The methodological quality of individual participant data meta-analysis on intervention effects: systematic review

Author:

Wang HuanORCID,Chen YancongORCID,Lin YaliORCID,Abesig JuliusORCID,Wu Irene XYORCID,Tam WilsonORCID

Abstract

Abstract Objective To assess the methodological quality of individual participant data (IPD) meta-analysis and to identify areas for improvement. Design Systematic review. Data sources Medline, Embase, and Cochrane Database of Systematic Reviews. Eligibility criteria for selecting studies Systematic reviews with IPD meta-analyses of randomised controlled trials on intervention effects published in English. Results 323 IPD meta-analyses covering 21 clinical areas and published between 1991 and 2019 were included: 270 (84%) were non-Cochrane reviews and 269 (84%) were published in journals with a high impact factor (top quarter). The IPD meta-analyses showed low compliance in using a satisfactory technique to assess the risk of bias of the included randomised controlled trials (43%, 95% confidence interval 38% to 48%), accounting for risk of bias when interpreting results (40%, 34% to 45%), providing a list of excluded studies with justifications (32%, 27% to 37%), establishing an a priori protocol (31%, 26% to 36%), prespecifying methods for assessing both the overall effects (44%, 39% to 50%) and the participant-intervention interactions (31%, 26% to 36%), assessing and considering the potential of publication bias (31%, 26% to 36%), and conducting a comprehensive literature search (19%, 15% to 23%). Up to 126 (39%) IPD meta-analyses failed to obtain IPD from 90% or more of eligible participants or trials, among which only 60 (48%) provided reasons and 21 (17%) undertook certain strategies to account for the unavailable IPD. Conclusions The methodological quality of IPD meta-analyses is unsatisfactory. Future IPD meta-analyses need to establish an a priori protocol with prespecified data syntheses plan, comprehensively search the literature, critically appraise included randomised controlled trials with appropriate technique, account for risk of bias during data analyses and interpretation, and account for unavailable IPD.

Publisher

BMJ

Subject

General Engineering

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3