Extension of Bootstrap MARS With Group LASSO for Heterogeneous Treatment Effect Estimation

In recent years, clinical data obtained from patient surveys and medical records have become increasingly pivotal in medical data science. These clinical data, collectively referred to as "real-world data (RWD)," are anticipated to play a key role in observational studies of specific diseases and in advancing personalized or precision medicine by identifying effective treatments for particular patient subgroups. Consequently, the estimation of heterogeneous treatment effects (HTEs) using RWD has garnered substantial attention. HTE estimation meaningfully contributes to precision medicine by enabling clinicians to make informed treatment decisions tailored to individual patient characteristics. Various treatment effect models for observational studies highlight the robust performance of bagging causal multivariate adaptive regression splines (MARS) (BCM). However, despite the notable efficacy of BCM, there remains potential for refinement. Here, we introduce a novel treatment effect model, the shrinkage causal bootstrap MARS method, which builds upon the following framework: initially, basis functions are estimated using transformed outcome bootstrap sampling MARS, followed by optimization of the model and parameter estimation via the group least absolute shrinkage and selection operator (LASSO) method. Our simulations demonstrate that the proposed method achieves improved mean square error and bias across most scenarios. Additionally, we validate the practical applicability of the method by implementing it on the ACTG 175 dataset.