diff --git a/yli/__init__.py b/yli/__init__.py
index c49b56e..1afea8d 100644
--- a/yli/__init__.py
+++ b/yli/__init__.py
@@ -18,7 +18,7 @@ from .bayes_factors import bayesfactor_afbf
 from .config import config
 from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
 from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
-from .regress import PenalisedLogit, regress, vif
+from .regress import PenalisedLogit, logit_then_regress, regress, vif
 from .sig_tests import anova_oneway, chi2, mannwhitney, pearsonr, ttest_ind
 
 def reload_me():
diff --git a/yli/regress.py b/yli/regress.py
index eeb7cc6..ba951ab 100644
--- a/yli/regress.py
+++ b/yli/regress.py
@@ -462,6 +462,21 @@ def regress(
 		exp
 	)
 
+def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kwargs):
+	"""Perform logistic regression, then use parameters as start parameters for desired regression"""
+	
+	# Check for/clean NaNs
+	# Do this once here so we only get 1 warning
+	df = df[[dep] + cols_for_formula(formula, df)]
+	df = check_nan(df, nan_policy)
+	
+	# Perform logistic regression
+	logit_result = regress(sm.Logit, df, dep, formula, **kwargs)
+	logit_params = logit_result.raw_result.params
+	
+	# Perform desired regression
+	return regress(model_class, df, dep, formula, start_params=logit_params, **kwargs)
+
 # -----------------------------
 # Penalised logistic regression