Merge pull request #1575 from TheooJ/invert_confidence_intervals

switch confidence interval values
CamDavidsonPilon · Nov 14, 2023 · c9b136b · c9b136b
2 parents 716b204 + 2abf0dd
commit c9b136b
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Showing 2 changed files with 18 additions and 4 deletions.
diff --git a/lifelines/fitters/breslow_fleming_harrington_fitter.py b/lifelines/fitters/breslow_fleming_harrington_fitter.py
@@ -72,7 +72,14 @@ def fit(
         alpha = coalesce(alpha, self.alpha)
 
         naf = NelsonAalenFitter(alpha=alpha)
-        naf.fit(durations, event_observed=event_observed, timeline=timeline, label=self._label, entry=entry, ci_labels=ci_labels)
+        naf.fit(
+            durations,
+            event_observed=event_observed,
+            timeline=timeline,
+            label=self._label,
+            entry=entry,
+            ci_labels=ci_labels,
+        )
         self.durations, self.event_observed, self.timeline, self.entry, self.event_table, self.weights = (
             naf.durations,
             naf.event_observed,
@@ -87,6 +94,7 @@ def fit(
         self.confidence_interval_ = np.exp(-naf.confidence_interval_)
         self.confidence_interval_survival_function_ = self.confidence_interval_
         self.confidence_interval_cumulative_density = 1 - self.confidence_interval_
+        self.confidence_interval_cumulative_density[:] = np.fliplr(self.confidence_interval_cumulative_density.values)
 
         # estimation methods
         self._estimation_method = "survival_function_"

diff --git a/lifelines/fitters/kaplan_meier_fitter.py b/lifelines/fitters/kaplan_meier_fitter.py
@@ -351,9 +351,14 @@ def _fit(
         primary_estimate_name = "survival_function_"
         secondary_estimate_name = "cumulative_density_"
 
-        (self.durations, self.event_observed, self.timeline, self.entry, self.event_table, self.weights) = _preprocess_inputs(
-            durations, event_observed, timeline, entry, weights
-        )
+        (
+            self.durations,
+            self.event_observed,
+            self.timeline,
+            self.entry,
+            self.event_table,
+            self.weights,
+        ) = _preprocess_inputs(durations, event_observed, timeline, entry, weights)
 
         alpha = alpha if alpha else self.alpha
         log_estimate, cumulative_sq_ = _additive_estimate(
@@ -386,6 +391,7 @@ def _fit(
 
         self.confidence_interval_survival_function_ = self.confidence_interval_
         self.confidence_interval_cumulative_density_ = 1 - self.confidence_interval_
+        self.confidence_interval_cumulative_density_[:] = np.fliplr(self.confidence_interval_cumulative_density_.values)
         self._median = median_survival_times(self.survival_function_)
         self._cumulative_sq_ = cumulative_sq_