Precision medicine tailors treatments to individual patient characteristics, which is especially valuable for conditions like obstructive sleep apnea (OSA), where treatment responses vary widely. Traditional trials often overlook subgroup differences, leading to suboptimal recommendations. Current approaches rely on pre-specified thresholds with inherent uncertainty, assuming these thresholds are correct-a flawed assumption. This case study compares pre-specified thresholds to two advanced Bayesian methods: the established FK-BMA method and its novel variant, FK. The FK approach retains the flexibility of free-knot splines but omits variable selection, providing stable, interpretable models. Using biomarker data from large studies, this design identifies subgroups dynamically, allowing early trial termination or enrollment adjustments. Simulations in this specific context show FK improves precision, efficiency, and subgroup detection, offering practical benefits over FK-BMA and advancing precision medicine for OSA.
@article{arxiv.2503.15455,
title = {A Precision Trial Case Study for Heterogeneous Treatment Effects in Obstructive Sleep Apnea},
author = {Lara Maleyeff and Shirin Golchi and Erica E. M. Moodie and R. John Kimoff},
journal= {arXiv preprint arXiv:2503.15455},
year = {2025}
}