Synthesis of evidence for reimbursement decisions: a Bayesian reanalysis

The aim of this study was to compare Bayesian methods with the standard methods that are used for evidence-based policy making.

We performed a Bayesian reanalysis of the data underlying a reimbursement advice by the Dutch National Health Insurance Board (CVZ) regarding the anti-diabetic drug exenatide (an alternative to insulin). We synthesized evidence from various sources that was available when the CVZ advice was drafted: expert opinion (as elicited from internists), experimental data (from direct comparison studies), and observational data. Subsequently, the original frequentist results and the results from the Bayesian reanalysis were compared in terms of outcomes and interpretations. These results were presented in a meeting with staff from CVZ, whose opinions about the usefulness of a Bayesian approach were assessed using a questionnaire.

The Bayesian approach yields outcomes that summarize different pieces of evidence, which would have been difficult to obtain otherwise. Moreover, there are conceptual differences, and the Bayesian approach allows for determining probabilities of clinically relevant differences. The staff at CVZ were fairly positive with respect to the use of Bayesian methods, although practical barriers were also seen as important.

The Bayesian outcomes are different and could be more suited to the informational needs of policy makers. The response from staff at CVZ provides some support for this statement, but more research at the interface of science and policy is needed.