Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Abstract

In precision medicine, subgroup identification is crucial for designing personalized treatments. This research focuses on subgroup identification in longitudinal clinical trials by integrating the Interaction Tree (ITree) with the Mixed Model for Repeated Measures (MMRM). Our ITree-MMRM approach retains the flexibility of tree-based methods in capturing nonlinear treatment interactions for heterogeneous treatment effects, while adhering to Food and Drug Administration guidelines for assessing treatment effects at the conclusion of longitudinal studies using MMRM. Additionally, we explore various options for tuning parameters and employ bootstrap methods to prune trees, reducing the risk of overoptimism. We demonstrate that our method outperforms existing subgroup identification techniques in simulations. The ITree-MMRM model is applied to an Alzheimer's disease clinical trial to identify subgroups with long-term treatment responses.