Reporting of master protocols towards a standardized approach: A systematic review
Bibliographic record
Abstract
BACKGROUND: In September 2018 the FDA provided a draft guidance on master protocols reflecting an increased interest in these designs by industry. Master protocols refer to a single overarching protocol developed to evaluate multiple hypotheses and may be further categorized as basket, umbrella, and platform trials. However, inconsistencies in reporting persist in the literature. We conducted a systematic review to describe master protocol reporting with the goal of facilitating the further development and spread of these innovative trial designs. METHODS: We searched MEDLINE, EMBASE, and CENTRAL from inception to April 25, 2019 for English articles on master protocols. This was supplemented by hand searches of trial registries and of the bibliographies of published reviews. We used the FDA's definitions of master protocols as references and compared them to self-reported master protocols. RESULTS: We identified 278 master protocol publications, consisting of 228 protocols and 50 reviews. Sixty-six records provided unique definitions of master protocol types. We observed considerable heterogeneity in definitions of master protocols, and over half (54%) used oncology-specific language. The majority of self-classified master protocols (57%) were consistent with the FDA's definitions of master protocols. CONCLUSION: The terms 'master protocol', 'basket trial', 'umbrella trial', and 'platform trial' are inconsistently described. Careful treatment of these terms and adherence to the definitions set forth by the FDA will facilitate better understanding of these trial designs and allow them to be used broadly and to their full potential in clinical research. We encourage trial methodologists to use these trial designations when applicable.
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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 | 0.558 | 0.781 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".