An analysis of outcome measures for the design of phase 2 futility trials in Alzheimer's Disease (AD)
Bibliographic record
Abstract
Alzheimer’s disease is a brain condition that slowly worsens over time, causing memory loss, confusion, and problems with daily life. It affects millions of people worldwide and is one of the most common causes of dementia in older adults. Despite the urgent need for better treatments, developing new medicines for Alzheimer’s disease has been very challenging. Traditionally, clinical trials (research studies that test whether new treatments are safe and effective) for Alzheimer’s disease are conducted as randomized controlled trials, which means that participants are randomly assigned to receive either the treatment or a comparison, such as a placebo (an inactive substance). While this approach is very reliable, it requires a large number of participants and can take several years to complete. These factors make trials expensive and limit how many can be carried out. To speed up the discovery of new treatments, we will explore a different trial approach called the Simon Two-Stage futility design. This design allows researchers to stop a study early if the treatment does not appear to be working, which can save time, reduce costs, and allow resources to be focused on more promising therapies. To design such trials well, we need to understand how people with Alzheimer’s disease typically change over time during a study. This includes knowing how quickly symptoms worsen or, in some cases, improve on clinical outcome measures (tests used to assess memory, thinking, or daily functioning). It is also important to understand what kinds of side effects (adverse events) participants experience, and when and why they may decide to stop taking part in trials. In this project, we will study large existing datasets from previous Alzheimer’s disease trials. These datasets contain information on participants’ baseline characteristics (such as clinical features, brain scans, or blood test results) as well as details on how their condition changed over time. By carefully analyzing these data, we will identify patterns in disease progression, side effects, and withdrawal from trials. This work is necessary because it will provide a clearer picture of how Alzheimer’s disease progresses in trial participants and how different baseline factors might influence outcomes. These insights will guide the design of more efficient future clinical trials. Ultimately, this research could make it faster and less costly to test new treatments, increasing the chances of finding effective therapies for people living with Alzheimer’s disease.
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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 | 0.341 | 0.441 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".