Optimal dynamic treatment regimes with survival endpoints: introducing <tt>DWSurv</tt> in the <tt>R</tt> package <tt>DTRreg</tt>
Bibliographic record
Abstract
Precision medicine is an approach to health care in which treatment decisions are tailored to patient-level information. Statistical methods for the estimation of dynamic treatment regimes (DTRs) allow to uncover a sequence of personalized treatment rules for patients with chronic diseases. Of particular interest is the identification of an optimal DTR, that is, the sequence of treatment rules that yields the best expected outcome. This is a challenging task, especially when the outcome is a survival time subject to right censoring or when available data are from observational studies. Dynamic weighted survival modelling (DWSurv) has been demonstrated to be theoretically robust and is accessible to users. We describe its implementation using the DWSurv function in the R package DTRreg. We review on the theory underlying DWSurv and demonstrate its use with hypothetical, and real-life inspired, simulated data sets.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".