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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. [ABSTRACT FROM AUTHOR]

This paper presents the first findings of an integrative data analysis of individual-level data from 19 adolescent depression prevention trials (n = 5210) involving nine distinct interventions across 2 years post-randomization. In separate papers, several interventions have been found to decrease the risk of depressive disorders or elevated depressive/internalizing symptoms among youth. One type of intervention specifically targets youth without a depressive disorder who are at risk due to elevated depressive symptoms and/or having a parent with a depressive disorder. A second type of intervention targets two broad domains: prevention of problem behaviors, which we define as drug use/abuse, sexual risk behaviors, conduct disorder, or other externalizing problems, and general mental health. Most of these latter interventions improve parenting or family factors. We examined the shared and unique effects of these interventions by level of baseline youth depressive symptoms, sociodemographic characteristics of the youth (age, sex, parent education, and family income), type of intervention, and mode of intervention delivery to the youth, parent(s), or both. We harmonized eight different measures of depression utilized across these trials and used growth models to evaluate intervention impact over 2 years. We found a significant overall effect of these interventions on reducing depressive symptoms over 2 years and a stronger impact among those interventions that targeted depression specifically rather than problem behaviors or general mental health, especially when baseline symptoms were high. Implications for improving population-level impact are discussed. [ABSTRACT FROM AUTHOR]

Individual participant data (IPD) meta-analysis is a meta-analysis in which the individual-level data for each study are obtained and used for synthesis. A common challenge in IPD meta-analysis is when variables of interest are measured differently in different studies. The term harmonization has been coined to describe the procedure of placing variables on the same scale in order to permit pooling of data from a large number of studies. Using data from an IPD meta-analysis of 19 adolescent depression trials, we describe a multiple imputation approach for harmonizing 10 depression measures across the 19 trials by treating those depression measures that were not used in a study as missing data. We then apply diagnostics to address the fit of our imputation model. Even after reducing the scale of our application, we were still unable to produce accurate imputations of the missing values. We describe those features of the data that made it difficult to harmonize the depression measures and provide some guidelines for using multiple imputation for harmonization in IPD meta-analysis. [ABSTRACT FROM AUTHOR]