Department of Medicine, Wakayama Medical University, Wakayama, Japan.
Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan.
Stat Med. 2024 Nov 30;43(27):5234-5271. doi: 10.1002/sim.10180.
With an increasing focus on precision medicine in medical research, numerous studies have been conducted in recent years to clarify the relationship between treatment effects and patient characteristics. The treatment effects for patients with different characteristics are always heterogeneous, and therefore, various heterogeneous treatment effect machine learning estimation methods have been proposed owing to their flexibility and high estimation accuracy. However, most machine learning methods rely on black-box models, preventing direct interpretation of the relationship between patient characteristics and treatment effects. Moreover, most of these studies have focused on continuous or binary outcomes, although survival outcomes are also important in medical research. To address these challenges, we propose a heterogeneous treatment effect estimation method for survival data based on RuleFit, an interpretable machine learning method. Numerical simulation results confirmed that the prediction performance of the proposed method was comparable to that of existing methods. We also applied a dataset from an HIV study, the AIDS Clinical Trials Group Protocol 175 dataset, to illustrate the interpretability of the proposed method using real data. Consequently, the proposed survival causal rule ensemble method provides an interpretable model with sufficient estimation accuracy.
随着医学研究中对精准医学的日益关注，近年来已经进行了许多研究来阐明治疗效果与患者特征之间的关系。具有不同特征的患者的治疗效果总是存在异质性，因此，由于其灵活性和高精度，已经提出了各种异质治疗效果机器学习估计方法。然而，大多数机器学习方法依赖于黑盒模型，从而阻止了对患者特征与治疗效果之间关系的直接解释。此外，这些研究大多集中在连续或二分类结果上，尽管生存结果在医学研究中也很重要。为了解决这些挑战，我们提出了一种基于 RuleFit 的生存数据异质治疗效果估计方法，RuleFit 是一种可解释的机器学习方法。数值模拟结果证实，所提出方法的预测性能可与现有方法相媲美。我们还应用了来自 HIV 研究的数据集，即艾滋病临床试验组协议 175 数据集，使用真实数据来说明所提出方法的可解释性。因此，所提出的生存因果规则集成方法提供了一个具有足够估计准确性的可解释模型。
