Cited by 730Meta-analysis and systematic reviewsDelphi Technique in ResearchClinical practice guidelines implementation

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Conducting quantitative synthesis when comparing medical interventions: AHRQ and the Effective Health Care Program

Fu, Rongwei, Gartlehner, Gerald, Grant, Mark, Shamliyan, Tatyana, Sedrakyan, Art, Wilt, Timothy J., Griffith, Lauren, Oremus, Mark, Raina, Parminder, Ismaila, Afisi, Santaguida, Pasqualina, Lau, Joseph, Trikalinos, Thomas A.·2011·Journal of Clinical Epidemiology

DOI 10.1016/j.jclinepi.2010.08.010

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Abstract

Objective This article is to establish recommendations for conducting quantitative synthesis, or meta-analysis, using study-level data in comparative effectiveness reviews (CERs) for the Evidence-based Practice Center (EPC) program of the Agency for Healthcare Research and Quality.Study design and setting We focused on recurrent issues in the EPC program and the recommendations were developed using group discussion and consensus based on current knowledge in the literature.Results We first discussed considerations for deciding whether to combine studies, followed by discussions on indirect comparison and incorporation of indirect evidence. Then, we described our recommendations on choosing effect measures and statistical models, giving special attention to combining studies with rare events; and on testing and exploring heterogeneity. Finally, we briefly presented recommendations on combining studies of mixed design and on sensitivity analysis.Conclusion Quantitative synthesis should be conducted in a transparent and consistent way. Inclusion of multiple alternative interventions in CERs increases the complexity of quantitative synthesis, whereas the basic issues in quantitative synthesis remain crucial considerations in quantitative synthesis for a CER. We will cover more issues in future versions and update and improve recommendations with the accumulation of new research to advance the goal for transparency and consistency.

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Authors: Fu, R., Gartlehner, G., Grant, M., Shamliyan, T., Sedrakyan, A., Wilt, T. J., Griffith, L., Oremus, M., Raina, P., Ismaila, A., Santaguida, P., Lau, J., & Trikalinos, T. A.
Affiliations: Oregon Health & Science University (US)
Universität für Weiterbildung Krems (AT)
Blue Cross Blue Shield Association (US)
University of Minnesota (US)
Agency for Healthcare Research and Quality (US)
McMaster University (CA)
Tufts Medical Center (US)
Corresponding author: Fu, Rongwei
ORCID: Fu, Rongwei
Gartlehner, Gerald
Shamliyan, Tatyana
Sedrakyan, Art
Wilt, Timothy J.
Griffith, Lauren
Oremus, Mark
Raina, Parminder
Ismaila, Afisi
Santaguida, Pasqualina
Lau, Joseph
Trikalinos, Thomas A.
Venue: Journal of Clinical Epidemiology
Publisher: Elsevier BV
Published: 2011-11
Volume: 64
Issue: 11
Pages: 1187-1197
ISSN: 0895-4356 (ISSN-L)
DOI: 10.1016/j.jclinepi.2010.08.010
OpenAlex: W2145171366
Cited by: 730
References: 62
Citations by year: 2026: 46 · 2025: 64 · 2024: 88 · 2023: 75 · 2022: 99 · 2021: 92 · 2020: 56 · 2019: 50 · 2018: 29 · 2017: 27 · 2016: 33 · 2015: 26 · 2014: 18 · 2013: 13 · 2012: 10
FWCI: 14.89
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Clinical Epidemiology (publishedVersion)
PubMed (publishedVersion)
Funders: Agency for Healthcare Research and Quality
H2020 European Research Council
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Social Sciences › Sociology and Political Science › Delphi Technique in Research
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
Keywords: meta-analysis, comparative effectiveness research, evidence-based practice centers, Agency for Healthcare Research and Quality, indirect comparison, effect measures, rare events, heterogeneity, sensitivity analysis
MeSH terms: Data Interpretation, Statistical, Humans, Review Literature as Topic, Statistics as Topic, United States, Meta-Analysis as Topic, Models, Statistical, Bias, United States Agency for Healthcare Research and Quality, Outcome Assessment, Health Care, Evidence-Based Medicine, Consensus, Comparative Effectiveness Research

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Conducting quantitative synthesis when comparing medical interventions: AHRQ and the Effective Health Care Program

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