Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal
<p>Abstract</p> <p>Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the tr...
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doaj-02000dbdcb5041a1868a0976c6313cc02020-11-24T21:19:07ZengBMCTrials1745-62152010-08-011118510.1186/1745-6215-11-85Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposalKent David MRothwell Peter MIoannidis John PAAltman Doug GHayward Rodney A<p>Abstract</p> <p>Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability.</p> http://www.trialsjournal.com/content/11/1/85 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kent David M Rothwell Peter M Ioannidis John PA Altman Doug G Hayward Rodney A |
spellingShingle |
Kent David M Rothwell Peter M Ioannidis John PA Altman Doug G Hayward Rodney A Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal Trials |
author_facet |
Kent David M Rothwell Peter M Ioannidis John PA Altman Doug G Hayward Rodney A |
author_sort |
Kent David M |
title |
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal |
title_short |
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal |
title_full |
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal |
title_fullStr |
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal |
title_full_unstemmed |
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal |
title_sort |
assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal |
publisher |
BMC |
series |
Trials |
issn |
1745-6215 |
publishDate |
2010-08-01 |
description |
<p>Abstract</p> <p>Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability.</p> |
url |
http://www.trialsjournal.com/content/11/1/85 |
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