diff --git a/NEWS.md b/NEWS.md
index 23cf1daa..3dabe7b0 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,6 +25,8 @@ intercept only models by allowing data frames with 0 columns and multiple rows.
 
 * Effective number of parameters are computed for K-fold CV not just LOO CV. (#462)
 
+* `stan_clogit()` now allows outcome variable to be a factor. (#520)
+
 # rstanarm 2.21.1
 
 * Compatible with rstan v2.21.1
diff --git a/R/stan_clogit.R b/R/stan_clogit.R
index 63d0f7ed..ecc703c4 100644
--- a/R/stan_clogit.R
+++ b/R/stan_clogit.R
@@ -125,8 +125,7 @@ stan_clogit <- function(formula, data, subset, na.action = NULL, contrasts = NUL
     group <- glmod$reTrms
     group$strata <- glmod$strata <- as.factor(mf[,"(weights)"])
     group$decov <- prior_covariance
-  }
-  else {
+  } else {
     validate_glm_formula(formula)
     mf[[1L]] <- as.name("model.frame")
     mf$drop.unused.levels <- TRUE
@@ -137,6 +136,9 @@ stan_clogit <- function(formula, data, subset, na.action = NULL, contrasts = NUL
     Y <- array1D_check(model.response(mf, type = "any"))
   }
   contrasts <- attr(X, "contrasts")
+  if (is.factor(Y)) {
+    Y <- fac2bin(Y)
+  }
   
   ord <- order(group$strata)
   if (any(diff(ord) <= 0)) {
diff --git a/tests/testthat/test_stan_clogit.R b/tests/testthat/test_stan_clogit.R
index 442eca4f..cc0dbf7d 100644
--- a/tests/testthat/test_stan_clogit.R
+++ b/tests/testthat/test_stan_clogit.R
@@ -42,6 +42,16 @@ test_that("stan_clogit runs for infert example", {
   expect_stanreg(fit)
 })
 
+test_that("stan_clogit works when y is a factor", {
+  d <- infert[order(infert$stratum), ]
+  d$case <- factor(d$case, labels = c("A", "B"))
+  SW(fit_factor <- stan_clogit(case ~ spontaneous + induced, strata = stratum, prior = NULL,
+                        data = infert[order(infert$stratum), ], 
+                        QR = TRUE, init_r = 0.5,
+                        chains = CHAINS, iter = ITER, seed = SEED, refresh = 0))
+  expect_equal(coef(fit_factor), coef(fit))
+})
+
 test_that("stan_clogit throws error if data are not sorted", {
   expect_error(update(fit, data = infert), 
                regexp = "Data must be sorted")