A Comparison of Randomizing Either One Eye or Both Eyes in Clinical Trials for Stargardt Disease Type 1
Bibliographic record
Abstract
Objective: Designing a clinical trial for rare diseases such as Stargardt disease type 1 is challenging due to the limited patient population. In traditional clinical trial designs for inherited retinal diseases, often only 1 eye of each patient is used as the treated eye or the sham, disregarding half of the available eyes.This study explores a trial design in which both eyes are included, with the fellow eye serving as the control, maximizing the use of available data and enhancing statistical power. Design: Retrospective analysis of natural history data to conduct sample size calculations. Participants: Patients with genetically solved Stargardt disease type 1 who had at least 2 fundus autofluorescence measurements obtained within 5 years of each other. Retrospective data of 164 patients were included for analysis. Methods: The required sample sizes for 1-eye and paired-eye study designs were calculated using retrospective natural history data on the progression of definitely decreased autofluorescence quantified from fundus autofluorescence imaging. Main Outcome Measures: Required sample size for a clinical trial. Results: Sample size calculations showed that 170 patients are needed for a 2-year clinical trial with a 1-eye design, decreasing to 99 patients for a 5-year trial. When using a paired-eye design, 64 patients are needed in a 2-year trial, decreasing to 28 patients in a 5-year trial. When using a paired-eye design and requiring definitely decreased autofluorescence atrophy in both eyes at inclusion, 37 patients were needed in a 2-year trial, decreasing to 16 patients in a 5-year trial. Conclusions: Using a paired-eye design for a clinical trial in Stargardt disease type 1, with definitely decreased autofluorescence atrophy growth rate as the primary end point, is more efficient than a 1-eye design. Implementing additional inclusion criteria, such as requiring definitely decreased autofluorescence atrophy in both eyes at baseline, further reduces the number of patients needed to achieve sufficient statistical power. This approach enhances the feasibility for trials in Stargardt disease type 1 where patient availability is limited. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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 | 0.129 | 0.198 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".