diff --git a/src/rtichoke/calibration/calibration.py b/src/rtichoke/calibration/calibration.py
index c809ed6d..0d102d45 100644
--- a/src/rtichoke/calibration/calibration.py
+++ b/src/rtichoke/calibration/calibration.py
@@ -106,19 +106,25 @@ def create_calibration_curve_times(
             competing events as censored, which calibration does not support.
     """
 
-    unsupported = [
-        heuristics
+    real_values = reals.values() if isinstance(reals, dict) else [reals]
+    has_censoring = any(np.any(np.asarray(values) == 0) for values in real_values)
+
+    unsupported_adjusted_censoring = has_censoring and any(
+        heuristics.get("censoring_heuristic") == "adjusted"
         for heuristics in heuristics_sets
-        if heuristics.get("censoring_heuristic") == "adjusted"
-        or heuristics.get("competing_heuristic") == "adjusted_as_censored"
-    ]
-    if unsupported:
+    )
+    unsupported_competing_as_censored = any(
+        heuristics.get("competing_heuristic") == "adjusted_as_censored"
+        for heuristics in heuristics_sets
+    )
+
+    if unsupported_adjusted_censoring or unsupported_competing_as_censored:
         raise ValueError(
             "Unsupported calibration heuristics: "
             "create_calibration_curve_times() does not support "
-            "censoring_heuristic='adjusted' or "
-            "competing_heuristic='adjusted_as_censored'. "
-            "Use a supported heuristic combination such as "
+            "censoring_heuristic='adjusted' when censored observations are present, "
+            "or competing_heuristic='adjusted_as_censored'. "
+            "When censoring is present, use a supported heuristic combination such as "
             "censoring_heuristic='excluded' with "
             "competing_heuristic='adjusted_as_negative'."
         )
@@ -1091,6 +1097,7 @@ def _create_calibration_curve_list_times(
     """
     # Part 1: Prepare initial dataframe from inputs
     initial_df = _build_initial_df_for_times(probs, reals, times)
+    has_censoring = initial_df.filter(pl.col("real") == 0).height > 0
 
     # Part 2: Iterate and generate calibration data for each horizon/heuristic
     all_deciles = []
@@ -1105,7 +1112,7 @@ def _create_calibration_curve_list_times(
             competing_heuristic = heuristics["competing_heuristic"]
 
             if (
-                censoring_heuristic == "adjusted"
+                (censoring_heuristic == "adjusted" and has_censoring)
                 or competing_heuristic == "adjusted_as_censored"
             ):
                 continue
diff --git a/tests/test_calibration_times.py b/tests/test_calibration_times.py
index 6fbc86ed..5be7d7e3 100644
--- a/tests/test_calibration_times.py
+++ b/tests/test_calibration_times.py
@@ -64,24 +64,58 @@ def test_create_calibration_curve_times_unequal_size_populations():
     "entry_point",
     [create_calibration_curve_times_top_level, create_calibration_curve_times_direct],
 )
+def test_create_calibration_curve_times_allows_adjusted_without_censoring(entry_point):
+    probs = {"model_1": np.array([0.1, 0.2, 0.3, 0.4])}
+    reals = np.array([1, 1, 1, 1])
+    times = np.array([1.0, 2.0, 3.0, 4.0])
+
+    fig = entry_point(
+        probs,
+        reals,
+        times,
+        fixed_time_horizons=[2.0],
+        heuristics_sets=[
+            {
+                "censoring_heuristic": "adjusted",
+                "competing_heuristic": "adjusted_as_negative",
+            }
+        ],
+    )
+
+    assert fig is not None
+
+
 @pytest.mark.parametrize(
-    "heuristics",
-    [
-        {
-            "censoring_heuristic": "adjusted",
-            "competing_heuristic": "adjusted_as_negative",
-        },
-        {
-            "censoring_heuristic": "excluded",
-            "competing_heuristic": "adjusted_as_censored",
-        },
-    ],
+    "entry_point",
+    [create_calibration_curve_times_top_level, create_calibration_curve_times_direct],
+)
+def test_create_calibration_curve_times_rejects_adjusted_with_censoring(entry_point):
+    probs = {"model_1": np.array([0.1, 0.2, 0.3, 0.4])}
+    reals = np.array([0, 1, 1, 1])
+    times = np.array([1.0, 2.0, 3.0, 4.0])
+
+    with pytest.raises(ValueError, match="Unsupported calibration heuristics"):
+        entry_point(
+            probs,
+            reals,
+            times,
+            fixed_time_horizons=[2.0],
+            heuristics_sets=[
+                {
+                    "censoring_heuristic": "adjusted",
+                    "competing_heuristic": "adjusted_as_negative",
+                }
+            ],
+        )
+
+
+@pytest.mark.parametrize(
+    "entry_point",
+    [create_calibration_curve_times_top_level, create_calibration_curve_times_direct],
 )
-def test_create_calibration_curve_times_rejects_unsupported_heuristics(
-    entry_point, heuristics
-):
+def test_create_calibration_curve_times_rejects_competing_as_censored(entry_point):
     probs = {"model_1": np.array([0.1, 0.2, 0.3, 0.4])}
-    reals = np.array([0, 1, 0, 1])
+    reals = np.array([1, 1, 1, 1])
     times = np.array([1.0, 2.0, 3.0, 4.0])
 
     with pytest.raises(ValueError, match="Unsupported calibration heuristics"):
@@ -90,5 +124,10 @@ def test_create_calibration_curve_times_rejects_unsupported_heuristics(
             reals,
             times,
             fixed_time_horizons=[2.0],
-            heuristics_sets=[heuristics],
+            heuristics_sets=[
+                {
+                    "censoring_heuristic": "excluded",
+                    "competing_heuristic": "adjusted_as_censored",
+                }
+            ],
         )