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

• Published in: Journal of clinical epidemiology 2011-11, Vol.64 (11), p.1187-1197
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Bias; Comparative Effectiveness Research - methods; Confidence intervals; Congressional investigations; Consensus; Data Interpretation, Statistical; Effect measure; Epidemiology; Evidence-Based Medicine - methods; Fixed/random effects model; Health care; Heart attacks; Heterogeneity; Humans; Indirect comparison; Internal Medicine; Meta-analysis; Meta-Analysis as Topic; Methods; Mixed design; Models, Statistical; Outcome Assessment (Health Care) - statistics & numerical data; Reporting requirements; Review Literature as Topic; Sensitivity analysis; Statistical models; Statistics as Topic; Studies; United States; United States Agency for Healthcare Research and Quality
• ISSN: 0895-4356; 1878-5921
• DOI: 10.1016/j.jclinepi.2010.08.010

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.