Cost-effectiveness analysis of secukinumab for the treatment of active psoriatic arthritis: a Canadian perspective
Bibliographic record
Abstract
Objective: The study evaluates the cost-effectiveness of secukinumab, a fully human monoclonal antibody that selectively neutralizes interleukin (IL)-17A, vs currently licensed biologic treatments in patients with active psoriatic arthritis (PsA) from a Canadian healthcare system perspective. Methods: A decision analytic semi-Markov model evaluated the cost-effectiveness of secukinumab 150 mg and 300 mg compared to subcutaneous biologics adalimumab, certolizumab pegol, etanercept, golimumab, and ustekinumab, and intravenous biologics infliximab and infliximab biosimilar in biologic-naive and biologic-experienced patients over a lifetime horizon. The response to treatments was evaluated after 12 weeks by PsA Response Criteria (PsARC) response rates. Non-responders or patients discontinuing initial-line of biologic treatment were allowed to switch to subsequent-line biologics. Model input parameters (Psoriasis Area Severity Index [PASI], Health Assessment Questionnaire [HAQ], withdrawal rates, costs, and resource use) were collected from clinical trials, published literature, and other Canadian sources. Benefits were expressed as quality-adjusted life years (QALYs). An annual discount rate of 5% was applied to costs and benefits. The robustness of the study findings were evaluated via sensitivity analyses. Results: Biologic-naive patients treated with secukinumab achieved the highest number of QALYs (8.54) at the lowest cost (CAD 925,387) over a lifetime horizon vs all comparators. Secukinumab dominated all treatments, except for infliximab and its biosimilar, which achieved minimally more QALYs (8.58). However, infliximab and its biosimilar incurred more costs than secukinumab (infliximab: CAD 1,015,437; infliximab biosimilar: CAD 941,004), resulting in higher cost-effectiveness estimates relative to secukinumab. In the biologic-experienced population, secukinumab dominated all treatments as it generated more QALYs (8.89) at lower costs (CAD 954,692). Deterministic sensitivity analyses indicated the results were most sensitive to variation in PsARC response rates, change in HAQ, and utility values in both populations. Conclusions: Secukinumab is either dominant or cost-effective vs all licensed biologics for the treatment of active PsA in biologic-naive and biologic-experienced populations in Canada.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".