COMPARZ Post Hoc Analysis: Characterizing Pazopanib Responders With Advanced Renal Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: The phase III COMPARZ study showed noninferior efficacy of pazopanib versus sunitinib in advanced renal cell carcinoma. In this COMPARZ post hoc analysis we characterized pazopanib responders, patient subgroups with better outcomes, and the effect of dose modification on efficacy and safety. PATIENTS AND METHODS: Patients were randomized to pazopanib 800 mg/d (n = 557) or sunitinib 50 mg/d, 4 weeks on/2 weeks off (n = 553). Secondary end points included time to complete response (CR)/partial response (PR); the proportion of patients with CR/PR ≥10 months and progression-free survival (PFS) ≥10 months; efficacy in patients with baseline metastasis; and logistic regression analyses of patient characteristics associated with CR/PR ≥10 months. Median PFS, objective response rate (ORR), and safety were evaluated in patients with or without dose reductions or interruptions lasting ≥7 days. RESULTS: Median time to response was numerically shorter for patients treated with pazopanib versus sunitinib (11.9 vs. 17.4 weeks). Similar percentages of pazopanib and sunitinib patients had CR/PR ≥10 months (14% and 13%, respectively), and PFS ≥10 months (31% and 34%, respectively). For patients without versus with adverse event (AE)-related dose reductions, median PFS, median overall survival, and ORR were 7.3 versus 12.5 months, 21.7 versus 36.8 months, and 22% versus 42% (all P < .0001) for pazopanib, and 5.5 versus 13.8 months, 18.1 versus 38.0 months, and 16% versus 34% (all P < .0001) for sunitinib; results were similar for dose interruptions. CONCLUSION: Dose modifications when required because of AEs were associated with improved efficacy, suggesting that AEs might be used as a surrogate marker of adequate dosing for individual patients.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".