The higher benefit of lecanemab in males compared to females in CLARITY AD is probably due to a real sex effect
Bibliographic record
Abstract
INTRODUCTION: The phase 3 trial CLARITY AD found lecanemab slowed cognitive decline by 27%. However, subgroup analyses indicated a significant 31% sex difference in the effect and suggested no or limited effectiveness in females. We used simulations constrained by the trial design to determine whether that difference reflects a pre-existing sex difference in Alzheimer's disease progression or was a random event. METHODS: Simulations were generated using linear mixed models of cognitive decline fit to data from Alzheimer's Disease Neuroimaging Initiative participants satisfying CLARITY AD inclusion criteria. RESULTS: The statistically non-significant 7.9% smaller cognitive decline rate in our cohort's males versus females does not explain CLARITY AD's 31% sex difference in lecanemab's effect. A ≥ 31% difference occurred randomly in only 12 of our 10,000 simulations (0.0012 probability). DISCUSSION: CLARITY AD's sex difference was probably not random. Lecanemab is likely less effective in females than males, but we cannot conclude the drug is ineffective in females. HIGHLIGHTS: Lecanemab is more clinically effective in males than in females. Forest plots should only report subgroup-specific effects in well-powered subgroups. Trial simulations based on real data enable investigation of subgroup drug effects. We cannot conclude that lecanemab is clinically ineffective in females. A sex difference in lecanemab's efficacy could be linked to its action mechanism.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".