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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Statistics in Medici...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Statistics in Medicine
Article . 2017 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
zbMATH Open
Article . 2017
Data sources: zbMATH Open
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Multiplicity considerations in subgroup analysis

Authors: Alex Dmitrienko; Brian Millen; Ilya Lipkovich;

Multiplicity considerations in subgroup analysis

Abstract

This paper deals with the general topic of subgroup analysis in late‐stage clinical trials with emphasis on multiplicity considerations. The discussion begins with multiplicity issues arising in the context of exploratory subgroup analysis, including principled approaches to subgroup search that are applied as part of subgroup exploration exercises as well as in adaptive biomarker‐driven designs. Key considerations in confirmatory subgroup analysis based on one or more pre‐specified patient populations are reviewed, including a survey of multiplicity adjustment methods recommended in multi‐population phase III clinical trials. Guidelines for interpretation of significant findings in several patient populations are introduced to facilitate the decision‐making process and achieve consistent labeling across development programs. Copyright © 2017 John Wiley & Sons, Ltd.

Related Organizations
Keywords

clinical trials, Clinical Trials as Topic, multiplicity adjustment, Endpoint Determination, influence and interaction conditions, Guidelines as Topic, Statistics, Nonparametric, Applications of statistics to biology and medical sciences; meta analysis, Decision Theory, Research Design, Sample Size, exploratory subgroup analysis, confirmatory subgroup analysis, Humans, Biomarkers

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Top 10%
Top 10%
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