Cost-effectiveness of moderate-to-severe psoriasis biologic treatments in Brazilian private healthcare system: cost-per-responder results derived from a network metanalysis
Bibliographic record
Abstract
Objective: To assess the cost-per-responder (CpR) of biologic therapies available in Brazil to treat moderate-to-severe plaque psoriasis (PsO) from the private healthcare system’s perspective. Methods: Number needed to treat (NNT) and (CpR) analyses were performed to evaluate biologic therapies’ cost-effectiveness for moderate-to-severe PsO available in Brazil. The effectiveness of biologic treatments for moderate-to-severe PsO was assessed based on a previously published metanalysis, which included studies considering PsO patients and outcomes of interest (PASI 75, 90, and 100). The clinical efficacy data in terms of estimated NNT based on the network metanalysis (NMA) results were combined with drug treatment costs to determine the CpR for each treatment arm in 3-time horizons: the primary response period, 1-year, and 2-years. Results: Risankizumab was the most cost-effective option when NMA base case scenario data was used to calculate NNT in all PASI response for both the primary response period and 1- and 2-years follow-up durations. Differences in CpR between risankizumab and other biologic drugs increased with more significant PASI improvements. CpR sensitivity analysis also confirmed these findings, indicating that risankizumab has a better performance for PASI 100, and both risankizumab and guselkumab are very similar in terms of cost per additional PASI 75 and PASI 90 responder. Conclusions: Risankizumab was estimated to have a lower cost per PASI 75, 90, and 100 responders in most simulated scenarios (primary response period [12-16 weeks], 1-year and 2-years), among the evaluated biologic therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".