diff --git a/.agents/skills/survival-design-routing/SKILL.md b/.agents/skills/survival-design-routing/SKILL.md
index bcf919cf..f33785b8 100644
--- a/.agents/skills/survival-design-routing/SKILL.md
+++ b/.agents/skills/survival-design-routing/SKILL.md
@@ -21,3 +21,25 @@ description: Choose the appropriate gsDesign survival or exact-binomial workflow
   otherwise keep the appropriate survival design function.
 - Set `ratio` explicitly when randomization is specified; `ratio = 1` means
   equal experimental:control randomization.
+- For the common four-period enrollment ramp-up, use `gamma = 1:4` for equal
+  relative rate increments and `R = rep(1, 4)`. The final period is extended
+  as needed by the selected duration solve.
+- With `T` and `minfup` specified, `gsSurv()` and `gsSurvCalendar()` fix total
+  study and minimum follow-up duration, scale `gamma` proportionally to power
+  the trial, and extend the final `R` period to `T - minfup`.
+- With `T = NULL` and `minfup` specified, keep `gamma` fixed and solve the
+  enrollment duration by extending the final `R` period.
+- With both `T = NULL` and `minfup = NULL`, keep enrollment rates and duration
+  fixed and solve follow-up duration. Warn that this can be infeasible when
+  the fixed enrollment plan is always over- or under-powered.
+- Use `gsSurvPower()` when enrollment, follow-up, and analysis timing are fixed
+  and the objective is achieved power rather than a powered sample-size plan.
+- Use `toInteger()` after design derivation when integer event targets and an
+  allocation-compatible total enrollment are needed.
+- For a `gsSurv` object, use `N` for cumulative total expected enrollment at
+  each analysis. Use `eNC` and `eNE` for control and experimental enrollment by
+  stratum.
+- For an `nSurv` object, scalar `n` and scalar `N` are identical total expected
+  enrollment values. Accept either name without converting existing code.
+- For stratified survival designs, supply matrices whose columns are strata;
+  align the columns of `lambdaC`, `eta`, `etaE`, and `gamma`.
diff --git a/DESCRIPTION b/DESCRIPTION
index fbc13d40..22a35daf 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: gsDesign
-Version: 3.11.0.9002
+Version: 3.11.0.9003
 Title: Group Sequential Design
 Authors@R: c(
     person("Keaven", "Anderson", email = "keaven_anderson@merck.com", role = c("aut", "cre")),
diff --git a/NEWS.md b/NEWS.md
index 02d25f41..3bd2145b 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,14 @@
 
