Placebo rates in randomized clinical trials of ulcerative colitis: an individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: We assessed placebo rates and associated factors using individual patient data (IDP) from randomized clinical trials (RCTs) in ulcerative colitis (UC). METHODS: We conducted an IPD meta-analysis using Vivli and Yale University Open Data Access data-sharing platforms. Phase 2 and 3 RCTs of advanced biologics in adults with moderate-to-severe UC published since 2010 were included. Pooled placebo rates and 95% CIs were estimated using one- and two-stage meta-analytical approaches. Significant patient-level factors (P < .05) were identified using regression analyses. Primary outcomes were clinical response and remission. RESULTS: Data were available for 1703 patients from nine studies. For induction trials, overall placebo response and remission rates were 33% (95% CI 29%-38%) and 9% (95% CI 7%-11%). Overall placebo response and remission rates in maintenance trials were 28% (95% CI 18%-41%) and 14% (95% CI 9%-20%). A lower body mass index reduced the odds of placebo response and remission, while higher baseline albumin levels and left-sided (compared to extensive) UC increased the odds of these outcomes. A 1-point increase in the Mayo Clinic Score (MCS) and adapted MCS was associated with a 26% and 27% reduction in odds of clinical remission. For induction trials, prior biologic exposure was associated with lower odds of response and remission. Multicenter trials have lower placebo effects than single-center trials. CONCLUSIONS: These results enable future trials to incorporate design elements that reduce placebo rates as well as a precise benchmark for expected rates in clinical trials that do not include placebo.
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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 | 0.132 | 0.180 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.066 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".