Bayesian evidence synthesis for exploring generalizability of treatment effects: a case study of combining randomized and non-randomized results in XXXX .

@entity1 @entity23 @entity6
UNASSIGNED: In this paper, we present a unified modeling framework to combine aggregated data from @entity23 ( @entity23 ) with individual @entity1 data (IPD) from observational studies. Rather than simply pooling the available evidence into an overall treatment effect, adjusted for potential confounding, the intention of this work is to explore treatment effects in specific @entity1 populations reflected by the IPD. In this way, by collecting IPD, we can potentially gain new insights from @entity23 ' results, which cannot be seen using only a meta-analysis of @entity23 . We present a new Bayesian hierarchical meta-regression model, which combines submodels, representing different types of data into a coherent analysis. Predictors of baseline risk are estimated from the individual data. Simultaneously, a bivariate random effects distribution of baseline risk and treatment effects is estimated from the combined individual and aggregate data. Therefore, given a subgroup of interest, the estimated treatment effect can be calculated through its correlation with baseline risk. We highlight different types of model parameters: those that are the focus of inference (e.g., treatment effect in a subgroup of @entity1 ) and those that are used to adjust for biases introduced by data collection processes (e.g., internal or external validity). The model is applied to a case study where @entity23 ' results, investigating efficacy in the treatment of @entity6 , are extrapolated to groups of @entity1 treated in medical routine and who were enrolled in a prospective cohort study. Copyright    2015 John Wiley _ Sons, Ltd.
