Comparative Risk-Benefit Analysis of Different Classes of Biologic Agents in Patients with Psoriasis: A Case Study on the Pros and Cons of Mixed Treatment Comparison in Synthesizing Complex Evidence Networks
Bibliographic record
Abstract
Background: Several classes of biologic agents are used for the management of moderate to severe psoriasis or psoriatic arthritis. However, there is uncertainty on which, if any, individual class of biologics is superior in terms of efficacy and safety in comparison to the other classes or placebo. We thus exploited the corresponding evidence network with suitable statistical methods (mixed treatment comparison and network meta-analysis) to formally address this issue. Methods: Randomized trials on biologic agents in psoriasis (including psoriatic arthritis) were systematically sought in several databases. We distinguished anti-tumor necrosis factor-α (TNF-α) agents, anti-T lymphocytes (T-cell) agents, anti-interleukin-12/23 (IL-12/23) agents, and anti-interleukin-17 (IL-17) agents. Endpoints of interest were the rates of ≥75% reduction in the Psoriasis Area and Severity Index (PASI75), of ≥20% improvement in the American College of Rheumatology core set of outcomes (ACR20), of serious adverse events (SAE), and of adverse events (AE) at the longest available non-cross-over follow-up. Random-effect methods were used to obtain network estimates for risk ratios (RR, with 95% credible intervals). Results: A total of 58 trials with 18,508 patients were included, with 51% affected by psoriatic arthritis. After a median of 17 weeks since randomization into parallel groups, several classes of biologic agents provided higher PASI75 rates than placebo, with anti-IL-17 agents yielding the most favorable results (RR=9.53 [5.55-13.80]). Accordingly, several classes of biologic agents provided higher ACR20 rates than placebo, with anti-TNF-α agents yielding the most favorable results (RR=2.58 [2.12-3.15]). Overall, rates of SAE and AE were higher for several but not all biologic agents versus placebo, with anti-T-cell agents being associated with the most favorable results for both SAE (RR=0.97 [0.30-3.35]), and AE (RR=1.00 [0.80-1.22]). Conclusions: Biologic agents provide significant clinical benefits in patients with moderate to severe psoriasis or psoriatic arthritis. There are differences in the efficacy and safety profile of each class, with anti-IL-17 and anti-TNF-α agents appearing most effective, and anti-T-cell agents appearing safest.
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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 | 0.193 | 0.384 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".