A Bayesian adaptive feasibility design for rare diseases
Bibliographic record
Abstract
It is important for researchers to carefully assess the feasibility of a clinical trial prior to the launch of the study. One feasibility aspect that needs to be considered includes whether investigators can expect to successfully achieve the sample size needed for their trial. In this manuscript, we present a Bayesian design in which data collected during a pilot study is used to predict the feasibility of a planned phase III trial. Specifically, we outline a model that predicts a target sample size obtained from the Gamma-Poisson distribution. In a simulation study, we showcase the utility of the proposed design by applying it to a phase III trial designed to assess the efficacy of mycophenolate mofetil in individuals with mild systemic sclerosis. We demonstrate that the predictive nature of the proposed design is particularly useful for rare disease clinical trials and has the potential to greatly increase their efficiency. • Pilot trials can be a useful way to make decisions regarding large-scale trials • Recruitment is an important component of assessing trial feasibility • Trials designed within a Bayesian framework allow for effective use of information
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".