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Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests
The confirmatory analysis of pre-specified multiple hypotheses has become common in pivotal clinical trials. In the recent past multiple test procedures have been developed that reflect the relative importance of different study objectives, such as fixed sequence, fallback, and gatekeeping procedure...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
WILEY-VCH Verlag
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427907/ https://www.ncbi.nlm.nih.gov/pubmed/21837623 http://dx.doi.org/10.1002/bimj.201000239 |
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author | Bretz, Frank Posch, Martin Glimm, Ekkehard Klinglmueller, Florian Maurer, Willi Rohmeyer, Kornelius |
author_facet | Bretz, Frank Posch, Martin Glimm, Ekkehard Klinglmueller, Florian Maurer, Willi Rohmeyer, Kornelius |
author_sort | Bretz, Frank |
collection | PubMed |
description | The confirmatory analysis of pre-specified multiple hypotheses has become common in pivotal clinical trials. In the recent past multiple test procedures have been developed that reflect the relative importance of different study objectives, such as fixed sequence, fallback, and gatekeeping procedures. In addition, graphical approaches have been proposed that facilitate the visualization and communication of Bonferroni-based closed test procedures for common multiple test problems, such as comparing several treatments with a control, assessing the benefit of a new drug for more than one endpoint, combined non-inferiority and superiority testing, or testing a treatment at different dose levels in an overall and a subpopulation. In this paper, we focus on extended graphical approaches by dissociating the underlying weighting strategy from the employed test procedure. This allows one to first derive suitable weighting strategies that reflect the given study objectives and subsequently apply appropriate test procedures, such as weighted Bonferroni tests, weighted parametric tests accounting for the correlation between the test statistics, or weighted Simes tests. We illustrate the extended graphical approaches with several examples. In addition, we describe briefly the gMCP package in R, which implements some of the methods described in this paper. |
format | Online Article Text |
id | pubmed-3427907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | WILEY-VCH Verlag |
record_format | MEDLINE/PubMed |
spelling | pubmed-34279072012-08-27 Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests Bretz, Frank Posch, Martin Glimm, Ekkehard Klinglmueller, Florian Maurer, Willi Rohmeyer, Kornelius Biom J Research Articles The confirmatory analysis of pre-specified multiple hypotheses has become common in pivotal clinical trials. In the recent past multiple test procedures have been developed that reflect the relative importance of different study objectives, such as fixed sequence, fallback, and gatekeeping procedures. In addition, graphical approaches have been proposed that facilitate the visualization and communication of Bonferroni-based closed test procedures for common multiple test problems, such as comparing several treatments with a control, assessing the benefit of a new drug for more than one endpoint, combined non-inferiority and superiority testing, or testing a treatment at different dose levels in an overall and a subpopulation. In this paper, we focus on extended graphical approaches by dissociating the underlying weighting strategy from the employed test procedure. This allows one to first derive suitable weighting strategies that reflect the given study objectives and subsequently apply appropriate test procedures, such as weighted Bonferroni tests, weighted parametric tests accounting for the correlation between the test statistics, or weighted Simes tests. We illustrate the extended graphical approaches with several examples. In addition, we describe briefly the gMCP package in R, which implements some of the methods described in this paper. WILEY-VCH Verlag 2011-11 2011-08-12 /pmc/articles/PMC3427907/ /pubmed/21837623 http://dx.doi.org/10.1002/bimj.201000239 Text en Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation. |
spellingShingle | Research Articles Bretz, Frank Posch, Martin Glimm, Ekkehard Klinglmueller, Florian Maurer, Willi Rohmeyer, Kornelius Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests |
title | Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests |
title_full | Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests |
title_fullStr | Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests |
title_full_unstemmed | Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests |
title_short | Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests |
title_sort | graphical approaches for multiple comparison procedures using weighted bonferroni, simes, or parametric tests |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427907/ https://www.ncbi.nlm.nih.gov/pubmed/21837623 http://dx.doi.org/10.1002/bimj.201000239 |
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