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非随机研究和随机对照试验综合框架:为医疗保健决策进行系统评价和荟萃分析的指南。

Framework for the synthesis of non-randomised studies and randomised controlled trials: a guidance on conducting a systematic review and meta-analysis for healthcare decision making.

机构信息

Real World Evidence Sciences, Visible Analytics Ltd, Oxford, UK

Division of Pharmacoepidemiology and Pharmacoeconomics, Dept. of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

出版信息

BMJ Evid Based Med. 2022 Apr;27(2):109-119. doi: 10.1136/bmjebm-2020-111493. Epub 2020 Dec 9.

DOI:10.1136/bmjebm-2020-111493
PMID:33298465
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8961747/
Abstract

INTRODUCTION

High-quality randomised controlled trials (RCTs) provide the most reliable evidence on the comparative efficacy of new medicines. However, non-randomised studies (NRS) are increasingly recognised as a source of insights into the real-world performance of novel therapeutic products, particularly when traditional RCTs are impractical or lack generalisability. This means there is a growing need for synthesising evidence from RCTs and NRS in healthcare decision making, particularly given recent developments such as innovative study designs, digital technologies and linked databases across countries. Crucially, however, no formal framework exists to guide the integration of these data types.

OBJECTIVES AND METHODS

To address this gap, we used a mixed methods approach (review of existing guidance, methodological papers, Delphi survey) to develop guidance for researchers and healthcare decision-makers on when and how to best combine evidence from NRS and RCTs to improve transparency and build confidence in the resulting summary effect estimates.

RESULTS

Our framework comprises seven steps on guiding the integration and interpretation of evidence from NRS and RCTs and we offer recommendations on the most appropriate statistical approaches based on three main analytical scenarios in healthcare decision making (specifically, 'high-bar evidence' when RCTs are the preferred source of evidence, 'medium,' and 'low' when NRS is the main source of inference).

CONCLUSION

Our framework augments existing guidance on assessing the quality of NRS and their compatibility with RCTs for evidence synthesis, while also highlighting potential challenges in implementing it. This manuscript received endorsement from the International Society for Pharmacoepidemiology.

摘要

简介

高质量的随机对照试验(RCT)为新药的比较疗效提供了最可靠的证据。然而,非随机研究(NRS)越来越被认为是了解新型治疗产品在实际应用中性能的一种来源,尤其是当传统 RCT 不切实际或缺乏普遍性时。这意味着在医疗保健决策中越来越需要综合 RCT 和 NRS 的证据,特别是考虑到最近的一些发展,如创新的研究设计、数字技术和跨国界的关联数据库。然而,至关重要的是,目前还没有正式的框架来指导这些数据类型的整合。

目的和方法

为了解决这一差距,我们使用混合方法(对现有指南、方法学论文、德尔菲调查的回顾)为研究人员和医疗保健决策者制定了指导意见,说明何时以及如何最好地结合 NRS 和 RCT 的证据,以提高透明度并增强对最终综合效果估计的信心。

结果

我们的框架包括七个步骤,用于指导 NRS 和 RCT 证据的整合和解释,我们根据医疗保健决策中的三个主要分析情况(特别是 RCT 是首选证据来源时的“高门槛证据”,NRS 是主要推断来源时的“中”和“低”),就最适合的统计方法提出了建议。

结论

我们的框架补充了现有的关于评估 NRS 质量及其与 RCT 进行证据综合的兼容性的指南,同时还强调了实施该框架的潜在挑战。本文得到了国际药物流行病学学会的认可。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a481/8961747/aed7107ea968/bmjebm-2020-111493f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a481/8961747/1cdaf200d46e/bmjebm-2020-111493f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a481/8961747/b498429f3d65/bmjebm-2020-111493f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a481/8961747/aed7107ea968/bmjebm-2020-111493f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a481/8961747/1cdaf200d46e/bmjebm-2020-111493f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a481/8961747/b498429f3d65/bmjebm-2020-111493f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a481/8961747/aed7107ea968/bmjebm-2020-111493f03.jpg

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