P849 Bayesian network meta-analysis of the efficacy of advanced therapies for patients with moderately to severely active ulcerative colitis naïve to advanced therapy
Bibliographic record
Abstract
Abstract Background Etrasimod is an oral, once-daily, selective sphingosine 1-phosphate (S1P)1,4,5 receptor modulator for the treatment of moderately to severely active ulcerative colitis (UC). In the absence of head-to-head randomised controlled trials (RCTs), network meta-analyses (NMA) offer insight into the comparative effectiveness of treatment options. NMA were conducted to examine the relative efficacy of etrasimod vs other advanced therapies (AT) with licensed dosing (European Medicines Agency) for the treatment of UC in patients naïve to biologic agents and/or Janus kinase inhibitors. Methods A systematic literature review (SLR) was performed on 15 November 2022, and covered all available records without time limit, using NICE DSU and PRISMA guidelines. NMA were conducted under a Bayesian framework, and a multinomial fixed-effect approach was used to model outcomes, clinical response and clinical remission, in the induction phase and among induction phase responders in the maintenance phase, in patients naïve to AT. Reported outcomes from trials with a treat-through design, such as ELEVATE UC 52, were recalculated to mimic those of a responder re-randomisation design; only responders in the induction phase were analysed in the maintenance phase. Data are presented as median relative risk (RR) of the treatment vs its comparator, along with corresponding 95% credible intervals (CrI). Prespecified sensitivity analyses were performed. Results Of 81 studies identified from the SLR, 21 and 11 RCTs were included in the induction and maintenance networks, respectively. For induction and maintenance phases, all therapies demonstrated benefit over placebo, consistent with phase 3 clinical trial data. In the NMA for clinical remission in the induction phase, etrasimod 2 mg had a statistically significant benefit over adalimumab 80/40 mg and 160/80 mg (RR [95% CrI] for treatment vs etrasimod 0.49 [0.29–0.78] and 0.67 [0.50–0.92], respectively), filgotinib 100 mg (0.55 [0.37–0.84]) and placebo; conversely, upadacitinib 45 mg had statistically significant benefit vs etrasimod (1.47 [1.07–2.03]; Table). There were no statistically significant differences for etrasimod vs other comparators. Similar results were observed for clinical response. In the maintenance phase, there were no statistically significant differences between etrasimod 2 mg and other treatments for clinical remission and clinical response (Table). Conclusion With respect to clinical remission and clinical response during induction and maintenance phases, etrasimod efficacy was similar to most comparators as a first-line AT. Differences in trial design and risk-benefit profiles of AT should be considered when interpreting NMA results.
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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 | 0.037 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.048 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".