Tocilizumab in rheumatoid arthritis: A case study of safety evaluations of a large postmarketing data set from multiple data sources
Bibliographic record
Abstract
OBJECTIVES: To evaluate the magnitude of serious adverse events (SAEs) observed in postmarketing reports of tocilizumab (TCZ) for rheumatoid arthritis (RA) in relation to SAEs observed in TCZ clinical trials and external epidemiology data. METHODS: A total of 64,000 patient-years (PY) of TCZ exposure was needed to determine, with 90% power, whether rates of SAEs of interest (eg, death, hepatic, gastrointestinal, and cardiovascular) were ≥50% higher (agreed with the Food and Drug Administration) than expected. Reporting rates were calculated for spontaneously reported SAEs, open-label or unblinded postmarketing clinical trials (phase 3b/4), and a Japanese postmarketing surveillance program in the global postmarketing safety database. Event rates were calculated for the registrational placebo-controlled trials and long-term extension data. External comparators for anti-tumor necrosis factor (aTNF)-treated RA patients were derived from a US-based health care insurance claims database or published literature. RESULTS: The global postmarketing safety database provided 65,099 PY of TCZ exposure; the aTNF external comparator population provided 53,360 PY. Spontaneous reporting rates per 100 PY (95% confidence interval) were 8.3 (8.1, 8.5) SAEs, 0.39 (0.34, 0.44) deaths, 0.06 (0.04, 0.08) serious hepatic events, 0.15 (0.12, 0.18) serious gastrointestinal events, 0.09 (0.07, 0.12) serious myocardial infarctions, 0.15 (0.12, 0.18) serious strokes, and 0.07 (0.05, 0.09) cardiac deaths in the global postmarketing safety database. These were of similar magnitude to corresponding rates from registrational clinical trials, the aTNF external comparator population, and published literature. CONCLUSIONS: SAE rates observed among postmarketing TCZ users were similar to those of various comparison populations. Predetermined design of studies to compare postmarketing AEs using multiple data sources is a useful strategy that can be applied to other medications.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".