Use of Quantile Regression to Characterize Solanezumab Effects across Percentiles of Disease Progression in EXPEDITION Alzheimer's Disease Trials (P7.102)
Bibliographic record
Abstract
OBJECTIVE: Use quantile regression to characterize solanezumab effects in Alzheimer’s disease (AD) patients based on degree of disease progression. BACKGROUND: Twenty-seven percent of patients with clinically defined mild-to-moderate AD enrolled in 2 solanezumab trials had baseline assessments of amyloid status. Of those, 17[percnt] were negative and did not exhibit progressive decline typical of AD. Because comparison of solanezumab- and placebo-treatment effects on clinical progression in amyloid positive versus negative patients in the full cohort was precluded, we developed a novel statistical strategy to examine treatment effects in patients who did and did not show typical disease progression. DESIGN/METHODS: Quantile regression was used to examine solanezumab- and placebo-treatment differences in a pooled dataset. Patients demonstrating clinical decline typical of AD were represented in upper clinical-change percentiles; patients with a clinical course atypical of AD were represented in lower percentiles. This approach modeled change from baseline on 2 primary measures: Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog14) and Alzheimer’s Disease Cooperative Study-Activities of Daily Living Inventory Instrumental domain (ADCS iADL). RESULTS: ADAS-Cog14 at Week 80: In patients with mild AD, solanezumab treatment effect was greater in the upper percentiles (AD-typical). Compared with placebo, solanezumab slowed disease progression by 1.27, 1.98, 2.55, and 3.07 points in the 30th, 40th, 60th, and 80th percentiles. In lower percentiles (AD-atypical), solanezumab showed less effect. ADCS-iADL at Week 80: In patients with mild AD, compared with placebo, solanezumab slowed disease progression by 0.47, 0.99, 1.79, and 1.69 points in the 30th, 40th, 60th, and 80th percentiles. In patients with moderate AD, solanezumab did not show effects across most percentiles on either measure. Conclusions: Results support design of current/future solanezumab trials to be limited to mild AD patients with evidence of amyloid pathology. Further, quantile regression is useful for retrospectively analyzing data when amyloid status was not uniformly determined.
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 | 0.055 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".