Systematic Review and Meta-analysis: Placebo Rates in Induction and Maintenance Trials of Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Minimisation of the placebo responses in randomised controlled trials [RCTs] is essential for efficient evaluation of new interventions. Placebo rates have been high in ulcerative colitis [UC] clinical trials, and factors influencing this are poorly understood. We quantify placebo response and remission rates in UC RCTs and identify trial design factors influencing them. METHODS: MEDLINE, EMBASE, and the Cochrane Library were searched from inception through April 2014 for placebo-controlled trials in adult patients with UC of a biological agent, corticosteroid, immunosuppressant, or aminosalicylate. Data were independently doubly extracted. Quality was assessed using the Cochrane risk of bias tool. RESULTS: In all, 51 trials [48 induction and 10 maintenance phases] were identified. Placebo response and remission rates were pooled according to random-effects models, and mixed-effects meta-regression models were used to evaluate effects of study-level characteristics on these rates. Pooled estimates of placebo remission and response rates for induction trials were 10% (95% confidence interval [CI] 7-13%) and 33% [95% CI 29-37%], respectively. Corresponding values for maintenance trials were 19% [95% CI 11-30%] and 22% [95% CI 17-28%]. Trials enrolling patients with more active disease confirmed by endoscopy [endoscopy subscore ≥ 2] were associated with lower placebo rates. Conversely, placebo rates increased with increasing trial duration and number of study visits. CONCLUSIONS: Objective assessment of greater disease activity at trial entry by endoscopy lowered placebo rates, whereas increasing trial duration and more interactions with healthcare providers increased placebo rates. These findings have important implications for design and conduct of clinical trials.
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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 | 0.094 | 0.212 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.051 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".