Design optimization for clinical trials in early‐stage manifest Huntington's disease
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to inform the design of randomized clinical trials in early-stage manifest Huntington's disease through analysis of longitudinal data from TRACK-Huntington's Disease (TRACK-HD), a multicenter observational study. METHODS: We compute sample sizes required for trials with candidate clinical, functional, and imaging outcomes, whose aims are to reduce rates of change. The calculations use a 2-stage approach: first using linear mixed models to estimate mean rates of change and components of variability from TRACK-HD data and second using these to predict sample sizes for a range of trial designs. RESULTS: For each outcome, the primary drivers of the required sample size were the anticipated treatment effect and the duration of treatment. Extending durations from 1 to 2 years yielded large sample size reductions. Including interim visits and incorporating stratified randomization on predictors of outcome together with covariate adjustment gave more modest, but nontrivial, benefits. Caudate atrophy, expressed as a percentage of its baseline, was the outcome that gave smallest required sample sizes. DISCUSSION: Here we consider potential required sample sizes for clinical trials estimated from naturalistic observation of longitudinal change. Choice among outcome measures for a trial must additionally consider their relevance to patients and the expected effect of the treatment under study. For all outcomes considered, our results provide compelling arguments for 2-year trials, and we also demonstrate the benefits of incorporating stratified randomization coupled with covariate adjustment, particularly for trials with caudate atrophy as the primary outcome. The benefits of enrichment are more debatable, with statistical benefits offset by potential recruitment difficulties and reduced generalizability. © 2017 International Parkinson and Movement Disorder Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".