Rule ensemble method with adaptive group lasso for heterogeneous treatment effect estimation
Rule ensemble method with adaptive group lasso for heterogeneous treatment effect estimation
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DOI：
10.1002/sim.9812
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发表时间：
2023-06
影响因子：
2
通讯作者：
Ke Wan;Kensuke Tanioka;Toshio Shimokawa
中科院分区：
文献类型：
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作者：
Ke Wan;Kensuke Tanioka;Toshio Shimokawa
基于真实的世界数据的精准医学越来越受到科学界的关注，这使得最近的许多研究澄清了治疗效果与患者特征之间的关系。然而，这是具有挑战性的，因为个体治疗效果的普遍异质性以及其背景的真实的世界数据复杂且嘈杂。由于其灵活性，各种机器学习（ML）方法已被提出用于估计异质性治疗效果（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和治疗效果之间关系的直接解释。本文提出了一种基于规则集成方法RuleFit的最大似然估计方法。RuleFit的主要优点是可解释性和准确性。然而，HTE始终在潜在结果框架中定义，并且无法直接应用RuleFit。因此，我们修改了RuleFit，并提出了一种方法来估计HTE，直接从模型中解释个体特征之间的关系。来自HIV研究的实际数据，ACTG 175数据集，被用来说明基于所提出的方法创建的规则的集合的解释。数值计算结果表明，与已有方法相比，该方法具有较高的预测精度，表明该方法建立了一个具有足够预测精度的可解释模型。
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.