Characteristics of Design and Statistical Analysis of NEI-funded Ophthalmic Clinical Trials
Bibliographic record
Abstract
Objective: To describe the characteristics of trial design and statistical analysis of the National Eye Institute (NEI)-funded randomized clinical trials (RCTs) conducted after the year 2000. Design: Review of 42 NEI-funded ophthalmic RCTs. Methods: Eligible trials were identified from ClinicalTrials.Gov and their primary result papers were identified from PubMed. Data on the design characteristics (primary outcome, number of arms, sample size, statistical power, inclusion of one eye or two eyes) and statistical analysis (statistical method for adjustment of inter-eye correlation, correction for multiple comparisons) as reported in the primary result paper were collected independently by two authors, and the differences were adjudicated by the senior author. Descriptive analyses were performed to summarize the characteristics of trial design and statistical analysis. Main Outcome Measures: Characteristics of trial design and statistical analysis. Results: Forty-two NEI-funded ophthalmic trials conducted after 2000 were included. The majority of trials were for evaluating the efficacy of drugs (57%), medical devices (21%), or procedures (14%) for the treatment of retinal diseases (45%) or pediatric eye diseases (45%). All trials were designed with at least 80% statistical power for comparing continuous (64%), binary (24%), or time-to-event (12%) primary outcome measures. In 11 (26%) trials enrolling both eyes of a participant, two eyes were in the same treatment group in 6 (55%) trials, and two-eye data were properly analyzed with adjustment for the inter-eye correlation when needed for all these trials. However, none of these trial publications explicitly stated that the inter-eye correlation was considered in the sample size and power calculation. In 13 trials with more than two arms, 12 (92%) trials adjusted for multiplicity using Bonferroni correction (42%), Hochberg procedure (42%) or Turkey’s method (17%). Conclusion: While the availability of two eyes of a participant may complicate the ophthalmic trial design and statistical analysis, NEI-funded trials followed good practice in the trial design and statistical analysis, with enrollment of two eyes of a participant when appropriate, and adjustment of the inter-eye correlation in the statistical analysis. The sample size and power calculation can be improved by considering the inter-eye correlation and clearly reporting such information for future ophthalmic trials is important.
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 | 0.466 | 0.729 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".