diff --git a/evaluation_age_time_dependent.py b/evaluation_age_time_dependent.py
index a4cf12a..8dafb7f 100644
--- a/evaluation_age_time_dependent.py
+++ b/evaluation_age_time_dependent.py
@@ -131,18 +131,24 @@ def aggregate_age_bin_results(df_by_bin: pd.DataFrame) -> pd.DataFrame:
 
     group_keys = ["mc_idx", "horizon_tau", "topk_percent", "cause_id"]
 
-    df_mc_macro = (
-        df_by_bin.groupby(group_keys)
-        .apply(lambda g: _bin_aggregate(g, weighted=False))
-        .reset_index()
-    )
+    gb = df_by_bin.groupby(group_keys)
+
+    try:
+        df_mc_macro = gb.apply(
+            lambda g: _bin_aggregate(g, weighted=False), include_groups=False
+        ).reset_index()
+    except TypeError:  # pandas<2.2 (no include_groups)
+        df_mc_macro = gb.apply(lambda g: _bin_aggregate(
+            g, weighted=False)).reset_index()
     df_mc_macro["agg_type"] = "macro"
 
-    df_mc_weighted = (
-        df_by_bin.groupby(group_keys)
-        .apply(lambda g: _bin_aggregate(g, weighted=True))
-        .reset_index()
-    )
+    try:
+        df_mc_weighted = gb.apply(
+            lambda g: _bin_aggregate(g, weighted=True), include_groups=False
+        ).reset_index()
+    except TypeError:  # pandas<2.2 (no include_groups)
+        df_mc_weighted = gb.apply(
+            lambda g: _bin_aggregate(g, weighted=True)).reset_index()
     df_mc_weighted["agg_type"] = "weighted"
 
     df_mc_binagg = pd.concat([df_mc_macro, df_mc_weighted], ignore_index=True)