Recent years, large scale clinical data like patient surveys and medical record data are playing an increasing role in medical data science. These large-scale clinical data, collectively referred to as "real-world data (RWD)". It is expected to be widely used in large-scale observational studies of specific diseases, personal medicine or precise medicine, finding the responder of drugs or treatments. Applying RWD for estimating heterogeneous treat ment effect (HTE) has already been a trending topic. HTE has the potential to considerably impact the development of precision medicine by helping doctors make more informed precise treatment decisions and provide more personalized medical care. The statistical models used to estimate HTE is called treatment effect models. Powers et al. proposed a some treatment effect models for observational study, where they pointed out that the bagging causal MARS (BCM) performs outstanding compared to other models. While BCM has excellent performance, it still has room for improvement. In this paper, we proposed a new treatment effect model called shrinkage causal bagging MARS method to improve their shared basis conditional mean regression framework based on the following points: first, we estimated basis functions using transformed outcome, then applied the group LASSO method to optimize the model and estimate parameters. Besides, we are focusing on pursing better interpretability of model to improve the ethical acceptance. We designed simulations to verify the performance of our proposed method and our proposed method superior in mean square error and bias in most simulation settings. Also we applied it to real data set ACTG 175 to verify its usability, where our results are supported by previous studies.

翻译：近年来，患者调查和病历数据等大规模临床数据在医学数据科学中发挥着越来越重要的作用。这些大规模临床数据统称为"真实世界数据"，有望广泛应用于特定疾病的大规模观察性研究、个性化医学或精准医学，以及药物或治疗应答者的识别。应用RWD估计异质性处理效应已成为热门课题。HTE有望通过帮助医生做出更明智的精准治疗决策和提供更个性化的医疗护理，对精准医学的发展产生重大影响。用于估计HTE的统计模型称为处理效应模型。Powers等人针对观察性研究提出了一系列处理效应模型，其中指出Bagging因果MARS方法的表现优于其他模型。尽管BCM具有优异性能，但其仍有改进空间。本文基于以下要点提出了一种新的处理效应模型——收缩因果Bagging MARS方法，以改进其共享基函数条件均值回归框架：首先，利用转化结果估计基函数，然后应用Group LASSO方法优化模型并估计参数。此外，我们着重追求模型更好的可解释性以提高伦理接受度。我们通过仿真验证了所提方法的性能，结果表明在大多数仿真设置中，所提方法在均方误差和偏差方面均具有优势。同时，我们将该方法应用于真实数据集ACTG 175以验证其实用性，实验结果与先前研究结论一致。