Diagnostic evaluation with simulated probabilities.

As policy makers require more rigorous assessments for the strength of evidence in Theory-Based evaluations, Bayesian logic is attracting increasing interest; however, the estimation of probabilities that this logic (almost) inevitably requires presents challenges. Probabilities can be estimated on the basis of empirical frequencies, but such data are often unavailable for most mechanisms that are objects of evaluation. Subjective probability elicitation techniques are well established in other fields and potentially applicable, but they present potential challenges and might not always be feasible. We introduce the community to a third way: simulated probabilities. We provide proof of concept that simulation can be used to estimate probabilities in diagnostic evaluation and illustrate our case with an application to health policy. [ABSTRACT FROM AUTHOR]