 ## New features
 
+- All `gsSurv` objects now include `N`, the cumulative total expected
+  enrollment at each analysis. `nSurv` objects retain scalar `n` and also
+  return identical scalar `N` as a non-breaking alias (#299).
+- Added survival enrollment-planning documentation for four-period ramp-up,
+  the three combinations of fixed or solved enrollment and follow-up duration,
+  calendar-time analyses, power sensitivity, integer conversion, and
+  stratified designs. `gsSurv(T = NULL, minfup = ...)` now consistently keeps
+  enrollment rates fixed and solves enrollment duration (#300).
 - Added `minMedianFollowUp()` and `plotMinMedianFollowUp()` to compute and plot
   minimum median follow-up at any calendar time from the piecewise enrollment
   assumptions in an `nSurv` or `gsSurv` design. The plot accepts arbitrary
diff --git a/R/gsSurv-nSurv.R b/R/gsSurv-nSurv.R
index 60aeb291..4ade6155 100644
--- a/R/gsSurv-nSurv.R
+++ b/R/gsSurv-nSurv.R
@@ -232,6 +232,8 @@
 #'   \item{hr0}{As input.}
 #'   \item{n}{Total expected sample size corresponding to output accrual rates
 #'   and durations.}
+#'   \item{N}{Identical to \code{n}; provided as a non-breaking alias for total
+#'   expected sample size.}
 #'   \item{d}{Total expected number of events under the alternate
 #'   hypothesis.}
 #'   \item{tol}{As input, except when not used in computations in
@@ -277,6 +279,8 @@
 #'   \item{minfup}{As input.}
 #'   \item{hr}{As input.}
 #'   \item{hr0}{As input.}
+#'   \item{N}{A vector containing cumulative total expected enrollment at each
+#'   analysis.}
 #'   \item{eNC}{Total expected sample size corresponding to output accrual rates
 #'   and durations.}
 #'   \item{eNE}{Total expected sample size corresponding to output accrual rates
@@ -325,8 +329,10 @@
 #'
 #' @author Keaven Anderson \email{keaven_anderson@@merck.com}
 #'
-#' @seealso \code{vignette("gsSurvBasicExamples", package = "gsDesign")} for
-#'   basic survival sample size examples, \code{vignette("SurvivalOverview",
+#' @seealso \code{vignette("SurvivalEnrollmentPlanning", package = "gsDesign")}
+#'   for enrollment ramp-up and duration planning,
+#'   \code{vignette("gsSurvBasicExamples", package = "gsDesign")} for basic
+#'   survival sample size examples, \code{vignette("SurvivalOverview",
 #'   package = "gsDesign")} for method background, and
 #'   \code{vignette("SeqDesignSurvival", package = "gsDesign")} for a SAS PROC
 #'   SEQDESIGN translation example.
@@ -412,6 +418,21 @@
 #' ) |>
 #'   print()
 #'
+#' # Common four-period enrollment ramp-up. With T and minfup fixed, the
+#' # relative gamma pattern is scaled to power the trial, and the final R
+#' # period is extended so enrollment lasts T - minfup.
+#' ramp_rate <- gsSurv(
+#'   T = 26, minfup = 12,
+#'   gamma = 1:4, R = rep(1, 4)
+#' )
+#'
+#' # With T = NULL and minfup fixed, gamma stays fixed and the final
+#' # enrollment period is extended to obtain the required sample size.
+#' ramp_duration <- gsSurv(
+#'   T = NULL, minfup = 12,
+#'   gamma = 1:4, R = rep(1, 4)
+#' )
+#'
 #' # Vary minimum follow-up duration minfup to obtain power
 #' # Accrual duration R rate gamma are fixed and will not change on output.
 #' # Trial duration T and minimum follow-up minfup are input as NULL
@@ -575,6 +596,7 @@ nSurv <- function(
   xx$method <- method
   xx$call <- match.call()
   xx$inputs <- input_vals
+  xx$N <- xx$n
   return(xx)
 }
 
diff --git a/R/gsSurv-utils.R b/R/gsSurv-utils.R
index 18ad04f0..b80d6840 100644
--- a/R/gsSurv-utils.R
+++ b/R/gsSurv-utils.R
@@ -67,6 +67,12 @@ validate_survival_timing_inputs <- function(R, T, minfup, call = "nSurv") {
   invisible(TRUE)
 }
 
+# Add cumulative total enrollment at each analysis to a gsSurv object.
+gsSurvAddN <- function(x) {
+  x$N <- rowSums(as.matrix(x$eNC)) + rowSums(as.matrix(x$eNE))
+  x
+}
+
 # Construct the gsDesign portion of a single-analysis survival design without
 # calling gsDesign(), whose group-sequential validation requires k >= 2.
 gsSurvFixedDesignObject <- function(
@@ -185,5 +191,5 @@ asGsSurvFixedDesign <- function(
   design$inputs <- inputs
   class(design) <- c("gsSurv", "gsDesign")
 
-  design
+  gsSurvAddN(design)
 }
diff --git a/R/gsSurv.R b/R/gsSurv.R
index f17f15c2..f1f690a8 100644
--- a/R/gsSurv.R
+++ b/R/gsSurv.R
@@ -99,13 +99,6 @@ gsSurv <- function(
     )$root
     T <- sum(R) + minfup
   }
-  # Preserve the historical Lachin-Foulkes default: with fixed follow-up and
-  # T = NULL, keep R fixed and vary the accrual rate.
-  if (method == "LachinFoulkes" && is.null(T) && !is.null(minfup) &&
-    !is.null(R) && length(R) > 0 &&
-    !is.null(gamma) && length(gamma) > 0) {
-    T <- sum(R) + minfup
-  }
   x <- nSurv(
     lambdaC = lambdaC, hr = hr, hr0 = hr0, eta = eta, etaE = etaE,
     gamma = gamma, R = R, S = S, T = T, minfup = minfup, ratio = ratio,
@@ -176,7 +169,7 @@ gsSurv <- function(
   colnames(y$etaE) <- stratnames
   rownames(y$gamma) <- nameR
   colnames(y$gamma) <- stratnames
-  return(y)
+  return(gsSurvAddN(y))
 }
 
