Abstract A002: A Bayesian Machine Learning Approach for Estimating Treatment Effects in Decentralized Clinical Trials
Bibliographic record
Abstract
Abstract A decentralized clinical trial (DCT) is a type of clinical trial where some or all trial-related activities occur at locations other than traditional clinical trial sites (e.g., at home). DCTs offer great flexibility and convenience for participants, potentially improving recruitment and retention rates. However, they are more susceptible to bias and measurement errors compared to conventional randomized clinical trials due to the off-site measurement of endpoints. This article introduces a Bayesian additive regression trees approach to estimate the conditional average treatment effect (cATE) in hybrid longitudinal DCTs, where endpoints are measured off-site at some time points and on-site at other time points. Extensive simulation studies demonstrate that our proposed method is not only robust but also more efficient than conventional approaches. We illustrate our approach using a dementia clinical trial. Citation Format: Ying Yuan. A Bayesian Machine Learning Approach for Estimating Treatment Effects in Decentralized Clinical Trials [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A002.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.169 | 0.781 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".