Methods in dynamic treatment regimens using observational healthcare data: A systematic review
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Bibliographic record
Abstract
• This systematic review identified 83 observational studies estimating DTRs • Methods mainly used reinforcement learning (44%), counterfactual (18%), and g-methods (10%) • Most studies focused on refining existing DTRs • 65% included key assumptions (e.g., exchangeability, positivity) • Validation was high (86%), mainly using expected outcomes, simulated data, or observational comparisons We present a systematic review of methods used to estimate Dynamic Treatment Regimens (DTR) using observational healthcare data and provide a brief summary of their strengths and weaknesses, evaluation metrics, and suitable research problem settings. We considered all observational studies identified in PubMed or EMBASE between January 1950 until January 2022, including only studies that evaluated medical treatments or interventions as exposure and/or outcome in patients and where DTRs were estimated. 83 studies met our inclusion criteria; 44.6% estimating DTR utilizing reinforcement learning, 18.1% utilizing counterfactual-based models, 12.1% utilizing classification-based methods, and 9.6% utilized g-methods. Among the studies analyzed, 28.9% aimed to replicate human expert DTRs, while 71.1% aimed to refine and improve existing DTRs. Approximately two-thirds of studies (65.1%) reported the assumptions required for their applied methods, such as exchangeability, positivity, consistency, and Markov property. Most of the studies (83.1%) estimated DTRs with more than two treatment options; 50.6% mentioned time-varying confounders, only a few estimated conditional average treatment effects (7.2%). Most (85.5%) validated their methods, with 32.5% using expected outcomes (e.g., survival rates), 26.5% employing simulated data, and 25.3% conducting direct comparisons with observational data.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it