Estimating optimal dynamic treatment regimes with Gaussian process emulation
Bibliographic record
Abstract
Identifying dynamic treatment regimes (DTRs) is a key objective in precision medicine. Value search approaches, including (Bayesian) dynamic marginal structural models offer an attractive approach to estimation by mapping candidate regimes to their expected outcome. As parametric models for the expected outcomes may be mis-specified and lead to incorrect conclusions, a grid search over candidate DTRs has been proposed, but this may be computationally prohibitive and also subject to high uncertainty in the estimated value function. These inferential challenges can be addressed using Gaussian process ($\mathcal {GP}$) optimization methods with estimators for the causal effect of adherence to a specified DTR. We demonstrate how to identify optimal DTRs using this approach in a variety of settings, including when the value function is multi-modal and show that the $\mathcal {GP}$ modeling approach that recognizes noise in the estimated response surface leads to improved results as compared to a grid search approach. Further, we show that a grid search may not yield a robust solution and that it often utilizes information less efficiently than a $\mathcal {GP}$ approach. The proposed approach is used to understand tailoring of HIV therapy to optimize CD4 cell counts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".