A precision trial case study for heterogeneous treatment effects in obstructive sleep apnea
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.167 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".