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We propose a Bayesian model selection approach that allows medicalpractitioners to select among predictor variables while taking their respectivecosts into account. Medical procedures almost always incur costs in time and/ormoney. These costs might exceed their usefulness for modeling the outcome ofinterest. We develop Bayesian model selection that uses flexible model priorsto penalize costly predictors a priori and select a subset of predictors usefulrelative to their costs. Our approach (i) gives the practitioner control overthe magnitude of cost penalization, (ii) enables the prior to scale well withsample size, and (iii) enables the creation of our proposed inclusion pathvisualization, which can be used to make decisions about individual candidatepredictors using both probabilistic and visual tools. We demonstrate theeffectiveness of our inclusion path approach and the importance of being ableto adjust the magnitude of the prior's cost penalization through a datasetpertaining to heart disease diagnosis in patients at the Cleveland ClinicFoundation, where several candidate predictors with various costs were recordedfor patients, and through simulated data.

Flexible cost-penalized Bayesian model selection: developing inclusion paths with an application to diagnosis of heart disease