Switching Therapy in Health Economics Trials: Confronting the Confusion
Bibliographic record
Abstract
Should patients in a randomized, pragmatic health economics trial be allowed to switch therapy in mid-trial to that provided in the other arm? Specifically, should patients in the treatment arm (T) be allowed to switch to the therapy of the comparator arm (C) if they need a change of therapy--that is, should TC switches be allowed? Also, should patients in the comparator arm be allowed to switch to the therapy of the treatment arm if they need changes of therapy--should CT switches be allowed? This is a nontrivial issue in study design that has been debated in the clinical trials literature and is currently being handled inconsistently in the health economics literature. In this article, the authors argue that TC switches should always be allowed and that CT switches should be allowed or not depending on the economic question. They further argue that the most common economic question is one that would lead to CT switches not being allowed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.739 | 0.781 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.045 | 0.051 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".