Stakeholder perspectives on adaptive clinical trials: a scoping review
Bibliographic record
Abstract
BACKGROUND: Adaptive clinical trials (ACTs) represent an emerging approach to trial design where accumulating data are used to make decisions about future conduct. Adaptations can include comparisons of multiple dose tiers, response-adaptive randomization, sample size re-estimation, and efficacy/futility stopping rules. The objective of this scoping review is to assess stakeholder attitudes, perspectives, and understanding of adaptive trials. METHODS: We conducted a review of articles examining stakeholders encompassing the broad medical trial community's perspectives of adaptive designs (ADs). A computerized search was conducted of four electronic databases with relevant search terms. Following screening of articles, the primary findings of each included article were coded for study design, population studied, purpose, and primary implications. RESULTS: Our team retrieved 167 peer-reviewed titles in total from the database search and 5 additional titles through searching web-based search engines for gray literature. Of those 172 titles, 152 were non-duplicate citations. Of these, 119 were not given full-text reviews, as their titles and abstracts indicated that they did not meet the inclusion criteria. Thirty-three articles were carefully examined for relevance, and of those, 18 were chosen to be part of the analysis; the other 15 were excluded, as they were not relevant upon closer inspection. Perceived advantages to ADs included limiting ineffective treatments and efficiency in answering the research question; -perceived barriers included insufficient sample size for secondary outcomes, challenges of consent, potential for bias, risk of type 1 error, cost and time to adaptively design trials, unclear rationales for using Ads, and, most importantly, a lack of education regarding ADs among stakeholders within the clinical trial community. Perceptions among different types of stakeholders varied from sector to sector, with patient perspectives being noticeably absent from the literature. CONCLUSION: There are diverse perceptions regarding ADs among stakeholders. Further training, guidelines, and toolkits on the proper use of ADs are needed at all levels to overcome many of these perceived barriers. While education for principal investigators is important, it is also crucial to educate other groups in the community, such as patients, as well as clinicians and staff involved in their daily implementation.
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 | 0.288 | 0.576 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.008 |
| 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".