The interpretation of long-term trials of biologic treatments for psoriasis: trial designs and the choices of statistical analyses affect ability to compare outcomes across trials
Bibliographic record
Abstract
Psoriasis is a chronic disease requiring long-term therapy, which makes finding treatments with favourable long-term safety and efficacy profiles crucial. The goal of this review is to provide the background needed to evaluate properly long-term studies of biologic treatments for psoriasis. Firstly, important elements of design and analysis strategies are described. Secondly, data from published trials of biologic therapies for psoriasis are reviewed in light of the design and analysis choices implemented in the studies. Published reports of clinical trials of biologic treatments (adalimumab, alefacept, etanercept, infliximab or ustekinumab) that lasted 33 weeks or longer and included efficacy results and statistical analysis were reviewed. Study designs and statistical analyses were evaluated and summarized, emphasizing patient follow-up methods and handling of missing data. Various trial designs and data handling methods are used in long-term studies of biologic psoriasis treatments. Responder analyses in long-term trials can be conducted in responder enrichment, re-treated nonresponder or intent-to-treat trials. Missing data can be handled in four ways, including, from most to least conservative, nonresponder imputation, last-observation-carried-forward, as-observed analysis and anytime analysis. Long-term clinical trials have shown that adalimumab, alefacept, etanercept, infliximab and ustekinumab are efficacious for psoriasis treatment; however, without common standards for these trials, direct comparisons of these agents are difficult. Understanding differences in trial design and data handling is essential to make informed treatment decisions.
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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 | 0.367 | 0.608 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".