Python: How can I get lower 95% confidence interval using Kaplan-Meier estimate for survival curve estimate
Question:
Looks like we can use Surv and survfit function in R to get 95% lower bound confidence interval by using
surv.fit <- survfit(...) and get the 95% CI using
summary(surv.fit)
On Python, I tried something like kmf = KaplanMeierFitter() and kmf.fit(T, event_observed=C)
Is there a way to get 95% lower bound CI on Python like R did?
Thanks!
Answers:
Try using kmf.confidence_interval_.
Looks like we can use Surv and survfit function in R to get 95% lower bound confidence interval by using
surv.fit <- survfit(...) and get the 95% CI using
summary(surv.fit)
On Python, I tried something like kmf = KaplanMeierFitter() and kmf.fit(T, event_observed=C)
Is there a way to get 95% lower bound CI on Python like R did?
Thanks!
Try using kmf.confidence_interval_.