diff --git a/lymph/models/bilateral.py b/lymph/models/bilateral.py
index e710f68..080f8f0 100644
--- a/lymph/models/bilateral.py
+++ b/lymph/models/bilateral.py
@@ -696,36 +696,57 @@ def risk(
         )
 
 
-    def generate_dataset(
+    def draw_patients(
         self,
-        num_patients: int,
-        stage_dist: dict[str, float],
+        num: int,
+        stage_dist: Iterable[float],
+        rng: np.random.Generator | None = None,
+        seed: int = 42,
+        **_kwargs,
     ) -> pd.DataFrame:
-        """Generate/sample a pandas :class:`DataFrame` from the defined network.
+        """Draw ``num`` random patients from the parametrized model.
 
-        Args:
-            num_patients: Number of patients to generate.
-            stage_dist: Probability to find a patient in a certain T-stage.
+        See Also:
+            :py:meth:`lymph.diagnose_times.Distribution.draw_diag_times`
+                Method to draw diagnose times from a distribution.
+            :py:meth:`lymph.models.Unilateral.draw_diagnoses`
+                Method to draw individual diagnoses from a unilateral model.
+            :py:meth:`lymph.models.Unilateral.draw_patients`
+                The unilateral method to draw a synthetic dataset.
         """
-        # TODO: check if this still works
-        drawn_t_stages, drawn_diag_times = self.diag_time_dists.draw(
-            dist=stage_dist, size=num_patients
+        if rng is None:
+            rng = np.random.default_rng(seed)
+
+        if sum(stage_dist) != 1.:
+            warnings.warn("Sum of stage distribution is not 1. Renormalizing.")
+            stage_dist = np.array(stage_dist) / sum(stage_dist)
+
+        drawn_t_stages = rng.choice(
+            a=list(self.diag_time_dists.keys()),
+            p=stage_dist,
+            size=num,
         )
+        drawn_diag_times = [
+            self.diag_time_dists[t_stage].draw_diag_times(rng=rng)
+            for t_stage in drawn_t_stages
+        ]
 
-        drawn_obs_ipsi = self.ipsi.draw_diagnoses(drawn_diag_times)
-        drawn_obs_contra = self.contra.draw_diagnoses(drawn_diag_times)
+        drawn_obs_ipsi = self.ipsi.draw_diagnoses(drawn_diag_times, rng=rng)
+        drawn_obs_contra = self.contra.draw_diagnoses(drawn_diag_times, rng=rng)
         drawn_obs = np.concatenate([drawn_obs_ipsi, drawn_obs_contra], axis=1)
 
-        # construct MultiIndex for dataset from stored modalities
+        # construct MultiIndex with "ipsi" and "contra" at top level to allow
+        # concatenation of the two separate drawn diagnoses
         sides = ["ipsi", "contra"]
-        modalities = list(self.modalities.keys())
-        lnl_names = [lnl.name for lnl in self.ipsi.graph._lnls]
-        multi_cols = pd.MultiIndex.from_product([sides, modalities, lnl_names])
+        modality_names = list(self.modalities.keys())
+        lnl_names = [lnl for lnl in self.ipsi.graph.lnls.keys()]
+        multi_cols = pd.MultiIndex.from_product([sides, modality_names, lnl_names])
 
-        # create DataFrame
+        # reorder the column levels and thus also the individual columns to match the
+        # LyProX format without mixing up the data
         dataset = pd.DataFrame(drawn_obs, columns=multi_cols)
         dataset = dataset.reorder_levels(order=[1, 0, 2], axis="columns")
         dataset = dataset.sort_index(axis="columns", level=0)
-        dataset[('info', 'tumor', 't_stage')] = drawn_t_stages
+        dataset[('tumor', '1', 't_stage')] = drawn_t_stages
 
         return dataset
diff --git a/lymph/models/unilateral.py b/lymph/models/unilateral.py
index 2323e5f..de21f9b 100644
--- a/lymph/models/unilateral.py
+++ b/lymph/models/unilateral.py
@@ -975,7 +975,7 @@ def draw_patients(
         seed: int = 42,
         **_kwargs,
     ) -> pd.DataFrame:
-        """Draw a ``num`` random patients from the model.
+        """Draw ``num`` random patients from the model.
 
         For this, a ``stage_dist``, i.e., a distribution over the T-stages, needs to
         be defined. This must be an iterable of probabilities with as many elements as
@@ -983,6 +983,14 @@ def draw_patients(
 
         A random number generator can be provided as ``rng``. If ``None``, a new one
         is initialized with the given ``seed`` (or ``42``, by default).
+
+        See Also:
+            :py:meth:`lymph.diagnose_times.Distribution.draw_diag_times`
+                Method to draw diagnose times from a distribution.
+            :py:meth:`lymph.models.Unilateral.draw_diagnoses`
+                Method to draw individual diagnoses.
+            :py:meth:`lymph.models.Bilateral.draw_patients`
+                The corresponding bilateral method.
         """
         if rng is None:
             rng = np.random.default_rng(seed)
@@ -1001,14 +1009,12 @@ def draw_patients(
             for t_stage in drawn_t_stages
         ]
 
-        drawn_obs = self.draw_diagnoses(drawn_diag_times)
+        drawn_obs = self.draw_diagnoses(drawn_diag_times, rng=rng)
 
-        # construct MultiIndex for dataset from stored modalities
         modality_names = list(self.modalities.keys())
         lnl_names = list(self.graph.lnls.keys())
         multi_cols = pd.MultiIndex.from_product([modality_names, ["ipsi"], lnl_names])
 
-        # create DataFrame
         dataset = pd.DataFrame(drawn_obs, columns=multi_cols)
         dataset[("tumor", "1", "t_stage")] = drawn_t_stages