TrialCurveCLEAR (Japanese subgroup): reconstructed Kaplan–Meier curves
Lenvatinib plus pembrolizumab versus sunitinib for advanced renal cell carcinoma: Japanese patients from the CLEAR study. Curves rebuilt as patient-level data (Guyot 2012) and checked against the statistics printed on the figure.
| Publication | Cancer Med. 2022 Dec 1;12(6):6902–12. doi: 10.1002/cam4.5483 |
|---|---|
| DOI | 10.1002/cam4.5483 |
| Registry | — |
| Phase | — |
| Conditions | — |
| Interventions | — |
| Sponsor | — |
| Enrolment | — |
| Source licence | Figure published under CC BY 4.0 |
Progression-free survival (independent imaging review per RECIST v1.1) — Japanese patients
From figure panel · validated against the values printed on the figure · curve source: vision model
Reconstructed from the published curve. Median: LEN + PEMBRO 22.1 months; SUN 10.9 months. Survival at 12 months: LEN + PEMBRO 67%; SUN 29%. Hazard ratio LEN + PEMBRO vs SUN 0.38 (95% CI 0.20–0.74).
| Arm | N | Events | Median (Months) |
|---|---|---|---|
| LEN + PEMBRO | 42 | 19 | 22.1 |
| SUN | 31 | 18 | 10.9 |
- LEN + PEMBRO vs SUN: reconstructed HR 0.38 (95% CI 0.20–0.74)
Landmark survival (reconstructed)
| Time | LEN + PEMBRO | SUN |
|---|---|---|
| 6 mo | 82.6% | 65.5% |
| 12 mo | 66.7% | 29.3% |
| 18 mo | 55.5% | 23.4% |
| 24 mo | 49.9% | 0.0% |
Checks against the published figure
- LEN + PEMBRO median: printed 22.1, reconstructed 22.1 pass
- SUN median: printed 10.9, reconstructed 10.9 pass
- LEN + PEMBRO events: printed 19, reconstructed 19 pass
- LEN + PEMBRO n: printed 42, reconstructed 42 pass
- SUN events: printed 19, reconstructed 18 pass
- SUN n: printed 31, reconstructed 31 pass
- hazard ratio: printed 0.39, reconstructed 0.381 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/clear-japanese-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/clear-japanese-subgroup-1.csv")
for arm, g in d.groupby("arm"):
KaplanMeierFitter().fit(g.time, g.event, label=arm).plot_survival_function()
Suggested citation: Cancer Med. 2022 Dec 1;12(6):6902–12. doi: 10.1002/cam4.5483 doi:10.1002/cam4.5483. Reconstructed data: TrialCurve (2026), https://trialcurve.com/trials/clear-japanese-subgroup
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