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Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases
Random‐effects meta‐analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516158/ https://www.ncbi.nlm.nih.gov/pubmed/27754556 http://dx.doi.org/10.1002/bimj.201500236 |
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author | Friede, Tim Röver, Christian Wandel, Simon Neuenschwander, Beat |
author_facet | Friede, Tim Röver, Christian Wandel, Simon Neuenschwander, Beat |
author_sort | Friede, Tim |
collection | PubMed |
description | Random‐effects meta‐analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random‐effects meta‐analysis assumes approximately normal effect estimates and a normal random‐effects model. However, standard methods based on this model ignore the uncertainty in estimating the between‐trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung‐Knapp‐Sidik‐Jonkman method (HKSJ), the modified Knapp‐Hartung method (mKH, a variation of the HKSJ method) and Bayesian random‐effects meta‐analyses with priors covering plausible heterogeneity values; [Formula: see text] code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes. |
format | Online Article Text |
id | pubmed-5516158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55161582017-08-02 Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases Friede, Tim Röver, Christian Wandel, Simon Neuenschwander, Beat Biom J Special Topic: ISCB2015 Random‐effects meta‐analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random‐effects meta‐analysis assumes approximately normal effect estimates and a normal random‐effects model. However, standard methods based on this model ignore the uncertainty in estimating the between‐trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung‐Knapp‐Sidik‐Jonkman method (HKSJ), the modified Knapp‐Hartung method (mKH, a variation of the HKSJ method) and Bayesian random‐effects meta‐analyses with priors covering plausible heterogeneity values; [Formula: see text] code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes. John Wiley and Sons Inc. 2016-10-18 2017-07 /pmc/articles/PMC5516158/ /pubmed/27754556 http://dx.doi.org/10.1002/bimj.201500236 Text en © 2016 The Authors. Biometrical Journal published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs (http://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Special Topic: ISCB2015 Friede, Tim Röver, Christian Wandel, Simon Neuenschwander, Beat Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases |
title | Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases |
title_full | Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases |
title_fullStr | Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases |
title_full_unstemmed | Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases |
title_short | Meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases |
title_sort | meta‐analysis of two studies in the presence of heterogeneity with applications in rare diseases |
topic | Special Topic: ISCB2015 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516158/ https://www.ncbi.nlm.nih.gov/pubmed/27754556 http://dx.doi.org/10.1002/bimj.201500236 |
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