Extention of Bagging MARS with Group LASSO for Heterogeneous Treatment Effect Estimation
结合 Group LASSO 扩展 Bagging MARS 的异质性处理效应估计AI 解读 ↓
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结合 Group LASSO 扩展 Bagging MARS 的异质性处理效应估计
- 研究问题
- 如何改进基于集成因果 MARS 的异质性处理效应估计，在临床数据分析中降低误差，并增强模型解释性以支持个体化治疗研究？
- 方法与思路
- 作者提出收缩型因果集成 MARS，先利用变换结果估计基函数，再以 Group LASSO 优化模型和估计参数，并开展模拟及实际数据应用。
- 材料陈述的贡献
- 摘要称，新方法在多数模拟设置中具有较低均方误差和偏差，并在 ACTG 175 数据上展示可用性；更好的模型解释性也是其设计目标。
- 阅读关注点
- 应核对变换结果的定义、因果识别所需条件、基函数与分组惩罚构造，以及模拟覆盖范围和解释性是否有明确评价标准。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实。摘要未说明识别假设、误差数值或解释性证据，多数模拟占优不意味着普遍优越。
原文摘要
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.