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Characteristics of Design and Statistical Analysis of NEI-funded Ophthalmic Clinical Trials

2023· article· en· W4390489379 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Open Ophthalmology Journal · 2023
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsConestoga College
Fundersnot available
KeywordsMedicineClinical trialSample size determinationRandomized controlled trialResearch designStatistical analysisDescriptive statisticsOphthalmologyOptometrySurgeryStatisticsInternal medicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.111
metaresearch head score (Gemma)0.303
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1110.303
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.864
GPT teacher head0.684
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it