Immunotherapy in advanced kidney cancer: an alternative meta-analytic method using reconstructed survival data in case of proportional hazard assumption violation
Bibliographic record
Abstract
Background With the advent of immuno-oncology compounds in randomized trials, we observe more and more survival curves crossing. From a statistical standpoint this corresponds to violation of the proportional hazard assumption. When this occurs, the hazard ratio from the Cox regression is not reliable as an estimate. Herein, we aimed to identify the most appropriate IO-based therapy for metastatic renal cell carcinoma applying an alternative method to overcome the issue of hazard assumption violation for meta-analyses. Methods Pubmed, EMBASE, Web of Science and Scopus databases were searched. Only phase III randomized clinical trials on IO-IO (nivo-ipi) or IO-TKI combinations were included. An algorithm to obtain survival data from published Kaplan-Meier curves was used to reconstruct data on overall survival (OS), progression-free survival (PFS) and duration of response (DoR). Differences in restricted mean survival time (RMST) were used for comparisons. Results individual survival data from 4,206 patients from five trials were reconciled. Patients who received nivo-ipi or IO-TKI had better OS, PFS and DoR relative to sunitinib (all p<0.001). Patients who received IO-TKI had similar OS and PFS relative to nivo-ipi, with a 36-month ΔRMST of -0.55 (95% CI: -1.71-0.60; p=0.3) and -1.5 (95% CI: -2.9-0.0; p=0.051) months, respectively. Regarding DoR, patients who received nivo-ipi had longer duration of response relative to IO-TKI, with a 24-month ΔRMST of 1.5 (95% CI: 0.2-2.8; p=0.02) months. Conclusion Despite overall similar OS and PFS for patients receiving nivo-ipi and IO-TKI combinations, DoR was more favorable in patients who received nivo-ipi compared to IO-TKI. A meta-analysis based on differences in RMST is a useful alternative whenever the proportional hazard assumption is violated. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/ , identifier CRD42021241421.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".