal myocardial infarction and cardiovascular death) to the composite of E1 and E2 (for example, non-fatal myocardial infarction, cardiovascular death or revascularisation). We investigate this problem by considering the asymptotic relative efficiency of a log-rank test for comparing treatment groups with respect to a primary relevant endpoint E1 versus the composite primary endpoint, say E, of E1 and E2, where E2 is some additional endpoint.
Copyright © 2012 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 22855368     DOI: 10.1002/sim.5547

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  7 in total

1.  An optimal Wilcoxon-Mann-Whitney test of mortality and a continuous outcome.

Authors:  Roland A Matsouaka; Aneesh B Singhal; Rebecca A Betensky
Journal:  Stat Methods Med Res       Date:  2016-12-29       Impact factor: 3.021

2.  Interim monitoring in a treatment strategy trial with a composite primary endpoint.

Authors:  Minhee Kang; Birgit Grund; Sally Hunsberger; David Glidden; Paul Volberding
Journal:  Contemp Clin Trials       Date:  2019-09-11       Impact factor: 2.226

3.  Semi-Competing Risks Data Analysis: Accounting for Death as a Competing Risk When the Outcome of Interest Is Nonterminal.

Authors:  Sebastien Haneuse; Kyu Ha Lee
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2016-04-12

4.  Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.

Authors:  Ina Jazić; Deborah Schrag; Daniel J Sargent; Sebastien Haneuse
Journal:  J Natl Cancer Inst       Date:  2016-07-05       Impact factor: 13.506

5.  Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints.

Authors:  Jordi Cortés Martínez; Ronald B Geskus; KyungMann Kim; Guadalupe Gómez Melis
Journal:  BMC Med Res Methodol       Date:  2021-05-06       Impact factor: 4.615

6.  Bin-CE: A comprehensive web application to decide upon the best set of outcomes to be combined in a binary composite endpoint.

Authors:  Josep Ramon Marsal; Ignacio Ferreira-González; Aida Ribera; Gerard Oristrell; Jose Ignacio Pijoan; David García-Dorado
Journal:  PLoS One       Date:  2018-12-13       Impact factor: 3.240

7.  Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints.

Authors:  Martina E McMenamin; Jessica K Barrett; Anna Berglind; James M S Wason
Journal:  Stat Med       Date:  2022-02-23       Impact factor: 2.497

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.