Update censoring

docs/conf.py

@@ -4,6 +4,7 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
 import importlib.metadata
+
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 import os

pySEQTarget/SEQuential.py

@@ -7,19 +7,36 @@
 import numpy as np
 import polars as pl
 
-from .analysis import (_calculate_hazard, _calculate_survival, _outcome_fit,
-                       _pred_risk, _risk_estimates, _subgroup_fit)
+from .analysis import (
+    _calculate_hazard,
+    _calculate_survival,
+    _outcome_fit,
+    _pred_risk,
+    _risk_estimates,
+    _subgroup_fit,
+)
 from .error import _datachecker, _param_checker
 from .expansion import _binder, _diagnostics, _dynamic, _random_selection
 from .helpers import _col_string, _format_time, bootstrap_loop
-from .initialization import (_cense_denominator, _cense_numerator,
-                             _denominator, _numerator, _outcome)
+from .initialization import (
+    _cense_denominator,
+    _cense_numerator,
+    _denominator,
+    _numerator,
+    _outcome,
+)
 from .plot import _survival_plot
 from .SEQopts import SEQopts
 from .SEQoutput import SEQoutput
-from .weighting import (_fit_denominator, _fit_LTFU, _fit_numerator,
-                        _weight_bind, _weight_predict, _weight_setup,
-                        _weight_stats)
+from .weighting import (
+    _fit_denominator,
+    _fit_LTFU,
+    _fit_numerator,
+    _weight_bind,
+    _weight_predict,
+    _weight_setup,
+    _weight_stats,
+)
 
 
 class SEQuential:

pySEQTarget/analysis/__init__.py

@@ -3,6 +3,5 @@
 from ._risk_estimates import _risk_estimates as _risk_estimates
 from ._subgroup_fit import _subgroup_fit as _subgroup_fit
 from ._survival_pred import _calculate_survival as _calculate_survival
-from ._survival_pred import \
-    _get_outcome_predictions as _get_outcome_predictions
+from ._survival_pred import _get_outcome_predictions as _get_outcome_predictions
 from ._survival_pred import _pred_risk as _pred_risk

pySEQTarget/analysis/_outcome_fit.py

@@ -20,6 +20,9 @@ def _outcome_fit(
             )
         )
 
+    if self.method == "censoring":
+        df = df.filter(pl.col("switch") != 1)
+
     df_pd = df.to_pandas()
 
     df_pd[self.treatment_col] = df_pd[self.treatment_col].astype("category")

pySEQTarget/weighting/_weight_fit.py

@@ -65,7 +65,7 @@ def _fit_denominator(self, WDT):
             DT_subset = WDT
         if self.weight_lag_condition:
             DT_subset = DT_subset[DT_subset["tx_lag"] == level]
-        if not self.weight_preexpansion and not self.excused:
+        if not self.weight_preexpansion:
             DT_subset = DT_subset[DT_subset["followup"] != 0]
         if self.weight_eligible_colnames[i] is not None:
             DT_subset = DT_subset[DT_subset[self.weight_eligible_colnames[i]] == 1]

tests/test_coefficients.py

@@ -120,14 +120,15 @@ def test_PreE_censoring_coefs():
     s.fit()
     matrix = s.outcome_model[0]["outcome"].summary2().tables[1]["Coef."].to_list()
     expected = [
-        -4.818288687908951,
-        0.511665606890965,
-        0.062028316788368384,
-        0.025489681857269905,
-        0.00018215948440046585,
-        -0.014019017637918164,
-        0.001110238926667272,
+        -4.872373936951975,
+        0.48389186624295133,
+        0.0477349453301334,
+        0.029127276869076173,
+        4.784054961154824e-05,
+        -0.013614654772205668,
+        0.0011281734101133744,
     ]
+
     assert [round(x, 3) for x in matrix] == [round(x, 3) for x in expected]
 
 
@@ -150,16 +151,16 @@ def test_PostE_censoring_coefs():
     s.fit()
     matrix = s.outcome_model[0]["outcome"].summary2().tables[1]["Coef."].to_list()
     expected = [
-        -7.9113179326280445,
-        0.49092190701455873,
-        0.08903087485402544,
-        0.026160806382879903,
-        0.00019078148503570062,
-        0.04445697224987294,
-        0.0007051968052005897,
-        0.004316239095295115,
-        0.013762799304812959,
-        0.3196331024454665,
+        -9.172266785106519,
+        0.4707554720116625,
+        0.08162617232478116,
+        0.029021087196430605,
+        7.8937226861939e-05,
+        0.06700192286925702,
+        0.0005834323664644191,
+        0.004870212765388434,
+        0.013503198983327514,
+        0.4466573801510379,
     ]
     assert [round(x, 3) for x in matrix] == [round(x, 3) for x in expected]
 
@@ -188,13 +189,14 @@ def test_PreE_censoring_excused_coefs():
     s.fit()
     matrix = s.outcome_model[0]["outcome"].summary2().tables[1]["Coef."].to_list()
     expected = [
-        -6.175691049418418,
-        1.3493634846413598,
-        0.1072284696749134,
-        -0.003977965364113033,
-        0.06959432825811135,
-        -0.00034297574787048573,
+        -6.460912082691973,
+        1.309708035546933,
+        0.10853511682679658,
+        -0.0038913520688693823,
+        0.08849129909709463,
+        -0.000647578869153453,
     ]
+    print(matrix)
     assert [round(x, 3) for x in matrix] == [round(x, 3) for x in expected]
 
 
@@ -222,16 +224,16 @@ def test_PostE_censoring_excused_coefs():
     s.fit()
     matrix = s.outcome_model[0]["outcome"].summary2().tables[1]["Coef."].to_list()
     expected = [
-        -7.126398786875262,
-        0.2632047482928519,
-        0.13345454814736696,
-        0.03967181206032395,
-        -0.00033089446793392585,
-        0.03763545026332514,
-        0.0007588725152627089,
-        0.0036793093608788923,
-        -0.022372677571544992,
-        0.2441842617520696,
+        -6.782732929102242,
+        0.26371172100905477,
+        0.13625528598217598,
+        0.040580427030886,
+        -0.000343018323531494,
+        0.031185150775465315,
+        0.000784356550754563,
+        0.004338417236024277,
+        -0.013052187516528172,
+        0.20402680950820007,
     ]
     assert [round(x, 3) for x in matrix] == [round(x, 3) for x in expected]
 
@@ -358,12 +360,12 @@ def test_weighted_multinomial():
     s.fit()
     matrix = s.outcome_model[0]["outcome"].summary2().tables[1]["Coef."].to_list()
     expected = [
-        -111.35419661939163,
-        -12.571187230338328,
-        9.234157699403015,
-        -0.6336774763031923,
-        0.016754692338530056,
-        5.8240772329087225,
-        -0.08598454090661659,
+        -109.99715622379995,
+        -12.536816769546702,
+        9.22013733949143,
+        -0.6129380297017852,
+        0.01597877250531723,
+        5.743984176710672,
+        -0.08478678955657822,
     ]
     assert [round(x, 3) for x in matrix] == [round(x, 3) for x in expected]