# include median confidence interval in estimates?

See original GitHub issueHello,

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.

### Issue Analytics

- State:
- Created 7 years ago
- Comments:7 (3 by maintainers)

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## Top GitHub Comments

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

returns two values, 1.383272 & 1.418151