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随机和非随机药物干预研究的治疗效果:Meta 分析。

Treatment Effects in Randomized and Nonrandomized Studies of Pharmacological Interventions: A Meta-Analysis.

机构信息

Department of Health Policy, London School of Economics and Political Science, London, United Kingdom.

World Health Organization Collaborating Centre for Pharmaceutical Pricing and Reimbursement Policies, Pharmacoeconomics Department, Gesundheit Österreich GmbH (GÖG)/Austrian National Public Health Institute, Vienna, Austria.

出版信息

JAMA Netw Open. 2024 Sep 3;7(9):e2436230. doi: 10.1001/jamanetworkopen.2024.36230.


DOI:10.1001/jamanetworkopen.2024.36230
PMID:39331390
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11437387/
Abstract

IMPORTANCE: Randomized clinical trials (RCTs) are widely regarded as the methodological benchmark for assessing clinical efficacy and safety of health interventions. There is growing interest in using nonrandomized studies to assess efficacy and safety of new drugs. OBJECTIVE: To determine how treatment effects for the same drug compare when evaluated in nonrandomized vs randomized studies. DATA SOURCES: Meta-analyses published between 2009 and 2018 were identified in MEDLINE via PubMed and the Cochrane Database of Systematic Reviews. Data analysis was conducted from October 2019 to July 2024. STUDY SELECTION: Meta-analyses of pharmacological interventions were eligible for inclusion if both randomized and nonrandomized studies contributed to a single meta-analytic estimate. DATA EXTRACTION AND SYNTHESIS: For this meta-analysis using a meta-epidemiological framework, separate summary effect size estimates were calculated for nonrandomized and randomized studies within each meta-analysis using a random-effects model and then these estimates were compared. The reporting of this study followed the Guidelines for Reporting Meta-Epidemiological Methodology Research and relevant portions of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. MAIN OUTCOME AND MEASURES: The primary outcome was discrepancies in treatment effects obtained from nonrandomized and randomized studies, as measured by the proportion of meta-analyses where the 2 study types disagreed about the direction or magnitude of effect, disagreed beyond chance about the effect size estimate, and the summary ratio of odds ratios (ROR) obtained from nonrandomized vs randomized studies combined across all meta-analyses. RESULTS: A total of 346 meta-analyses with 2746 studies were included. Statistical conclusions about drug benefits and harms were different for 130 of 346 meta-analyses (37.6%) when focusing solely on either nonrandomized or randomized studies. Disagreements were beyond chance for 54 meta-analyses (15.6%). Across all meta-analyses, there was no strong evidence of consistent differences in treatment effects obtained from nonrandomized vs randomized studies (summary ROR, 0.95; 95% credible interval [CrI], 0.89-1.02). Compared with experimental nonrandomized studies, randomized studies produced on average a 19% smaller treatment effect (ROR, 0.81; 95% CrI, 0.68-0.97). There was increased heterogeneity in effect size estimates obtained from nonrandomized compared with randomized studies. CONCLUSIONS AND RELEVANCE: In this meta-analysis of treatment effects of pharmacological interventions obtained from randomized and nonrandomized studies, there was no overall difference in effect size estimates between study types on average, but nonrandomized studies both overestimated and underestimated treatment effects observed in randomized studies and introduced additional uncertainty. These findings suggest that relying on nonrandomized studies as substitutes for RCTs may introduce additional uncertainty about the therapeutic effects of new drugs.

摘要

重要性:随机临床试验(RCTs)被广泛认为是评估健康干预措施临床疗效和安全性的方法学基准。人们越来越感兴趣的是使用非随机研究来评估新药的疗效和安全性。 目的:确定相同药物的治疗效果在非随机和随机研究中评估时有何不同。 数据来源:通过 PubMed 在 MEDLINE 中检索了 2009 年至 2018 年间发表的荟萃分析,并在 Cochrane 系统评价数据库中进行了检索。数据分析于 2019 年 10 月至 2024 年 7 月进行。 研究选择:如果药理学干预的荟萃分析同时包含随机和非随机研究,那么这些研究才有资格被纳入。 数据提取和综合:使用荟萃分析的元分析框架,对于每个荟萃分析,使用随机效应模型分别计算非随机和随机研究的综合效应大小估计值,然后比较这些估计值。本研究报告遵循了荟萃流行病学方法学研究报告指南和系统评价和荟萃分析(PRISMA)报告指南的相关部分。 主要结果和测量:主要结果是通过非随机和随机研究获得的治疗效果的差异,其衡量标准是两种研究类型在药物效果的方向或幅度上存在不一致意见的荟萃分析比例、对效应大小估计不一致的荟萃分析比例,以及从所有荟萃分析中获得的非随机与随机研究的汇总比值比(ROR)。 结果:共纳入了 346 项荟萃分析,涉及 2746 项研究。当仅关注非随机或随机研究时,346 项荟萃分析中有 130 项(37.6%)对药物的益处和危害得出了不同的统计结论。54 项荟萃分析(15.6%)的结论超出了随机的可能性。在所有荟萃分析中,非随机和随机研究的治疗效果没有明显的差异(综合 ROR,0.95;95%置信区间[CrI],0.89-1.02)。与实验性非随机研究相比,随机研究的治疗效果平均小 19%(ROR,0.81;95%CrI,0.68-0.97)。与随机研究相比,非随机研究的效应量估计值的异质性更大。 结论和相关性:在这项关于从随机和非随机研究中获得的药理学干预治疗效果的荟萃分析中,两种研究类型的平均效应大小估计值没有总体差异,但非随机研究既高估了也低估了随机研究中观察到的治疗效果,并引入了额外的不确定性。这些发现表明,依赖非随机研究作为 RCTs 的替代品可能会增加对新药治疗效果的不确定性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/a68beb39de36/jamanetwopen-e2436230-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/1fd470aa47c7/jamanetwopen-e2436230-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/f75228434ae2/jamanetwopen-e2436230-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/fbd8604e4d4b/jamanetwopen-e2436230-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/a68beb39de36/jamanetwopen-e2436230-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/1fd470aa47c7/jamanetwopen-e2436230-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/f75228434ae2/jamanetwopen-e2436230-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/fbd8604e4d4b/jamanetwopen-e2436230-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb96/11437387/a68beb39de36/jamanetwopen-e2436230-g004.jpg

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本文引用的文献

[1]
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[2]
Feasibility of Emulating Clinical Trials Supporting US FDA Supplemental Indication Approvals of Drugs and Biologics.

JAMA Intern Med. 2023-11-1

[3]
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JAMA Netw Open. 2023-9-5

[4]
Review of Evidence Supporting 2022 US Food and Drug Administration Drug Approvals.

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[5]
Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses: Results of 32 Clinical Trials.

JAMA. 2023-4-25

[6]
Replacing RCTs with real world data for regulatory decision making: a self-fulfilling prophecy?

BMJ. 2023-3-2

[7]
Target Trial Emulation: A Framework for Causal Inference From Observational Data.

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[8]
Agreement of treatment effects from observational studies and randomized controlled trials evaluating hydroxychloroquine, lopinavir-ritonavir, or dexamethasone for covid-19: meta-epidemiological study.

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[9]
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