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Simultaneous inference of a binary composite endpoint and its components

Grosse Ruse, Mareile LU ; Ritz, C and Hothorn, L A (2017) In Journal of Biopharmaceutical Statistics 27(1). p.56-69
Abstract
Binary composite endpoints offer some advantages as a way to succinctly combine evidence from a number of related binary endpoints recorded in the same clinical trial into a single outcome. However, as some concerns about the clinical relevance as well as the interpretation of such composite endpoints have been raised, it is recommended to evaluate the composite endpoint jointly with the involved components. We propose an approach for carrying out simultaneous inference based on separate model fits for each endpoint, yet controlling the family-wise type I error rate asymptotically. The key idea is to stack parameter estimates from the different fits and derive their joint asymptotic distribution. Simulations show that the proposed approach... (More)
Binary composite endpoints offer some advantages as a way to succinctly combine evidence from a number of related binary endpoints recorded in the same clinical trial into a single outcome. However, as some concerns about the clinical relevance as well as the interpretation of such composite endpoints have been raised, it is recommended to evaluate the composite endpoint jointly with the involved components. We propose an approach for carrying out simultaneous inference based on separate model fits for each endpoint, yet controlling the family-wise type I error rate asymptotically. The key idea is to stack parameter estimates from the different fits and derive their joint asymptotic distribution. Simulations show that the proposed approach comes closer to nominal levels and has comparable or higher power as compared to existing approaches, even for moderate sample sizes (around 100-200 observations). The method is compared to the gatekeeping approach and results are provided in the Supplementary Material. In two data examples we show how the procedure may be adapted to handle local significance levels specified through a priori given weights. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Biopharmaceutical Statistics
volume
27
issue
1
pages
56 - 69
publisher
Taylor & Francis
external identifiers
  • pmid:26881805
  • scopus:84973165910
  • wos:000396555100005
ISSN
1520-5711
DOI
10.1080/10543406.2016.1148704
language
English
LU publication?
yes
id
4c6f545f-be17-4416-a69e-9e3c52281389 (old id 8825344)
date added to LUP
2016-03-07 13:00:01
date last changed
2018-04-15 03:19:02
@article{4c6f545f-be17-4416-a69e-9e3c52281389,
  abstract     = {Binary composite endpoints offer some advantages as a way to succinctly combine evidence from a number of related binary endpoints recorded in the same clinical trial into a single outcome. However, as some concerns about the clinical relevance as well as the interpretation of such composite endpoints have been raised, it is recommended to evaluate the composite endpoint jointly with the involved components. We propose an approach for carrying out simultaneous inference based on separate model fits for each endpoint, yet controlling the family-wise type I error rate asymptotically. The key idea is to stack parameter estimates from the different fits and derive their joint asymptotic distribution. Simulations show that the proposed approach comes closer to nominal levels and has comparable or higher power as compared to existing approaches, even for moderate sample sizes (around 100-200 observations). The method is compared to the gatekeeping approach and results are provided in the Supplementary Material. In two data examples we show how the procedure may be adapted to handle local significance levels specified through a priori given weights.},
  author       = {Grosse Ruse, Mareile and Ritz, C and Hothorn, L A},
  issn         = {1520-5711},
  language     = {eng},
  number       = {1},
  pages        = {56--69},
  publisher    = {Taylor & Francis},
  series       = {Journal of Biopharmaceutical Statistics},
  title        = {Simultaneous inference of a binary composite endpoint and its components},
  url          = {http://dx.doi.org/10.1080/10543406.2016.1148704},
  volume       = {27},
  year         = {2017},
}