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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诊断测试与人工智能医疗器械的因果评估：效应修饰与信息增强决策
- 研究问题
- 诊断信息通常通过后续治疗决策影响健康，如何区分其解释治疗效应异质性的价值，与纳入个体化决策后改善预期结果的价值？
- 方法与思路
- 作者用基于方差的治疗效应变量重要性刻画解释有效性，以有无诊断信息的最优规则结果差定义实用有效性，并提出 TMLE 推断。
- 材料陈述的贡献
- 摘要称，建立估计目标的识别及交叉验证 TMLE 方法，通过模拟和合成结直肠癌案例区分信息扩充与决策规则优化两条路径。
- 阅读关注点
- 应核对两类有效性的估计目标、诊断信息及治疗的识别条件、最优规则估计的推断处理，以及器械成本和潜在直接影响是否纳入。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未完整列出识别条件，合成案例也不能证明具体人工智能器械改善临床结局。
原文摘要
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.