Global platform trial design for tau (MAPT) mutation carriers
Bibliographic record
Abstract
Abstract Background Pathogenic mutations in the MAPT (tau) gene cause familial frontotemporal lobar degeneration (f‐FTLD). No disease‐modifying treatments exist. Tau therapeutics being tested in other tauopathies may be suitable candidates, but the small number of identified mutation carriers prohibits traditional clinical trial designs from achieving adequate power. Platform trial designs permit multiple therapies to be tested in parallel and improve power by sharing placebo groups across treatment arms. We evaluate whether a platform design could improve the likelihood of identifying an effective treatment for MAPT mutation carriers. Method ALLFTD and GENFI observational data were used to simulate MAPT prevention and early symptomatic clinical trials (CDR®+NACC‐FTLD≤1). Only presymptomatic participants (CDR®+NACC‐FTLD = 0) within 2.5 years of expected symptom onset based on disease progression models (DPM) were included. First, we evaluated the time and sample size required to test five treatments using a platform design compared to sequential randomized control trials. Platform designs assumed a max of three treatment arms at any time, with new arms enrolling upon completion of prior arms. Each arm randomized 60 mutations carriers 3:1 treatment to placebo, with a placebo group shared across arms. Minimum follow up was 1.5 years with common close. Sequential trials assumed 90 participants allocated 1:1 treatment to placebo, with three‐month intervals between trials. Using a platform design, we compared a novel method for testing therapeutic effects using DPMs to account for differential rates of expected progression compared to traditional mixed models for repeated measures (MMRM) tests controlling for baseline CDR®+NACC‐FTLD. Result Five treatments could be tested in a platform design using 300 participants (75% treated) over five years, compared to 450 participants (50% treated) over 13 years using traditional, sequential trials. Platform trial simulations indicated that with 80% power, DPM‐based analytic approach could detect a 25% treatment effect, whereas traditional MMRM could only detect a 40% effect. Conclusion Platform designs combined with DPM‐based analyses provide a path to testing more treatments with fewer participants. Adequately powered MAPT platform trials are feasible but require a global clinical trial infrastructure.
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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 | 0.061 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".