Empirical progression criteria thresholds for feasibility outcomes in HIV clinical trials: a methodological study
Bibliographic record
Abstract
INTRODUCTION: Pilot and feasibility trials use predetermined thresholds for feasibility outcomes to decide if a larger trial is feasible. These thresholds may be derived from the literature, observational data, or clinical experience. The aim of this study was to determine empirical estimates for feasibility outcomes to inform future HIV pilot randomized trials. METHODS: We conducted a methodological study of HIV clinical trials indexed in the past 5 years (2017-2021) in the PubMed database. We included trials of people living with HIV individually randomized to any type of intervention and excluded pilot trials and cluster randomized trials. Screening and data extraction were conducted in duplicate. We computed estimates for recruitment, randomization, non-compliance, lost to follow-up, discontinuation, and the proportion analyzed using a random effects meta-analysis of proportions and reported these estimates according to the following subgroups: use of medication, intervention type, trial design, income level, WHO region, participant type, comorbidities, and source of funding. We report estimates with 95% confidence intervals. RESULTS: We identified 2122 studies in our search, of which 701 full texts were deemed relevant, but only 394 met our inclusion criteria. We found the following estimates: recruitment (64.1%; 95% CI 57.7 to 70.3; 156 trials); randomization (97.1%; 95% CI 95.8 to 98.3; 187 trials); non-compliance (3.8%; 95% CI 2.8 to 4.9; 216 trials); lost to follow-up (5.8%; 95% CI 4.9 to 6.8; 251 trials); discontinuation (6.5%; 95% CI 5.5 to 7.5; 215 trials); analyzed (94.2%; 95% CI 92.9 to 95.3; 367 trials). There were differences in estimates across most subgroups. CONCLUSION: These estimates may be used to inform the design of HIV pilot randomized trials with careful consideration of variations due to some of the subgroups investigated.
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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 | 0.698 | 0.880 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".