Efficient Subgroup Analysis via Optimal Trees with Global Parameter Fusion
This work addresses limitations in subgroup analysis for clinical research, offering a more accurate method for identifying differential treatment effects, though it is incremental as it builds on existing tree-based approaches.
The paper tackled the problem of suboptimal and overfitting subgroup analysis in precision health by proposing a fused optimal causal tree method using mixed integer optimization, which improved subgroup discovery accuracy and statistical efficiency, as demonstrated in simulations and a case study on the HABS-HD dataset.
Identifying and making statistical inferences on differential treatment effects (commonly known as subgroup analysis in clinical research) is central to precision health. Subgroup analysis allows practitioners to pinpoint populations for whom a treatment is especially beneficial or protective, thereby advancing targeted interventions. Tree based recursive partitioning methods are widely used for subgroup analysis due to their interpretability. Nevertheless, these approaches encounter significant limitations, including suboptimal partitions induced by greedy heuristics and overfitting from locally estimated splits, especially under limited sample sizes. To address these limitations, we propose a fused optimal causal tree method that leverages mixed integer optimization (MIO) to facilitate precise subgroup identification. Our approach ensures globally optimal partitions and introduces a parameter fusion constraint to facilitate information sharing across related subgroups. This design substantially improves subgroup discovery accuracy and enhances statistical efficiency. We provide theoretical guarantees by rigorously establishing out of sample risk bounds and comparing them with those of classical tree based methods. Empirically, our method consistently outperforms popular baselines in simulations. Finally, we demonstrate its practical utility through a case study on the Health and Aging Brain Study Health Disparities (HABS-HD) dataset, where our approach yields clinically meaningful insights.