Signal Detection and Methodological Limitations in a Real-World Registry: Learnings from the Evaluation of Long-Term Safety Analyses in PSOLAR
Bibliographic record
Abstract
INTRODUCTION: Psoriasis Longitudinal Assessment and Registry (PSOLAR) was designed in 2007 as the first disease-based registry for patients with psoriasis. OBJECTIVE: The aim of this study was to discuss methodological limitations and post hoc analyses in long-term safety registries using learnings from analyses of a potential safety risk for major adverse cardiovascular events (MACE) in PSOLAR. METHODS: PSOLAR is an international observational study of over 12,000 psoriasis patients that was conducted to meet postmarketing safety commitments for infliximab and ustekinumab. A recent annual review of registry data indicated a potential MACE risk for ustekinumab vs. non-biologics based on prespecified COX model regression analyses, which yielded an adjusted hazard ratio (HR) of 1.533 (95% confidence interval [CI] 1.103-2.131). Therefore, we conducted a comprehensive review of key statistical methodology and implemented post hoc analytical methods to address specific limitations. RESULTS: The following limiting factors were identified: (1) inclusion of both prevalent and incident (new) users of biologics; (2) unanticipated imbalances in patient characteristics between treatment cohorts at baseline; (3) limited availability of relevant clinical data after enrollment; and (4) divergence of characteristics associated with outcomes among comparator groups over time. The analysis was modified to include only incident users, propensity scores were used to weight HRs, and adalimumab was deemed a more clinically appropriate comparator. The revised HR was 0.820 (95% CI 0.532-1.265), indicating no meaningful increase in MACE risk for ustekinumab. CONCLUSION: Our results, which do not support a causal association between ustekinumab exposure and MACE risk, underscore the need for ongoing assessment of analytical methods in long-term observational studies.
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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 | 0.768 | 0.883 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 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".