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Let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses
We appreciate Jacobson and colleagues’ thoughtful commentary on our meta-review of mobile phone-based interventions for mental health. In this response, we address 2 issues raised: requiring low to moderate heterogeneity (I(2) < 50%) and requiring no evidence of publication bias for evidence to b...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728627/ https://www.ncbi.nlm.nih.gov/pubmed/36484072 http://dx.doi.org/10.1371/journal.pdig.0000127 |
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author | Goldberg, Simon B. Torous, John Sun, Shufang |
author_facet | Goldberg, Simon B. Torous, John Sun, Shufang |
author_sort | Goldberg, Simon B. |
collection | PubMed |
description | We appreciate Jacobson and colleagues’ thoughtful commentary on our meta-review of mobile phone-based interventions for mental health. In this response, we address 2 issues raised: requiring low to moderate heterogeneity (I(2) < 50%) and requiring no evidence of publication bias for evidence to be classified as “convincing.” While we agree these represent a high bar, we disagree that these requirements are destined to fail. Other effect sizes reported in the literature, including effect sizes related to mental health interventions and effect sizes related to mobile health (mHealth) interventions (although not their combination) have met requirements for convincing evidence. Jacobson and colleagues argue that features of the mHealth interventions may produce heterogeneity when meta-analyses combine across intervention types. However, several of the effect sizes we reviewed were based on relatively homogeneous portions of the literature and many of the effect sizes we reviewed showed low to moderate heterogeneity. Ideally, future meta-analyses will examine intervention features as moderators of treatment effects. While an absence of publication bias may be a stringent criterion, all but 2 of the 34 effect sizes we reviewed did not report formal tests of publication bias. Clearly, there is a need to reach consensus on how the strength of evidence for mHealth interventions can be evaluated. From our perspective, convincing evidence will ultimately come from large-scale randomized controlled trials employing rigorous comparison conditions along with meta-analyses that do not combine across control condition types, that examine theoretically important moderators, and report formal tests of publication bias. It is this kind of evidence that the public, the clinicians, and the scientific community may need to encourage adoption of mHealth interventions for mental health treatment and prevention. |
format | Online Article Text |
id | pubmed-9728627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97286272022-12-07 Let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses Goldberg, Simon B. Torous, John Sun, Shufang PLOS Digit Health Formal Comment We appreciate Jacobson and colleagues’ thoughtful commentary on our meta-review of mobile phone-based interventions for mental health. In this response, we address 2 issues raised: requiring low to moderate heterogeneity (I(2) < 50%) and requiring no evidence of publication bias for evidence to be classified as “convincing.” While we agree these represent a high bar, we disagree that these requirements are destined to fail. Other effect sizes reported in the literature, including effect sizes related to mental health interventions and effect sizes related to mobile health (mHealth) interventions (although not their combination) have met requirements for convincing evidence. Jacobson and colleagues argue that features of the mHealth interventions may produce heterogeneity when meta-analyses combine across intervention types. However, several of the effect sizes we reviewed were based on relatively homogeneous portions of the literature and many of the effect sizes we reviewed showed low to moderate heterogeneity. Ideally, future meta-analyses will examine intervention features as moderators of treatment effects. While an absence of publication bias may be a stringent criterion, all but 2 of the 34 effect sizes we reviewed did not report formal tests of publication bias. Clearly, there is a need to reach consensus on how the strength of evidence for mHealth interventions can be evaluated. From our perspective, convincing evidence will ultimately come from large-scale randomized controlled trials employing rigorous comparison conditions along with meta-analyses that do not combine across control condition types, that examine theoretically important moderators, and report formal tests of publication bias. It is this kind of evidence that the public, the clinicians, and the scientific community may need to encourage adoption of mHealth interventions for mental health treatment and prevention. Public Library of Science 2022-11-03 /pmc/articles/PMC9728627/ /pubmed/36484072 http://dx.doi.org/10.1371/journal.pdig.0000127 Text en © 2022 Goldberg et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Formal Comment Goldberg, Simon B. Torous, John Sun, Shufang Let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses |
title | Let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses |
title_full | Let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses |
title_fullStr | Let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses |
title_full_unstemmed | Let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses |
title_short | Let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses |
title_sort | let’s decide what would be convincing, conduct randomized trials with rigorous comparison conditions, and report tests of moderation and publication bias in meta-analyses |
topic | Formal Comment |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728627/ https://www.ncbi.nlm.nih.gov/pubmed/36484072 http://dx.doi.org/10.1371/journal.pdig.0000127 |
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