Landscape of Phase II Trials in Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Drug development in Alzheimer's disease (AD) over the past two decades has had high rates of failure. Novel trial designs, such as adaptive designs, have the potential to improve the efficiency of drug development in AD. OBJECTIVE: To evaluate the design characteristics, temporal trends, and differences in design between sponsor types in phase II trials of investigational agents in AD. METHODS: Phase I/II, II, and II/III trials for AD with drug or other biological interventions registered from December 1996 to December 2021 in ClinicalTrials.gov were included. Descriptive statistics were used to summarize trial characteristics. Linear, logistic, and multinomial regression models assessed temporal trends and differences between sponsor types in design characteristics. RESULTS: Of N = 474 trials identified, randomized parallel group design was the most common design (72%). Only 12 trials (2.5%) used an adaptive design; adaptive features included early stopping rules, model-based dose-finding, adaptive treatment arm selection, and response adaptive randomization. The use of non-randomized parallel-group and open-label single arm designs increased over time. No temporal trend in the use of adaptive design was identified. Trials sponsored by industry only were more likely to use a randomized parallel-group design and have a larger estimated sample size than trials with other sponsor types. CONCLUSION: Our systematic review showed that very few phase II trials in AD used an adaptive trial design. Innovation and implementation of novel trial designs in AD trials can accelerate the drug development process.
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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 | 0.346 | 0.516 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".