We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always incur costs in time and/or money. These costs might exceed their usefulness for modeling the outcome of interest. We develop Bayesian model selection that uses flexible model priors to penalize costly predictors a priori and select a subset of predictors useful relative to their costs. Our approach (i) gives the practitioner control over the magnitude of cost penalization, (ii) enables the prior to scale well with sample size, and (iii) enables the creation of our proposed inclusion path visualization, which can be used to make decisions about individual candidate predictors using both probabilistic and visual tools. We demonstrate the effectiveness of our inclusion path approach and the importance of being able to adjust the magnitude of the prior's cost penalization through a dataset pertaining to heart disease diagnosis in patients at the Cleveland Clinic Foundation, where several candidate predictors with various costs were recorded for patients, and through simulated data.

翻译：我们提出了一种贝叶斯模型选择方法，使医疗从业者能够在考虑各自成本的前提下选择预测变量。医疗程序几乎总会产生时间和/或金钱成本，这些成本可能超过其对建模结果变量的实用价值。我们开发的贝叶斯模型选择方法采用灵活模型先验，通过先验惩罚高成本预测变量，并选择一组与成本相关的有用预测变量子集。该方法具备以下特性：(i) 允许从业者控制成本惩罚的强度；(ii) 使先验能够随样本量合理缩放；(iii) 可生成我们提出的包含路径可视化图，该图结合概率工具与可视化工具辅助对单个候选预测变量的决策。我们通过克利夫兰医学中心的心脏病诊断患者数据集（记录了患者多项不同成本的候选预测变量）及模拟数据，验证了包含路径方法的有效性以及调整先验成本惩罚强度的重要性。