Adaptations and Heterogeneity of Treatment Effects in Platform Trials—Protocol for Two Methodological Studies
Bibliographic record
Abstract
BACKGROUND: Adaptive platform trials bring opportunities for improved infrastructure and effective advancement in medical care but are methodologically complex. Assessment of heterogeneity of treatment effects (HTE) according to participant characteristics and adaptations, including adaptive stopping, are important methodological features in these trials, which may be approached in multiple ways. We aim to characterise the assessment of HTE and use of adaptations, including their key methodological features, in adaptive platform trials. METHODS: This protocol outlines two methodological studies, which will be based on a common, systematic literature search and data extraction. We will include adaptive platform trials conducted from 2005 onwards. Screening and data extraction will be performed independently and in duplicate. In Study I, we will assess methods used to evaluate HTE, and in Study II, we will assess adaptations and stopping rules in the included trials. DISCUSSION: The two proposed methodological studies will provide an overview of important methodological features regarding the assessment of HTE and adaptations used in adaptive platform trials. Better knowledge of available methods to assess these features can improve the conditions for designing adaptive platform trials and identify areas for further development.
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 | 0.220 | 0.390 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".