Can Bayesian methods make data and analyses more relevant to decision makers? A perspective from Medicare.
Decision making in health care has become increasingly reliant on information technology, evidence-based processes, and performance measurement. It is therefore a time at which it is of critical importance to make data and analyses more relevant to decision makers. Those who support Bayesian approaches contend that their analyses provide more relevant information for decision making than do classical or "frequentist" methods, and that a paradigm shift to the former is long overdue. While formal Bayesian analyses may eventually play an important role in decision making, there are several obstacles to overcome if these methods are to gain acceptance in an environment dominated by frequentist approaches. Supporters of Bayesian statistics must find more accommodating approaches to making their case, especially in finding ways to make these methods more transparent and accessible. Moreover, they must better understand the decision-making environment they hope to influence. This paper discusses these issues and provides some suggestions for overcoming some of these barriers to greater acceptance.
Sheingold
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D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D003657:Decision Making; D006291:Health Policy / Q000191:economics; D006801:Humans; D007349:Insurance, Health, Reimbursement; D006278:Medicare; D014481:United States