 # gsnSurv function [sinew] ----
diff --git a/R/gsSurvCalendar.R b/R/gsSurvCalendar.R
index 7f00c6e3..d0eec26f 100644
--- a/R/gsSurvCalendar.R
+++ b/R/gsSurvCalendar.R
@@ -33,8 +33,10 @@
 #'
 #' @rdname gsSurvCalendar
 #'
-#' @seealso \code{vignette("SeqDesignSurvival", package = "gsDesign")} for a
-#'   SAS PROC SEQDESIGN sample size translation example and
+#' @seealso \code{vignette("SurvivalEnrollmentPlanning", package = "gsDesign")}
+#'   for enrollment ramp-up and duration planning,
+#'   \code{vignette("SeqDesignSurvival", package = "gsDesign")} for a SAS
+#'   PROC SEQDESIGN sample size translation example and
 #'   \code{vignette("gsSurvPower", package = "gsDesign")} for power
 #'   calculations with fixed calendar analysis assumptions.
 #'
@@ -71,6 +73,12 @@
 #' y$usTime
 #' # Actual calendar fraction from design after toInteger() conversion
 #' y$T / max(y$T)
+#'
+#' # Four-period enrollment ramp-up with fixed study duration and follow-up.
+#' ramp_calendar <- gsSurvCalendar(
+#'   calendarTime = c(12, 18, 26), minfup = 12,
+#'   gamma = 1:4, R = rep(1, 4)
+#' )
 gsSurvCalendar <- function(
   test.type = 4, alpha = 0.025, sided = 1, beta = 0.1, astar = 0,
   sfu = gsDesign::sfHSD, sfupar = -4,
@@ -184,5 +192,5 @@ gsSurvCalendar <- function(
   colnames(y$etaE) <- stratnames
   rownames(y$gamma) <- nameR
   colnames(y$gamma) <- stratnames
-  return(y)
+  return(gsSurvAddN(y))
 }
diff --git a/R/gsSurvPower.R b/R/gsSurvPower.R
index 7410526a..c7d489fe 100644
--- a/R/gsSurvPower.R
+++ b/R/gsSurvPower.R
@@ -247,6 +247,7 @@
 #' \item{T}{Calendar times of analyses.}
 #' \item{eDC, eDE}{Expected events by stratum (control, experimental).}
 #' \item{eNC, eNE}{Expected sample sizes by stratum (control, experimental).}
+#' \item{N}{Cumulative total expected enrollment at each analysis.}
 #' \item{upper, lower}{Bounds and crossing probabilities.}
 #' \item{harm}{Harm-bound information when \code{test.type} is 7 or 8.}
 #' \item{en, theta}{Expected sample size summary and drift values returned by
@@ -1236,5 +1237,5 @@ gsSurvPower <- function(
   result$beta <- 1 - result$power
 
   class(result) <- c("gsSurv", "gsDesign")
-  .gsSurvPower_label_output_matrices(result)
+  gsSurvAddN(.gsSurvPower_label_output_matrices(result))
 }
diff --git a/R/toInteger.R b/R/toInteger.R
index 3d3a0882..2b92c4f1 100644
--- a/R/toInteger.R
+++ b/R/toInteger.R
@@ -137,6 +137,7 @@ toInteger <- function(x, ratio = x$ratio, roundUpFinal = TRUE) {
     for (nm in plan_fields) result[[nm]] <- integer_design[[nm]]
     result$d <- integer_design$n.I[1]
     result$n <- sum(result$eNC + result$eNE)
+    result$N <- result$n
     result$beta <- integer_design$beta
     result$power <- 1 - result$beta
     class(result) <- class(original)
@@ -402,6 +403,7 @@ toInteger <- function(x, ratio = x$ratio, roundUpFinal = TRUE) {
     rownames(xi$gamma) <- nameR
     colnames(xi$gamma) <- stratnames
   }
+  if (inherits(xi, "gsSurv")) xi <- gsSurvAddN(xi)
   return(xi)
 }
 
