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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

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, par...

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Autores principales: Sarri, Grammati, Patorno, Elisabetta, Yuan, Hongbo, Guo, Jianfei (Jeff), Bennett, Dimitri, Wen, Xuerong, Zullo, Andrew R, Largent, Joan, Panaccio, Mary, Gokhale, Mugdha, Moga, Daniela Claudia, Ali, M Sanni, Debray, Thomas P A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961747/
https://www.ncbi.nlm.nih.gov/pubmed/33298465
http://dx.doi.org/10.1136/bmjebm-2020-111493
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author Sarri, Grammati
Patorno, Elisabetta
Yuan, Hongbo
Guo, Jianfei (Jeff)
Bennett, Dimitri
Wen, Xuerong
Zullo, Andrew R
Largent, Joan
Panaccio, Mary
Gokhale, Mugdha
Moga, Daniela Claudia
Ali, M Sanni
Debray, Thomas P A
author_facet Sarri, Grammati
Patorno, Elisabetta
Yuan, Hongbo
Guo, Jianfei (Jeff)
Bennett, Dimitri
Wen, Xuerong
Zullo, Andrew R
Largent, Joan
Panaccio, Mary
Gokhale, Mugdha
Moga, Daniela Claudia
Ali, M Sanni
Debray, Thomas P A
author_sort Sarri, Grammati
collection PubMed
description 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.
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spelling pubmed-89617472022-04-11 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 Sarri, Grammati Patorno, Elisabetta Yuan, Hongbo Guo, Jianfei (Jeff) Bennett, Dimitri Wen, Xuerong Zullo, Andrew R Largent, Joan Panaccio, Mary Gokhale, Mugdha Moga, Daniela Claudia Ali, M Sanni Debray, Thomas P A BMJ Evid Based Med Evidence Synthesis 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. BMJ Publishing Group 2022-04 2020-12-09 /pmc/articles/PMC8961747/ /pubmed/33298465 http://dx.doi.org/10.1136/bmjebm-2020-111493 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Evidence Synthesis
Sarri, Grammati
Patorno, Elisabetta
Yuan, Hongbo
Guo, Jianfei (Jeff)
Bennett, Dimitri
Wen, Xuerong
Zullo, Andrew R
Largent, Joan
Panaccio, Mary
Gokhale, Mugdha
Moga, Daniela Claudia
Ali, M Sanni
Debray, Thomas P A
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Evidence Synthesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961747/
https://www.ncbi.nlm.nih.gov/pubmed/33298465
http://dx.doi.org/10.1136/bmjebm-2020-111493
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