Department of Medical Data Science, Wakayama Medical University, Wakayama, Japan.
Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan.
Stat Med. 2023 Aug 30;42(19):3413-3442. doi: 10.1002/sim.9812. Epub 2023 Jun 7.
The increasing scientific attention given to precision medicine based on real-world data has led to many recent studies clarifying the relationships between treatment effects and patient characteristics. However, this is challenging because of ubiquitous heterogeneity in the treatment effect for individuals and the real-world data on their backgrounds being complex and noisy. Because of their flexibility, various machine learning (ML) methods have been proposed for estimating heterogeneous treatment effect (HTE). However, most ML methods incorporate black-box models that hamper direct interpretation of the relationships between an individual's characteristics and treatment effects. This study proposes an ML method for estimating HTE based on the rule ensemble method RuleFit. The main advantages of RuleFit are interpretability and accuracy. However, HTEs are always defined in the potential outcome framework, and RuleFit cannot be applied directly. Thus, we modified RuleFit and proposed a method to estimate HTEs that directly interpret the relationships among the individuals' features from the model. Actual data from an HIV study, the ACTG 175 dataset, was used to illustrate the interpretation based on the ensemble of rules created by the proposed method. The numerical results confirm that the proposed method has high prediction accuracy compared to previous methods, indicating that the proposed method establishes an interpretable model with sufficient prediction accuracy.
基于真实世界数据的精准医学日益受到科学界的关注，这促使许多最近的研究阐明了治疗效果与患者特征之间的关系。然而，由于个体治疗效果普遍存在异质性，以及关于他们背景的真实世界数据复杂且嘈杂，这具有挑战性。由于其灵活性，已经提出了各种机器学习 (ML) 方法来估计异质治疗效果 (HTE)。然而，大多数 ML 方法都包含黑盒模型，这阻碍了对个体特征与治疗效果之间关系的直接解释。本研究提出了一种基于规则集成方法 RuleFit 的估计 HTE 的 ML 方法。RuleFit 的主要优点是可解释性和准确性。然而，HTE 总是在潜在结果框架中定义的，而 RuleFit 不能直接应用。因此，我们修改了 RuleFit，并提出了一种从模型中直接解释个体特征之间关系的方法来估计 HTE。来自 HIV 研究（ACTG 175 数据集）的实际数据用于说明基于所提出方法创建的规则集合的解释。数值结果证实，与以前的方法相比，所提出的方法具有较高的预测准确性，这表明所提出的方法建立了一个具有足够预测准确性的可解释模型。
