include median confidence interval in estimates?

Hello,

Would it make sense to include median confidence intervals in the estimates? For example, in the object returned from KaplanMeierFitter(), after fitting, one could do:

# get the confidence interval of the median
# start by finding the median on the timeline
median_ci_idx = kmf.confidence_interval_.index.get_loc(median_lifespan,
                                                       method='nearest')
# then get the confidence interval at the point of the median
median_ci = kmf.confidence_interval_.iloc[median_ci_idx]
# to find the confidence interval for the value of the median
# we will get a horizontal line passing through the value of the
# median; note that the greater value comes earlier, and the smaller later
lcl = (kmf.survival_function_['KM_estimate']
       <= median_ci.loc['KM_estimate_upper_0.95']).idxmax(0)    
ucl = (kmf.survival_function_['KM_estimate']
       <= median_ci.loc['KM_estimate_lower_0.95']).idxmax(0)

This is what is essentially done by R, as explained in print.survfit.

[robertcv]

I am sorry, now I see why it didn’t work for me. The function behaves differently depending on what you have on the input. I used median_survival_times(kmf) instead of median_survival_times(kmf.confidence_interval_) and only got the median time. I would suggest updating the documentation on this function. The parameters description uses “or” and I thought that it makes no difference if I use the model class or the confidence interval DataFrame.

[CamDavidsonPilon]

I’m not sure I’m seeing the same thing as you:

from lifelines.utils import median_survival_times
kmf = KaplanMeierFitter().fit(np.random.exponential(2, size=50000))
print(median_survival_times(kmf.confidence_interval_))

returns two values, 1.383272 & 1.418151