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：我们提出一种贝叶斯模型选择方法，使医疗从业者能够在考虑各变量成本的前提下筛选预测变量。医疗程序几乎总会在时间和/或金钱上产生成本，这些成本可能超过其对建模结果变量的实用性。我们开发的贝叶斯模型选择方法利用灵活的模型先验，在贝叶斯框架下对高成本预测变量进行先验惩罚，从而选择出相对于成本具有实用价值的预测变量子集。该方法：(i)赋予从业者控制成本惩罚强度的能力，(ii)确保先验分布能随样本量良好扩展，且(iii)支持构建我们提出的包含路径可视化方案，该方案可综合使用概率化与可视化工具对单个候选预测变量进行决策。我们通过克利夫兰诊所基金会的心脏病诊断数据集（该数据集记录了患者多个具有不同成本的候选预测变量）以及模拟数据，验证了包含路径方案的有效性及调整先验成本惩罚强度的重要性。