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 41a471c6..68a244a5 100755
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -154,6 +154,7 @@ articles:
     contents:
       - SurvivalOverview
       - gsSurvBasicExamples
+      - SurvivalEnrollmentPlanning
       - gsSurvPower
       - SeqDesignSurvival
       - toInteger
diff --git a/man/gsSurvCalendar.Rd b/man/gsSurvCalendar.Rd
index 228de6db..f69b691a 100644
--- a/man/gsSurvCalendar.Rd
+++ b/man/gsSurvCalendar.Rd
@@ -235,6 +235,12 @@ gsBoundSummary(y)
 y$usTime
 # Actual calendar fraction from design after toInteger() conversion
 y$T / max(y$T)
+
+# Four-period enrollment ramp-up with fixed study duration and follow-up.
+ramp_calendar <- gsSurvCalendar(
+  calendarTime = c(12, 18, 26), minfup = 12,
+  gamma = 1:4, R = rep(1, 4)
+)
 }
 \references{
 Lan KKG and DeMets DL (1983), Discrete Sequential Boundaries for Clinical
@@ -250,8 +256,10 @@ Freedman LS (1982), Tables of the Number of Patients Required in Clinical
 Trials Using the Logrank Test. \emph{Statistics in Medicine}, 1, 121-129.
 }
 \seealso{
-\code{vignette("SeqDesignSurvival", package = "gsDesign")} for a
-  SAS PROC SEQDESIGN sample size translation example and
+\code{vignette("SurvivalEnrollmentPlanning", package = "gsDesign")}
+  for enrollment ramp-up and duration planning,
+  \code{vignette("SeqDesignSurvival", package = "gsDesign")} for a SAS
+  PROC SEQDESIGN sample size translation example and
   \code{vignette("gsSurvPower", package = "gsDesign")} for power
   calculations with fixed calendar analysis assumptions.
 
diff --git a/man/gsSurvPower.Rd b/man/gsSurvPower.Rd
index a3da2d6f..6806b4d2 100644
--- a/man/gsSurvPower.Rd
+++ b/man/gsSurvPower.Rd
@@ -220,6 +220,7 @@ An object of class \code{c("gsSurv", "gsDesign")} containing:
 \item{T}{Calendar times of analyses.}
 \item{eDC, eDE}{Expected events by stratum (control, experimental).}
 \item{eNC, eNE}{Expected sample sizes by stratum (control, experimental).}
+\item{N}{Cumulative total expected enrollment at each analysis.}
 \item{upper, lower}{Bounds and crossing probabilities.}
 \item{harm}{Harm-bound information when \code{test.type} is 7 or 8.}
 \item{en, theta}{Expected sample size summary and drift values returned by
diff --git a/man/nSurv.Rd b/man/nSurv.Rd
index 3950d1d7..494b58a7 100644
--- a/man/nSurv.Rd
+++ b/man/nSurv.Rd
@@ -342,6 +342,8 @@ analyses is still displayed.}
   \item{hr0}{As input.}
   \item{n}{Total expected sample size corresponding to output accrual rates
   and durations.}
+  \item{N}{Identical to \code{n}; provided as a non-breaking alias for total
+  expected sample size.}
   \item{d}{Total expected number of events under the alternate
   hypothesis.}
   \item{tol}{As input, except when not used in computations in
@@ -387,6 +389,8 @@ analyses is still displayed.}
   \item{minfup}{As input.}
   \item{hr}{As input.}
   \item{hr0}{As input.}
+  \item{N}{A vector containing cumulative total expected enrollment at each
+  analysis.}
   \item{eNC}{Total expected sample size corresponding to output accrual rates
   and durations.}
   \item{eNE}{Total expected sample size corresponding to output accrual rates
@@ -599,6 +603,21 @@ gsSurv(
 ) |>
   print()
 
