Matching-adjusted indirect comparison of efficacy in patients with moderate-to-severe plaque psoriasis treated with ixekizumab vs. secukinumab
Bibliographic record
Abstract
BACKGROUND: Head-to-head randomized studies comparing ixekizumab and secukinumab in the treatment of psoriasis are not available. OBJECTIVES: To assess efficacy and quality of life using matching-adjusted indirect comparisons for treatment with ixekizumab vs. secukinumab. METHODS: Psoriasis Area and Severity Index (PASI) improvement of at least 75%, 90% and 100% and Dermatology Life Quality Index (DLQI) 0/1 response rates for approved dosages of ixekizumab (160 mg at Week 0, then 80 mg every two weeks for the first 12 weeks) and secukinumab (300 mg at Weeks 0, 1, 2, 3 and 4, then 300 mg every 4 weeks) treatment were compared using data from active (etanercept and ustekinumab) and placebo-controlled studies. Comparisons were made using the Bucher (BU) method and two modified versions of the Signorovitch (SG) method (SG total and SG separate). Subsequently, results based on active treatment common comparators were combined using generic inverse-variance meta-analysis. RESULTS: In the meta-analysis of studies with active comparators, PASI 90 response rates were 12·7% [95% confidence interval (CI) 5·5-19·8, P = 0·0005], 10·0% (95% CI 2·1-18·0, P = 0·01) and 11·2% (95% CI 3·2-19·1, P = 0·006) higher and PASI 100 response rates were 11·7% (95% CI 5·9-17·5, P < 0·001), 12·7% (95% CI 6·0-19·4, P < 0·001) and 13·1% (95% CI 6·3-19·9, P < 0·001) higher for ixekizumab compared with secukinumab using BU, SG total and SG separate methods. PASI 75 results were comparable when SG methods were used and favoured ixekizumab when the BU method was used. Week 12 DLQI 0/1 response rates did not differ significantly. CONCLUSIONS: Ixekizumab had higher PASI 90 and PASI 100 responses at week 12 compared with secukinumab using adjusted indirect comparisons.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".