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：本文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预测变量相应成本的前提下进行变量选择。医疗程序几乎总会产生时间成本和/或经济成本，这些成本可能超过其对建模目标结局的有用性。我们开发的贝叶斯模型选择方法采用灵活的模型先验，对高成本预测变量进行先验惩罚，从而筛选出相对于成本具有实用价值的预测变量子集。该方法具有以下特点：(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良好扩展；(iii) 可生成我们提出的包含路径可视化图，该图能够综合运用概率工具和可视化工具对个体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患者数据集（记录了患者多个不同成本的候选预测变量）以及模拟数据，验证了包含路径方法的有效性，并论证了调整成本惩罚先验强度的重要性。