+# Common four-period enrollment ramp-up. With T and minfup fixed, the
+# relative gamma pattern is scaled to power the trial, and the final R
+# period is extended so enrollment lasts T - minfup.
+ramp_rate <- gsSurv(
+  T = 26, minfup = 12,
+  gamma = 1:4, R = rep(1, 4)
+)
+
+# With T = NULL and minfup fixed, gamma stays fixed and the final
+# enrollment period is extended to obtain the required sample size.
+ramp_duration <- gsSurv(
+  T = NULL, minfup = 12,
+  gamma = 1:4, R = rep(1, 4)
+)
+
 # Vary minimum follow-up duration minfup to obtain power
 # Accrual duration R rate gamma are fixed and will not change on output.
 # Trial duration T and minimum follow-up minfup are input as NULL
@@ -693,8 +712,10 @@ Trials Using the Logrank Test. \emph{Statistics in Medicine}, 1, 121-129.
 \seealso{
 \code{\link[stats]{uniroot}}
 
-\code{vignette("gsSurvBasicExamples", package = "gsDesign")} for
-  basic survival sample size examples, \code{vignette("SurvivalOverview",
+\code{vignette("SurvivalEnrollmentPlanning", package = "gsDesign")}
+  for enrollment ramp-up and duration planning,
+  \code{vignette("gsSurvBasicExamples", package = "gsDesign")} for basic
+  survival sample size examples, \code{vignette("SurvivalOverview",
   package = "gsDesign")} for method background, and
   \code{vignette("SeqDesignSurvival", package = "gsDesign")} for a SAS PROC
   SEQDESIGN translation example.
diff --git a/tests/testthat/_snaps/independent-test-print.gsSurv.md b/tests/testthat/_snaps/independent-test-print.gsSurv.md
index 8b86fcfa..fcd81f36 100644
--- a/tests/testthat/_snaps/independent-test-print.gsSurv.md
+++ b/tests/testthat/_snaps/independent-test-print.gsSurv.md
@@ -2,7 +2,7 @@
 
     Group sequential design (method=LachinFoulkes; k=3 analyses; Two-sided asymmetric with non-binding futility)
     HR=0.500 vs HR0=1.000 | alpha=0.025 (sided=2) | power=90.0%
-    N=187.2 subjects | D=93.5 events | T=18.5 study duration | accrual=18.0 Accrual duration | minfup=0.5 minimum follow-up | ratio=1 randomization ratio (experimental/control)
+    N=105.2 subjects | D=92.8 events | T=105.7 study duration | accrual=105.2 Accrual duration | minfup=0.5 minimum follow-up | ratio=1 randomization ratio (experimental/control)
     
     Spending functions:
       Efficacy bounds derived using a Hwang-Shih-DeCani spending function with gamma = -4.
@@ -12,35 +12,35 @@
     Method: LachinFoulkes 
        Analysis              Value Efficacy Futility
       IA 1: 33%                  Z   3.0107  -0.2388
-         N: 100        p (1-sided)   0.0013   0.5944
-     Events: 32       ~HR at bound   0.3401   1.0893
-      Month: 10   P(Cross) if HR=1   0.0013   0.4056
+          N: 44        p (1-sided)   0.0013   0.5944
+     Events: 31       ~HR at bound   0.3386   1.0897
+      Month: 43   P(Cross) if HR=1   0.0013   0.4056
                 P(Cross) if HR=0.5   0.1412   0.0148
       IA 2: 67%                  Z   2.5465   0.9410
-         N: 150        p (1-sided)   0.0054   0.1733
-     Events: 63       ~HR at bound   0.5246   0.7879
-      Month: 14   P(Cross) if HR=1   0.0062   0.8347
+          N: 76        p (1-sided)   0.0054   0.1733
+     Events: 62       ~HR at bound   0.5233   0.7872
+      Month: 75   P(Cross) if HR=1   0.0062   0.8347
                 P(Cross) if HR=0.5   0.5815   0.0437
           Final                  Z   1.9992   1.9992
-         N: 188        p (1-sided)   0.0228   0.0228
-     Events: 94       ~HR at bound   0.6613   0.6613
-      Month: 18   P(Cross) if HR=1   0.0233   0.9767
+         N: 106        p (1-sided)   0.0228   0.0228
+     Events: 93       ~HR at bound   0.6603   0.6603
+     Month: 106   P(Cross) if HR=1   0.0233   0.9767
                 P(Cross) if HR=0.5   0.9000   0.1000
     
