评估诊断测试与人工智能医疗设备的因果推理方法：从效应修饰到信息增强决策
A Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-Making
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中文总结 AI 辅助
本研究提出因果推理方法，区分诊断测试的解释性与实用性有效性，采用TMLE及交叉验证TMLE进行非参数估计，可用于评估诊断测试及人工智能医疗设备的作用途径。
中文摘要 AI 辅助
诊断医学测试与设备为评估治疗的潜在获益和风险提供有用信息。然而，与治疗不同，它们对健康结局的影响通常是间接的，因为测量诊断信息本身一般不会影响患者结局，这使得评估其有效性变得复杂。本研究中，我们开发了一种用于评估诊断测试的因果推理方法，该方法区分了解释性有效性和实用性有效性。解释性有效性评估诊断测试结果是否通过解释额外的治疗效应异质性，提供了超出基线协变量的与治疗相关的信息，我们使用基于方差的治疗效应变量重要性测度来表征这一点。实用性有效性评估将该信息纳入个性化治疗决策是否能改善预期结局，这是患者、临床医生及其他医疗保健利益相关者决策的核心问题。我们将实用性有效性形式化为在有和无诊断测试结果的情况下定义的最优个性化治疗规则下预期结局的对比。我们确立了所提出估计量的可识别性，并提供了针对性最大似然估计（TMLE）和交叉验证TMLE程序，用于非参数估计和推断。模拟研究和合成结直肠癌应用示例说明了所提出的估计量和估计性能。更广泛而言，该方法通过区分人工智能设备作为信息丰富化工具和个性化决策优化工具的双重角色，为评估人工智能医疗设备提供了因果推理视角，阐明人工智能是否通过扩大下游决策的信息、改善用于利用该信息的决策规则，或通过这两种途径来改善结局。
英文摘要
Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments. However, unlike treatments, their impact on health outcomes is generally indirect because measuring diagnostic information typically does not itself affect patient outcomes, which complicates evaluation of their effectiveness. In this work, we develop a causal inference approach for evaluating diagnostic tests by distinguishing explanatory and pragmatic effectiveness. Explanatory effectiveness evaluates whether a diagnostic test result provides treatment-relevant information beyond baseline covariates by explaining additional treatment-effect heterogeneity, which we characterize using a variance-based treatment-effect variable importance measure. Pragmatic effectiveness evaluates whether incorporating this information into personalized treatment decisions improves expected outcomes, a question central to decision-making for patients, clinicians, and other health care stakeholders. We formalize pragmatic effectiveness as a contrast between expected outcomes under optimal personalized treatment rules defined with and without access to the diagnostic test result. We establish identification of the proposed estimand and provide Targeted Maximum Likelihood Estimation (TMLE) and cross-validated TMLE procedures for nonparametric estimation and inference. Simulation studies and a synthetic colorectal cancer application illustrate the proposed estimands and estimation performance. More broadly, this approach provides a causal inference perspective for evaluating AI-enabled devices by distinguishing their dual roles as information-enrichment and personalized decision-optimization tools, clarifying whether AI improves outcomes by expanding information for downstream decisions, improving the decision rule used to act on that information, or through both pathways.
