Sequential Bayesian Data Synthesis for Mediation and Regression Analysis

Ingrid C. Wurpts, Milica Miočević, David P. MacKinnon

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Science is an inherently cumulative process, and knowledge on a specific topic is organized through synthesis of findings from related studies. Meta-analysis has been the most common statistical method for synthesizing findings from multiple studies in prevention science and other fields. In recent years, Bayesian statistics have been put forth as another way to synthesize findings and have been praised for providing a natural framework for update existing knowledge with new data. This article presents a Bayesian method for cumulative science and describes a SAS macro %SBDS for synthesizing findings from multiple studies or multiple data sets from a single study using three different methods: meta-analysis using raw data, sequential Bayesian data synthesis, and a single-level analysis on pooled data. Sequential Bayesian data synthesis and Bayesian statistics in general are discussed in an accessible manner, and guidelines are provided on how researchers can use the accompanying SAS macro for synthesizing data from their own studies. Four alcohol use studies were used to demonstrate how to apply the three data synthesis methods using the SAS macro.

Original languageEnglish (US)
Pages (from-to)378-389
Number of pages12
JournalPrevention Science
Issue number3
StatePublished - Apr 2022


  • Bayesian statistics
  • Data synthesis
  • Meta-analysis
  • Sequential bayesian data synthesis

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health


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