AI 中文总结
本研究利用贝叶斯加性回归树，基于观测数据评估治疗获益预测因子，通过模拟和COPD案例验证方法有效性，发现所构建的TBP预测不校准。
AI 中文摘要
治疗获益预测因子（TBP）是一种将患者特征映射到其从给定治疗中获得的假定获益的算法，可用于指导治疗决策。然而，在将TBP用于患者护理之前，必须在其目标人群中进行评估。当仅有观测数据可用时，由于此类设置中的治疗分配并非随机，评估TBP需要标准的因果识别假设。我们利用贝叶斯加性回归树（BART）的优势，获得预测性能指标的后验分布，以使用观测数据评估预先指定的TBP。我们使用选定的指标和图形可视化来说明TBP的评估：获益集中度（$C_b$）指数和中等校准曲线。对二元和连续结局设置的模拟研究，包括二元设置中平衡和不平衡的治疗分配，确立了所提出方法的有效性。在一项案例研究中，我们使用该方法评估针对慢性阻塞性肺疾病（COPD）患者的全身性抗生素治疗的TBP。我们表明，所构建的TBP实际上并未做出校准的预测，因为它既对风险做出了乐观的预测，又夸大了治疗带来的风险降低。我们得出结论，灵活的贝叶斯方法具有评估TBP的潜力，为灵活的模型规格、混杂因素调整和不确定性表征提供了机会。
英文摘要
A treatment benefit predictor (TBP) is an algorithm that maps a patient's characteristics to their putative benefit from a given treatment, which can be used to inform treatment decisions. However, a TBP must be evaluated in the target population before being adopted for patient care. When only observational data are available, evaluating TBPs requires standard causal identification assumptions, as treatment assignment in such settings is not random. We obtain the posterior distributions of predictive performance measures to evaluate prespecified TBPs using observational data, by taking advantage of Bayesian additive regression trees (BART). We illustrate the evaluation of TBPs using selected measures and graphical visualizations: the concentration of benefit ($C_b$) index and the moderate calibration curve. Simulation studies of binary and continuous outcomes settings, including balanced and imbalanced treatment allocation in the binary setting, establish the validity of the proposed approach. In a case study, we use this approach to assess a TBP for systemic antibiotic therapy for patients with chronic obstructive pulmonary disease (COPD). We show that the constructed TBP does not in fact make calibrated predictions, because it both makes optimistic predictions of risks and exaggerates the risk reduction due to treatment. We conclude that flexible Bayesian approaches have the potential to assess TBPs, offering opportunities for flexible model specifications, adjustment for confounding, and uncertainty characterization.
Comments44 pages, 12 figures
