Work

A precision trial case study for heterogeneous treatment effects in obstructive sleep apnea

Year: 2025

Type: article

Abstract: Precision medicine tailors treatments to individual patient characteristics, which is especially valuable for obstructive sleep apnea (OSA), where treatment responses vary widely. Traditional trials often overlook subgroup differences, leading to suboptimal recommendations. Current approaches rely on prespecified thresholds, which may be incorrectly specified. This case study compares prespecified thresholds to two Bayesian methods: the established FK‐BMA (free‐knot Bayesian model averaging) 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. Simulation results—motivated by real‐world biomarker distributions and clinical constraints—show that under conditions of limited signal‐to‐noise ratio and limited candidate biomarkers, FK improves efficiency and subgroup detection.

Source: Canadian Journal of Statistics

DOI: 10.1002/cjs.70028

Authors: Lara Maleyeff, Shirin Golchi, Erica E. M. Moodie, R. John Kimoff

Institutions: McGill University, McGill University Health Centre, Research Institute of the McGill University Health Centre

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,970