Prevention Science

, Volume 14, Issue 2, pp 144-156

Methods for Synthesizing Findings on Moderation Effects Across Multiple Randomized Trials

  • C. Hendricks BrownAffiliated withUniversity of Miami, Miller School of Medicine Email author 
  • , Zili SlobodaAffiliated withJBS International
  • , Fabrizio FaggianoAffiliated withAvogadro University
  • , Brent TeasdaleAffiliated withGeorgia State University
  • , Ferdinand KellerAffiliated withUniversity of Ulm
  • , Gregor BurkhartAffiliated withEuropean Monitoring Centre for Drugs and Drug Addiction
  • , Federica Vigna-TagliantiAffiliated withPiedmont Centre for Drug Addiction Epidemiology
  • , George HoweAffiliated withGeorge Washington University
  • , Katherine MasynAffiliated withHarvard University
    • , Wei WangAffiliated withUniversity of Miami, Miller School of MedicineUniversity of South Florida
    • , Bengt MuthénAffiliated withUniversity of Miami, Miller School of MedicineUCLA
    • , Peggy StephensAffiliated withUniversity of Miami, Miller School of MedicineAkron University
    • , Scott GreyAffiliated withUniversity of Miami, Miller School of MedicineKent State University
    • , Tatiana PerrinoAffiliated withUniversity of Miami, Miller School of Medicine
    • , Prevention Science and Methodology Group

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This paper presents new methods for synthesizing results from subgroup and moderation analyses across different randomized trials. We demonstrate that such a synthesis generally results in additional power to detect significant moderation findings above what one would find in a single trial. Three general methods for conducting synthesis analyses are discussed, with two methods, integrative data analysis and parallel analyses, sharing a large advantage over traditional methods available in meta-analysis. We present a broad class of analytic models to examine moderation effects across trials that can be used to assess their overall effect and explain sources of heterogeneity, and present ways to disentangle differences across trials due to individual differences, contextual level differences, intervention, and trial design.


Meta-analysis Parallel data analysis Integrative data analysis Variation in impact Subgroup analyses