Sensitivity of adaptive enrichment trial designs to accrual rates, time to outcome measurement, and prognostic variables
Bibliographic record
Abstract
Adaptive enrichment designs involve rules for restricting enrollment to a subset of the population during the course of an ongoing trial. This can be used to target those who benefit from the experimental treatment. Trial characteristics such as the accrual rate and the prognostic value of baseline variables are typically unknown when a trial is being planned; these values are typically assumed based on information available before the trial starts. Because of the added complexity in adaptive enrichment designs compared to standard designs, it may be of special concern how sensitive the trial performance is to deviations from assumptions. Through simulation studies, we evaluate the sensitivity of Type I error, power, expected sample size, and trial duration to different design characteristics. Our simulation distributions mimic features of data from the Alzheimer's Disease Neuroimaging Initiative cohort study, and involve two subpopulations based on a genetic marker. We investigate the impact of the following design characteristics: the accrual rate, the time from enrollment to measurement of a short-term outcome and the primary outcome, and the prognostic value of baseline variables and short-term outcomes. To leverage prognostic information in baseline variables and short-term outcomes, we use a semiparametric, locally efficient estimator, and investigate its strengths and limitations compared to standard estimators. We apply information-based monitoring, and evaluate how accurately information can be estimated in an ongoing trial.
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 | 0.293 | 0.541 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".