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Reanalysis of Urothelial Cancer Chemoimmunotherapy Trials With Differential Censoring

2025· article· en· W4406732473 on OpenAlexaff
Tomer Meirson, Jonathan Ofer, Noa Zimhony‐Nissim, Avital Bareket‐Samish, Gal Markel, Victoria Neiman, Nathan I. Cherny, Daniel A. Goldstein, Bishal Gyawali, Ian F. Tannock, Eli Rosenbaum

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreQueen's University
Fundersnot available
KeywordsCensoring (clinical trials)MedicineOncologyClinical trialSurvival analysisRandomized controlled trialInternal medicinePathology

Abstract

fetched live from OpenAlex

Importance: Three similar phase 3 randomized clinical trials have investigated PD-1/PD-L1 (programmed cell death 1 protein/programmed cell death 1 ligand 1) inhibitors in combination with platinum-based chemotherapy vs chemotherapy alone as first-line treatment for advanced urothelial carcinoma (IMvigor130, atezolizumab; KEYNOTE-361, pembrolizumab; and CheckMate901, nivolumab). Only CheckMate901 reported overall survival (OS) benefit for the combination. The reason for these inconsistent results is unclear. Objective: To explore whether differential censoring-that is, censoring imbalance between the study groups-is a possible explanation for these inconsistent findings. Design, Setting, and Participants: This comparative effectiveness study involved a censoring analysis of data from IMvigor130, KEYNOTE-361, and CheckMate901, which enrolled patients between 2016 and 2022. Participants included patients in these 3 trials. Exposure: Participation in 1 of the 3 trials. Main Outcomes and Measures: The primary outcomes were censoring rates adjusted for treatment effects. Censoring rates were calculated from the Kaplan-Meier (KM) curves. When excess censoring in the control group of open-label trials was found, the hypothesis was that better-performing patients might be dropping out to seek alternative treatments; a sensitivity analysis was conducted in which their survival was assumed to be similar to that of the longest surviving patients in the control group. Treatment effects of the censoring-adjusted KM curves were calculated using the 2-sided log-rank test. Results: The 3 trials involved a total of 2162 patients (1640 male [76%]; age range, 65-69 years) Analysis of progression-free survival (PFS) curves demonstrated no differential censoring in IMvigor130, but there was more than 30% excess censoring in the chemotherapy-only groups in KEYNOTE-361 and CheckMate901 trials. After sensitivity analysis, the PFS benefit was no longer significant in either study (KEYNOTE-361, adjusted hazard ratio [HR], 1.13 [95% CI, 0.95-1.35]; CheckMate901, adjusted HR, 1.17 [0.96-1.44]). Analysis of OS curves demonstrated no differential censoring in IMvigor130 or KEYNOTE-361, but there was more censoring in the chemotherapy-only group in CheckMate901. After sensitivity analysis, the OS benefit of adding nivolumab to chemotherapy was lost (before adjustment, HR, 0.77 [95% CI, 0.63-0.95]; P = .01; adjusted HR, 0.95 [95% CI, 0.77-1.17]; P = .64). Conclusions and Relevance: In this comparative effectiveness study, differential censoring explained the inconsistent results reported in the evaluated trials. The term perceived-inferiority censoring is suggested to describe a phenomenon wherein better-performing patients are aware of their treatment and drop out to pursue alternative therapeutic options; it is possible that this occurred in the open-label KEYNOTE-361 and CheckMate901 trials. Such censoring confounds randomization and interpretation of clinical trials, since a larger experimental group is compared with a selected group of controls with poorer prognosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.373
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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