| Summary: | The use of Bayesian statistical approaches has gained broader acceptance within the clinical trial community. The impact of these methods on CMS decisional contexts and policy-level decisionmaking however was uncertain. Our analyses explore the main proclaimed advantages of Bayesian statistics (namely, the use of prior information, sample size determination, borrowing strength from different trials, and sequential monitoring of trials) and provide examples of decisionmaking situations where the findings reached using these approaches both agree with and differ from findings reached using frequentist statistical techniques. Our findings confirm that, like classical techniques, Bayesian approaches are affected by the problems of model specification (i.e., the relationship between various factors - patient, provider, intervention, and other contextual features - and the outcome of interest). In addition, Bayesian approaches can be substantially affected by the "Bayesian prior" - the representation of beliefs about the quantity of interest (e.g., relative risk of events when a new device is used vs. a conventional device). Thus, when considering using or interpreting Bayesian analyses, the focus of attention and thoughtful ex ante agreement are the specification of the model and specification of the Bayesian prior. The case study of the use of ICD therapy in the prevention of sudden cardiac death demonstrates the application of these techniques and highlights some of the practical challenges. The use of Bayesian statistical approaches, and incorporation of our findings concerning their strengths and limitations into the CMS decisionmaking process will enable policymakers to harness the power of the available sources of clinical evidence, explore subgroup effects within a trial and across trials in a methodologically rigorous manner, assess the uncertainty in clinical trial findings, and - ideally - improve health outcomes for Medicare beneficiaries
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