Meta‐analysis: Placebo rates in microscopic colitis randomised trials and applications for future drug development using a historical control arm
Bibliographic record
Abstract
BACKGROUND: Effective medical therapies for patients with microscopic colitis (MC) who fail budesonide are lacking. However, conducting randomised controlled trials (RCTs) in MC has been challenging due to small sample sizes. Understanding placebo responses can help inform more efficient future trials. AIMS: The aim of this study is to estimate clinical and histologic placebo response rates and to determine factors associated with placebo response in MC. METHODS: EMBASE, MEDLINE, and CENTRAL were searched until 7 January 2022, to identify placebo-controlled RCTs in adult patients with MC. Clinical and histologic response in the placebo arms were pooled using random-effects models. Stratified analyses based on disease- and trial-level characteristics, leave-one-out meta-analysis, and cumulative meta-analysis were performed. RESULTS: = 66.4%, p = 0.01 (tests for heterogeneity), respectively. Clinical response to placebo was numerically higher in patients with lymphocytic compared to collagenous colitis (39.9% vs. 19.8%, p = 0.08). Heterogeneity in clinical response to placebo was significantly reduced when the Miehlke 2014 RCT was excluded in the leave-one-out meta-analysis or when a more stringent secondary definition of response based on the Hjortswang criteria was applied. CONCLUSIONS: Approximately one-quarter of patients in MC trials respond to placebo, although with substantial heterogeneity, reflecting the need for standardised outcome definitions and study designs for MC. This analysis also serves to inform future MC trials that may consider incorporating an external, historical placebo control arm, rather than directly randomising patients to 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 | 0.137 | 0.224 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.056 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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; 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".