From living systematic reviews to meta-analytical research domains.

IF 6.6 2区 医学 Q1 PSYCHIATRY Evidence Based Mental Health Pub Date : 2022-07-19 DOI:10.1136/ebmental-2022-300509
Pim Cuijpers, Clara Miguel, Davide Papola, Mathias Harrer, Eirini Karyotaki
{"title":"From living systematic reviews to meta-analytical research domains.","authors":"Pim Cuijpers, Clara Miguel, Davide Papola, Mathias Harrer, Eirini Karyotaki","doi":"10.1136/ebmental-2022-300509","DOIUrl":null,"url":null,"abstract":"<p><p>Because of the rapidly increasing number of randomised controlled trials (RCTs) and meta-analyses in many fields, there is an urgent need to step up from meta-analyses to higher levels of aggregation of outcomes of RCTs. Network meta-analyses and umbrella reviews allow higher levels of aggregation of RCT outcomes, but cannot adequately cover the evidence for a whole field. The 'Meta-Analytic Research Domain' (MARD) may be a new methodology to aggregate RCT data of a whole field. A MARD is a living systematic review of a research domain that cannot be covered by one PICO. For example, a MARD of psychotherapy for depression covers all RCTs comparing the effects of all types of psychotherapy to control conditions, to each other, to pharmacotherapy and combined treatment. It also covers all RCTs comparing treatment formats, the effects in different target groups, subtypes of depression and secondary outcomes. Although the time and resources needed to build a MARD are considerable, they offer many advantages, including a comprehensive and consistent overview of a research field and important meta-analytic studies that cannot be conducted with conventional methods. MARDs are a promising method to step up the aggregation of RCTs to a next level and it is highly relevant to work out the methods of this approach in a more detailed way.</p>","PeriodicalId":12233,"journal":{"name":"Evidence Based Mental Health","volume":null,"pages":null},"PeriodicalIF":6.6000,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/6f/85/ebmental-2022-300509.PMC9685685.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evidence Based Mental Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/ebmental-2022-300509","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0

Abstract

Because of the rapidly increasing number of randomised controlled trials (RCTs) and meta-analyses in many fields, there is an urgent need to step up from meta-analyses to higher levels of aggregation of outcomes of RCTs. Network meta-analyses and umbrella reviews allow higher levels of aggregation of RCT outcomes, but cannot adequately cover the evidence for a whole field. The 'Meta-Analytic Research Domain' (MARD) may be a new methodology to aggregate RCT data of a whole field. A MARD is a living systematic review of a research domain that cannot be covered by one PICO. For example, a MARD of psychotherapy for depression covers all RCTs comparing the effects of all types of psychotherapy to control conditions, to each other, to pharmacotherapy and combined treatment. It also covers all RCTs comparing treatment formats, the effects in different target groups, subtypes of depression and secondary outcomes. Although the time and resources needed to build a MARD are considerable, they offer many advantages, including a comprehensive and consistent overview of a research field and important meta-analytic studies that cannot be conducted with conventional methods. MARDs are a promising method to step up the aggregation of RCTs to a next level and it is highly relevant to work out the methods of this approach in a more detailed way.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
从活生生的系统综述到元分析研究领域。
由于许多领域的随机对照试验(RCT)和荟萃分析的数量迅速增加,因此迫切需要从荟萃分析提升到对 RCT 结果进行更高层次的汇总。网络荟萃分析和总括综述可以对研究性试验结果进行更高层次的汇总,但无法充分涵盖整个领域的证据。元分析研究领域"(MARD)可能是汇总整个领域 RCT 数据的一种新方法。元分析研究领域 "是对一个 PICO 无法涵盖的研究领域进行的系统性审查。例如,针对抑郁症心理疗法的 MARD 涵盖了将所有类型的心理疗法与对照条件、相互之间、药物疗法和综合疗法的效果进行比较的所有 RCT。它还包括所有对治疗形式、不同目标群体的效果、抑郁症亚型和次要结果进行比较的研究性试验。虽然建立 MARD 所需的时间和资源相当可观,但它具有很多优势,包括对研究领域进行全面、一致的概述,以及进行传统方法无法进行的重要元分析研究。MARDs 是一种很有前途的方法,可以将 RCTs 的汇总提高到一个新的水平,因此,更详细地研究这种方法的方法是非常有意义的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
18.10
自引率
7.70%
发文量
31
期刊介绍: Evidence-Based Mental Health alerts clinicians to important advances in treatment, diagnosis, aetiology, prognosis, continuing education, economic evaluation and qualitative research in mental health. Published by the British Psychological Society, the Royal College of Psychiatrists and the BMJ Publishing Group the journal surveys a wide range of international medical journals applying strict criteria for the quality and validity of research. Clinicians assess the relevance of the best studies and the key details of these essential studies are presented in a succinct, informative abstract with an expert commentary on its clinical application.Evidence-Based Mental Health is a multidisciplinary, quarterly publication.
期刊最新文献
Vitruvian plot: a visualisation tool for multiple outcomes in network meta-analysis. Important adverse events to be evaluated in antidepressant trials and meta-analyses in depression: a large international preference study including patients and healthcare professionals. Comparison of prediction methods for treatment continuation of antipsychotics in children and adolescents with schizophrenia. Components of smartphone cognitive-behavioural therapy for subthreshold depression among 1093 university students: a factorial trial. Associations between antipsychotics and risk of violent crimes and suicidal behaviour in personality disorder.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1