TrialCurveEV-302 (pan-Asian subgroup): reconstructed Kaplan–Meier curves
Pan-Asian subgroup analysis of EV-302/KEYNOTE-A39: a phase 3 study to evaluate enfortumab vedotin and pembrolizumab in patients with untreated advanced urothelial carcinoma. Curves rebuilt as patient-level data (Guyot 2012) and checked against the statistics printed on the figure.
| Publication | Int J Clin Oncol. 2026 Jan 21;31(3):436–46. doi: 10.1007/s10147-025-02950-8 |
|---|---|
| DOI | 10.1007/s10147-025-02950-8 |
| Registry | NCT04223856 |
| Phase | Phase 3 |
| Conditions | Urothelial Cancer |
| Interventions | Enfortumab vedotin, Pembrolizumab, Cisplatin, Carboplatin, Gemcitabine |
| Sponsor | Astellas Pharma Global Development, Inc. |
| Enrolment | 886 |
| Source licence | Figure published under CC BY 4.0 |
Progression-free survival by BICR — Pan-Asian population
From figure panel A · validated against the values printed on the figure · curve source: pixel measurement confirmed by vision model
Reconstructed from the published curve. Median: Enfortumab vedotin–pembrolizumab 23.8 months; Chemotherapy 6.4 months. Survival at 12 months: Enfortumab vedotin–pembrolizumab 66%; Chemotherapy 29%. Hazard ratio Enfortumab vedotin–pembrolizumab vs Chemotherapy 0.37 (95% CI 0.24–0.56).
| Arm | N | Events | Median (Months) |
|---|---|---|---|
| Enfortumab vedotin–pembrolizumab | 94 | 44 | 23.8 |
| Chemotherapy | 82 | 52 | 6.4 |
- Enfortumab vedotin–pembrolizumab vs Chemotherapy: reconstructed HR 0.37 (95% CI 0.24–0.56)
Landmark survival (reconstructed)
| Time | Enfortumab vedotin–pembrolizumab | Chemotherapy |
|---|---|---|
| 6 mo | 80.4% | 58.2% |
| 12 mo | 66.4% | 28.8% |
| 18 mo | 61.3% | 20.6% |
| 24 mo | 49.1% | 18.3% |
| 36 mo | 44.9% | — |
Checks against the published figure
- Enfortumab vedotin–pembrolizumab median: printed 23.8, reconstructed 23.8 pass
- Chemotherapy median: printed 6.3, reconstructed 6.43 pass
- Enfortumab vedotin–pembrolizumab events: printed 44, reconstructed 44 pass
- Enfortumab vedotin–pembrolizumab n: printed 94, reconstructed 94 pass
- Chemotherapy events: printed 52, reconstructed 52 pass
- Chemotherapy n: printed 82, reconstructed 82 pass
- hazard ratio (direction inferred): printed 0.37, reconstructed 0.368 pass
Overall survival — Pan-Asian population
From figure panel B · validated against the values printed on the figure · curve source: pixel measurement confirmed by vision model
Reconstructed from the published curve. Median: Enfortumab vedotin–pembrolizumab not reached; Chemotherapy 17.9 months. Survival at 12 months: Enfortumab vedotin–pembrolizumab 88%; Chemotherapy 65%. Hazard ratio Enfortumab vedotin–pembrolizumab vs Chemotherapy 0.33 (95% CI 0.20–0.54).
| Arm | N | Events | Median (Months) |
|---|---|---|---|
| Enfortumab vedotin–pembrolizumab | 94 | 24 | not reached |
| Chemotherapy | 82 | 45 | 17.9 |
- Enfortumab vedotin–pembrolizumab vs Chemotherapy: reconstructed HR 0.33 (95% CI 0.20–0.54)
Landmark survival (reconstructed)
| Time | Enfortumab vedotin–pembrolizumab | Chemotherapy |
|---|---|---|
| 6 mo | 93.5% | 84.2% |
| 12 mo | 87.9% | 65.1% |
| 18 mo | 78.9% | 48.5% |
| 24 mo | 75.1% | 41.2% |
| 36 mo | 71.9% | 36.4% |
Checks against the published figure
- Chemotherapy median: printed 18, reconstructed 17.9 pass
- Enfortumab vedotin–pembrolizumab events: printed 24, reconstructed 24 pass
- Enfortumab vedotin–pembrolizumab n: printed 94, reconstructed 94 pass
- Chemotherapy events: printed 45, reconstructed 45 pass
- Chemotherapy n: printed 82, reconstructed 82 pass
- hazard ratio (direction inferred): printed 0.33, reconstructed 0.326 pass
About these data
Pseudo-IPD reproduces the published curves and numbers at risk; the rows are not real patients. Cite the publication (DOI above) and TrialCurve. The source figure is © the authors (CC BY 4.0); the charts here are redrawn from the reconstruction.
Load these data in R or Python
# R
library(survival)
d <- read.csv("https://trialcurve.com/data/trials/ev-302-pan-asian-subgroup-1.csv")
survfit(Surv(time, event) ~ arm, data = d)
coxph(Surv(time, event) ~ arm, data = d)
# Python
import pandas as pd
from lifelines import KaplanMeierFitter
d = pd.read_csv("https://trialcurve.com/data/trials/ev-302-pan-asian-subgroup-1.csv")
for arm, g in d.groupby("arm"):
KaplanMeierFitter().fit(g.time, g.event, label=arm).plot_survival_function()
Suggested citation: Int J Clin Oncol. 2026 Jan 21;31(3):436–46. doi: 10.1007/s10147-025-02950-8 doi:10.1007/s10147-025-02950-8. Reconstructed data: TrialCurve (2026), https://trialcurve.com/trials/ev-302-pan-asian-subgroup
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