173 Comparative effectiveness of secukinumab and golimumab in ankylosing spondylitis assessed by matching-adjusted indirect comparison using pivotal phase III clinical trial data
Bibliographic record
Abstract
Background: No data are available from head-to-head randomised controlled trials (RCTs) between secukinumab 150 mg (an anti-interleukin-17A) and golimumab 50 mg (a tumour necrosis factor inhibitor [TNFi]) in patients with active ankylosing spondylitis (AS). Matching-adjusted indirect comparison (MAIC) can be used to estimate comparative effectiveness and enables treatment outcomes to be compared across effectively balanced trial populations. MAIC is an established method in health-technology assessments and the National Institute for Health and Care Excellence has published guidance on appropriate methodology for addressing imbalances in observed covariates between trials. The comparative effectiveness of secukinumab and golimumab up to Week 24 was assessed using MAIC with pooled individual patient data (IPD) from the RCTs MEASURE 1 and MEASURE 2 and published aggregate data from the RCT GO-RAISE. Methods: Pooled MEASURE 1 and 2 data were used to maximise the effective sample size (ESS) for secukinumab. IPD from the secukinumab arms of MEASURE 1 and MEASURE 2 (n = 197) were weighted to match the published baseline characteristics of the golimumab arm of GO-RAISE (n = 138). Placebo arms were matched in the same way; placebo-adjusted comparisons were possible only until week 16 because patients could receive active treatment from this time onwards. Logistic regression was used to determine weights for age, sex, Bath AS Functional Index, disease duration, C-reactive protein and previous TNFi therapy. Recalculated outcomes from MEASURE 1 and MEASURE 2 (secukinumab, ESS=102; placebo, ESS=81) were compared with data from GO-RAISE (golimumab, n = 138; placebo, n = 78). Pairwise comparisons reported as odds ratios (ORs [95% CIs]) were performed for Assessment in SpondyloArthritis International Society criteria (ASAS) 20, ASAS 40 and ASAS partial remission (PR) responses at nearest-equivalent timepoints across trials: week 12 (secukinumab)/14 (golimumab), week 14 (golimumab)/16 (secukinumab) and week 24 (secukinumab and golimumab). Non-responder imputation (NRI) was available for all binary outcome data. Strict thresholds were avoided when interpreting P values, in line with American Statistical Association guidance.
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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 | 0.162 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".