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When making therapeutic decisions for an individual patient or formulating treatment guidelines on a population level, it is often necessary to utilize information arising from different study designs, settings, or treatments. In clinical practice, heterogeneous information is frequently synthesized qualitatively, whereas in comparative effectiveness research and guideline development, it is imperative that heterogeneous data are integrated quantitatively and in a manner that accurately captures the true uncertainty in the results. Bayesian hierarchical modeling is a technique that utilizes all available information from multiple sources and naturally yields a revised estimate of the treatment effect associated with each source. A hierarchical model consists of multiple levels (ie, a hierarchy) of probability distributions that represent relationships between information arising within single populations or trials, as well as relationships between information arising from different populations or trials. We describe the structure of Bayesian hierarchical models and discuss their advantages over simpler models when multiple information sources are relevant. Two examples are presented that illustrate this technique: a meta-analysis of immunosuppressive therapy in idiopathic dilated cardiomyopathy and a subgroup analysis of the National Institute of Neurological Disorders and Stroke Intravenous Tissue Plasminogen Activator Stroke Trial.

Bayesian Hierarchical Modeling and the Integration of Heterogeneous Information on the Effectiveness of Cardiovascular Therapies