AI 中文总结
本研究提出贝叶斯中介框架，将个体化治疗规则的价值对比分解为直接与间接成分，采用贝叶斯因果中介森林估计，经模拟与TRIUMPH试验数据验证，可实现ITR的机制评估与解释。
AI 中文摘要
个体化治疗规则（ITR）的价值被定义为根据该规则分配治疗时的预期结局，可用于评估平均临床获益，但无法解释该规则的获益是如何产生的。我们提出一种因果中介框架，用于将预先指定的候选ITR与临床有意义的参考规则之间的价值对比分解为直接和间接成分。利用规则特异性嵌套潜在结局，我们定义了自然直接和间接规则效应，以量化价值的改善是通过直接作用于结局的通路，还是通过指定的中介变量产生的。我们给出了识别条件，在此条件下，这些成分可通过规则水平中介g公式识别。在估计方面，我们采用贝叶斯因果中介森林，以获得价值对比及其通路特异性成分的后验推断。我们的模拟结果表明，在具有不同直接和中介贡献的场景中，所提出的估计量实现了接近名义水平的可信区间覆盖率，且随着样本量的增加，偏差和均方根误差逐渐减小。我们进一步使用TRIUMPH试验的数据说明该方法，通过候选神经血管、心肺和行为中介变量分解生活方式干预规则的认知获益。所提出的框架通过中介分析为最优ITR学习补充了解释，为ITR的机制评估提供了一种自然方法。
英文摘要
The value of an individualized treatment rule (ITR), defined as the expected outcome under treatment assignment according to the rule, is useful for assessing average clinical benefit but does not explain how the benefit of a rule is generated. We propose a causal mediation framework for decomposing the value contrast between a prespecified candidate ITR and a clinically meaningful reference rule into direct and indirect components. Using rule-specific nested potential outcomes, we define natural direct and indirect rule effects that quantify the extent to which the improvement in value arises through pathways operating directly on the outcome versus through a specified mediator. We give identification conditions under which these components are identified by a rule-level mediation g-formula. For estimation, we adapt Bayesian causal mediation forests to obtain posterior inference for the value contrast and its path-specific components. Our simulations demonstrate that the proposed estimator achieved near-nominal credible interval coverage with decreasing bias and root mean squared error as sample size increased in settings with varying direct and mediated contributions. We further illustrate the method using data from the TRIUMPH trial, decomposing the cognitive benefit of a lifestyle intervention rule through candidate neurovascular, cardiorespiratory, and behavioral mediators. The proposed framework complements optimal ITR learning with explanation using mediation, providing a natural approach for mechanistic evaluation of ITRs.
Comments21 pages, 3 figures, 2 tables
