def KM_median(array,
upper_lim_flags,
left_censor=True,
return_type='percentile'):
kmf = KaplanMeierFitter()
if upper_lim_flags is not None:
if left_censor == True:
kmf.fit_left_censoring(array, upper_lim_flags)
else:
kmf.fit(array, event_observed=upper_lim_flags) #right censoring
else:
kmf.fit(array, upper_lim_flags)
median = median_survival_times(kmf.survival_function_)
if return_type == 'percentile':
upper_perc = kmf.percentile(0.25)
lower_perc = kmf.percentile(0.75)
print(
f'median and 1st/3rd quartiles: {median}, {lower_perc}, {upper_perc}'
)
return median, upper_perc, lower_perc
elif return_type == 'ci':
median_ci = median_survival_times(kmf.confidence_interval_).values
print(f'median and CI: {median}, {median_ci}')
return median, median_ci[0][0], median_ci[0][1]
elif return_type == 'median':
return median
