Methodological and Reporting Quality of Noninferiority Randomized Controlled Trials Comparing Antiretroviral Therapies: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: It is unclear whether the reporting quality of antiretroviral (ARV) noninferiority (NI) randomized controlled trials (RCTs) has improved since the CONSORT guideline release in 2006. The primary objective of this systematic review was assessing the methodological and reporting quality of ARV NI-RCTs. We also assessed reporting quality by funding source and publication year. METHODS: We searched Medline, Embase, and Cochrane Central from inception to 14 November 2022. We included NI-RCTs comparing ≥2 ARV regimens used for human immunodeficiency virus treatment or prophylaxis. We used the Cochrane Risk of Bias 2.0 tool to assess risk of bias. Screening and data extraction were performed blinded and in duplicate. Descriptive statistics were used to summarize data; statistical tests were 2 sided, with significance defined as P < .05. The systematic review was prospectively registered (PROSPERO CRD42022328586), and not funded. RESULTS: We included 160 articles reporting 171 trials. Of these articles, 101 (63.1%) did not justify the NI margin used, and 28 (17.5%) did not provide sufficient information for sample size calculation. Eighty-nine of 160 (55.6%) reported both intention-to-treat and per-protocol analyses, while 118 (73.8%) described missing data handling. Ten of 171 trials (5.9%) reported potentially misleading results. Pharmaceutical industry-funded trials were more likely to be double-blinded (28.1% vs 10.3%; P = .03) and to describe missing data handling (78.5% vs 59.0%; P = .02). The overall risk of bias was low in 96 of 160 studies (60.0%). CONCLUSIONS: ARV NI-RCTs should improve NI margin justification, reporting of intention-to-treat and per-protocol analyses, and missing data handling to increase CONSORT adherence.
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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 | 0.291 | 0.644 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.015 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".