diff --git a/NEWS.md b/NEWS.md
index 0c30a2e..5c69d50 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,9 @@
+# appendMCP (development version)
+
+## Bug Fixes
+
+- Fixed enrollment-rate pooling in `get_info_tite()` and `get_dist_tite()`: arm-level enrollment is now aggregated within each stratum's enrollment periods (`group_by(stratum, rate, duration, index)`, matching `get_boundaries()`), instead of `group_by(rate, duration)`, which collapsed identical periods across strata. Previously, in multi-stratum designs where strata share period values — e.g. common zero-rate lead-in periods for a sub-study that opens late — all but the first stratum lost those periods and appeared to start enrolling at time 0, inflating expected events/information and therefore simulated power (Tables 6a/6b) for time-to-event hypotheses. The design boundaries and local power (Tables 1/4/5), computed via `get_boundaries()`, were unaffected.
+
 # appendMCP 0.3.0
 
 ## New Features
diff --git a/R/utils-process.R b/R/utils-process.R
index 8dd83f2..0c9035a 100644
--- a/R/utils-process.R
+++ b/R/utils-process.R
@@ -421,9 +421,9 @@ get_info_tite <- function(control, test, enroll_rate, fail_rate,
                     stratum      = .$stratum)}
   gsDesign2::ahr(
     enroll_rate |>
-      dplyr::group_by(.data$rate, .data$duration) |>
-      dplyr::mutate(rate = sum(.data$arm_rate)) |>
-      dplyr::slice_head(n = 1),
+      dplyr::group_by(.data$stratum, .data$rate, .data$duration, .data$index) |>
+      dplyr::summarise(rate = sum(.data$arm_rate), .groups = "drop") |>
+      dplyr::arrange(.data$index),
     fail_rate_wide,
     times_analysed,
     dplyr::filter(enroll_rate, .data$treatments == test)$ratio[1]/
diff --git a/R/utils-sim.R b/R/utils-sim.R
index 1af29b2..beeaae0 100644
--- a/R/utils-sim.R
+++ b/R/utils-sim.R
@@ -560,9 +560,9 @@ get_dist_tite <- function(control, test, enroll_rate, fail_rate,
                     stratum        = .$stratum)}
   ahr_obj <- ahr_dd(
     enroll_rate |>
-      dplyr::group_by(.data$rate, .data$duration) |>
-      dplyr::mutate(rate = sum(.data$arm_rate)) |>
-      dplyr::slice_head(n = 1),
+      dplyr::group_by(.data$stratum, .data$rate, .data$duration, .data$index) |>
+      dplyr::summarise(rate = sum(.data$arm_rate), .groups = "drop") |>
+      dplyr::arrange(.data$index),
     fail_rate_wide,
     times_analysed,
     dplyr::filter(enroll_rate, .data$treatments == test)$ratio[1]/
diff --git a/tests/testthat/test-enroll-pooling.R b/tests/testthat/test-enroll-pooling.R
new file mode 100644
index 0000000..9af4d6d
--- /dev/null
+++ b/tests/testthat/test-enroll-pooling.R
@@ -0,0 +1,77 @@
+library(appendMCP)
+
+# Regression tests for enrollment-rate pooling in get_info_tite / get_dist_tite.
+# Pooling arm-level enrollment down to stratum-level totals must preserve each
+# stratum's sequence of enrollment periods. Grouping by (rate, duration) alone
+# collapses same-valued periods ACROSS strata (e.g. shared zero-rate lead-ins
+# for a sub-study that opens late), so all but the first stratum lose their
+# delay and appear to start enrolling at time 0, inflating expected events,
+# information, and simulated power.
+
+# Two strata in a sub-study that opens at month 14: two zero-rate lead-in
+# periods (6 + 8 months) shared by both strata, then 8 months of enrollment
+# with 2:1 (Trt:Control) allocation.
+make_enroll <- function() {
+  tibble::tibble(
+    stratum    = rep(c("S_Bpos", "S_Bpos", "S_Bneg", "S_Bneg"), times = 3),
+    treatments = rep(c("Control", "Trt"), times = 6),
+    rate       = c(rep(0, 8), 15, 15, 22.5, 22.5),
+    duration   = rep(c(6, 8, 8), each = 4),
+    ratio      = rep(c(1, 2), times = 6),
+    arm_rate   = c(rep(0, 8), 5, 10, 7.5, 15),
+    index      = rep(1:3, each = 4)
+  ) |>
+    dplyr::mutate(stratum_treatment = paste(stratum, treatments, sep = "-"))
+}
+
+make_fail <- function() {
+  tibble::tibble(
+    stratum      = rep(c("S_Bpos", "S_Bneg"), each = 2),
+    treatment    = rep(c("Control", "Trt"), times = 2),
+    duration     = Inf,
+    fail_rate    = rep(c(log(2)/12, log(2)/18), times = 2),
+    dropout_rate = 0.001
+  )
+}
+
+# Reference enrollment with each stratum's 14-month lead-in explicitly kept
+enroll_reference <- tibble::tibble(
+  stratum  = rep(c("S_Bpos", "S_Bneg"), each = 3),
+  duration = rep(c(6, 8, 8), times = 2),
+  rate     = c(0, 0, 15, 0, 0, 22.5)
+)
+
+test_that("get_info_tite preserves per-stratum enrollment delays", {
+  times <- c(30, 40)
+  info <- get_info_tite("Control", "Trt", make_enroll(), make_fail(), times)
+
+  # Independent reference: gsDesign2::ahr with the correct stratified enrollment
+  fail_reference <- tibble::tibble(
+    stratum      = c("S_Bpos", "S_Bneg"),
+    duration     = Inf,
+    fail_rate    = log(2)/12,
+    hr           = (log(2)/18)/(log(2)/12),
+    dropout_rate = 0.001
+  )
+  info_reference <- gsDesign2::ahr(enroll_reference, fail_reference, times, ratio = 2)$info
+
+  expect_equal(info, info_reference, tolerance = 1e-6)
+})
+
+test_that("get_dist_tite uses each stratum's own enrollment periods", {
+  times <- c(30, 40)
+  dist <- get_dist_tite("Control", "Trt", make_enroll(), make_fail(), times)
+
+  fail_dd <- tibble::tibble(
+    stratum        = c("S_Bpos", "S_Bneg"),
+    duration       = Inf,
+    fail_rate      = log(2)/12,
+    dropout_rate_c = 0.001,
+    dropout_rate_e = 0.001,
+    hr             = (log(2)/18)/(log(2)/12)
+  )
+  ref <- ahr_dd(enroll_reference, fail_dd, times, ratio = 2)
+
+  expect_equal(dist$EZ, -log(ref$ahr)*sqrt(ref$info), tolerance = 1e-6)
+  expect_equal(unname(dist$CovZ[1, 2]), sqrt(ref$info[1]/ref$info[2]), tolerance = 1e-6)
+})