diff --git a/R/cast_mat_to_long_pem.R b/R/cast_mat_to_long_pem.R
index 2639487a..ad06f671 100644
--- a/R/cast_mat_to_long_pem.R
+++ b/R/cast_mat_to_long_pem.R
@@ -65,6 +65,7 @@ cast_mat_to_long_pem <- function(analysis_obj) {
                                  end = analysis_obj@outcome@time_var)
   names(long_df)[which(names(long_df) == "psb2__period")] <- "__period__"
   long_df[, analysis_obj@outcome@cens_var] <- 1 - long_df[, "psb2__status"]
+  long_df[, "time"] <- long_df[, "time"] - long_df[, "psb2__tstart"]
   long_df <- long_df[, c(cn, "__period__")]
   long_mat <- as.matrix(long_df)
 
diff --git a/tests/testthat/test-mcmc_sample-analysis.R b/tests/testthat/test-mcmc_sample-analysis.R
index f1e0a0fe..3b5a1e38 100644
--- a/tests/testthat/test-mcmc_sample-analysis.R
+++ b/tests/testthat/test-mcmc_sample-analysis.R
@@ -562,12 +562,18 @@ test_that("mcmc_sample for Analysis works for full borrowing, piecewise exponent
 
   full_pem_bayes <- mcmc_sample(full_pem_bayes_ao,
     iter_warmup = 2000,
-    iter_sampling = 10000,
-    chains = 4
+    iter_sampling = 5000,
+    chains = 2
   )
   expect_r6(full_pem_bayes, "CmdStanMCMC")
-  expect_equal(full_pem_bayes$summary("beta_trt")[[2]], pem_eha$coefficients[['trt']], tolerance = 0.1)
-  expect_equal(full_pem_bayes$summary("beta[1]")[[2]], pem_eha$coefficients[['cov1']], tolerance = 0.1)
-  expect_equal(full_pem_bayes$summary("beta[2]")[[2]], pem_eha$coefficients[['cov2']], tolerance = 0.1)
+  expect_equal(full_pem_bayes$summary("beta_trt")[[2]], pem_eha$coefficients[['trt']], tolerance = 0.05)
+  expect_equal(full_pem_bayes$summary("beta[1]")[[2]], pem_eha$coefficients[['cov1']], tolerance = 0.05)
+  expect_equal(full_pem_bayes$summary("beta[2]")[[2]], pem_eha$coefficients[['cov2']], tolerance = 0.05)
+
+  # Check that the cut points are the same
+  expect_equal(full_pem_bayes$summary("alpha[1]")[[2]], log(pem_eha$hazards[1]), tolerance = 0.05)
+  expect_equal(full_pem_bayes$summary("alpha[2]")[[2]], log(pem_eha$hazards[2]), tolerance = 0.05)
+  expect_equal(full_pem_bayes$summary("alpha[3]")[[2]], log(pem_eha$hazards[3]), tolerance = 0.05)
+  expect_equal(full_pem_bayes$summary("alpha[4]")[[2]], log(pem_eha$hazards[4]), tolerance = 0.05)
 
 })