title: Conducting quantitative synthesis when comparing medical interventions: AHRQ and the Effective Health Care Program.
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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Public Health and Preventive Medicine, Oregon Evidence-based Practice Center, Oregon Health and Science University, Portland, OR 97239, USA. fur@ohsu.edu
authors.forename: Rongwei
authors.initials: R
authors.lastname: Fu
authors.forename: Gerald
authors.initials: G
authors.lastname: Gartlehner
authors.forename: Mark
authors.initials: M
authors.lastname: Grant
authors.forename: Tatyana
authors.initials: T
authors.lastname: Shamliyan
authors.forename: Art
authors.initials: A
authors.lastname: Sedrakyan
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Wilt
authors.forename: Lauren
authors.initials: L
authors.lastname: Griffith
authors.forename: Mark
authors.initials: M
authors.lastname: Oremus
authors.forename: Parminder
authors.initials: P
authors.lastname: Raina
authors.forename: Afisi
authors.initials: A
authors.lastname: Ismaila
authors.forename: Pasqualina
authors.initials: P
authors.lastname: Santaguida
authors.forename: Joseph
authors.initials: J
authors.lastname: Lau
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Trikalinos
mesh_terms: D015982:Bias; D057186:Comparative Effectiveness Research / Q000379:methods*; D032921:Consensus; D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine / Q000379:methods; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D012196:Review Literature as Topic; D013223:Statistics as Topic; D014481:United States; D016270:United States Agency for Healthcare Research and Quality