     Key inputs (names preserved):
-                                   desc    item  value input
-                        Accrual rate(s)   gamma 10.401     1
-               Accrual rate duration(s)       R     18    18
-                 Control hazard rate(s) lambdaC  0.116 0.116
-                Control dropout rate(s)     eta      0     0
-           Experimental dropout rate(s)    etaE      0  etaE
-     Event and dropout rate duration(s)       S   NULL     S
+                                   desc    item   value input
+                        Accrual rate(s)   gamma       1     1
+               Accrual rate duration(s)       R 105.244    18
+                 Control hazard rate(s) lambdaC   0.116 0.116
+                Control dropout rate(s)     eta       0     0
+           Experimental dropout rate(s)    etaE       0  etaE
+     Event and dropout rate duration(s)       S    NULL     S
 
 # Test: checking hazard ratio hr0 != 1
 
     Group sequential design (method=LachinFoulkes; k=3 analyses; Two-sided asymmetric with non-binding futility)
     HR=0.500 vs HR0=1.500 | alpha=0.025 (sided=2) | power=90.0%
-    N=75.7 subjects | D=37.8 events | T=18.5 study duration | accrual=18.0 Accrual duration | minfup=0.5 minimum follow-up | ratio=1 randomization ratio (experimental/control)
+    N=49.1 subjects | D=37.1 events | T=49.6 study duration | accrual=49.1 Accrual duration | minfup=0.5 minimum follow-up | ratio=1 randomization ratio (experimental/control)
     
     Spending functions:
       Efficacy bounds derived using a Hwang-Shih-DeCani spending function with gamma = -4.
@@ -50,29 +50,29 @@
     Method: LachinFoulkes 
        Analysis              Value Efficacy Futility
       IA 1: 33%                  Z   3.0107  -0.2388
-          N: 42        p (1-sided)   0.0013   0.5944
-     Events: 13       ~HR at bound   0.2750   1.7160
-      Month: 10 P(Cross) if HR=1.5   0.0013   0.4056
+          N: 24        p (1-sided)   0.0013   0.5944
+     Events: 13       ~HR at bound   0.2705   1.7182
+      Month: 23 P(Cross) if HR=1.5   0.0013   0.4056
                 P(Cross) if HR=0.5   0.1412   0.0148
       IA 2: 67%                  Z   2.5465   0.9410
-          N: 62        p (1-sided)   0.0054   0.1733
-     Events: 26       ~HR at bound   0.5438   1.0310
-      Month: 14 P(Cross) if HR=1.5   0.0062   0.8347
+          N: 38        p (1-sided)   0.0054   0.1733
+     Events: 25       ~HR at bound   0.5385   1.0273
+      Month: 37 P(Cross) if HR=1.5   0.0062   0.8347
                 P(Cross) if HR=0.5   0.5815   0.0437
           Final                  Z   1.9992   1.9992
-          N: 76        p (1-sided)   0.0228   0.0228
-     Events: 38       ~HR at bound   0.7827   0.7827
-      Month: 18 P(Cross) if HR=1.5   0.0233   0.9767
+          N: 50        p (1-sided)   0.0228   0.0228
+     Events: 38       ~HR at bound   0.7779   0.7779
+      Month: 50 P(Cross) if HR=1.5   0.0233   0.9767
                 P(Cross) if HR=0.5   0.9000   0.1000
     
     Key inputs (names preserved):
-                                   desc    item value input
-                        Accrual rate(s)   gamma 4.204     1
-               Accrual rate duration(s)       R    18    18
-                 Control hazard rate(s) lambdaC 0.116 0.116
-                Control dropout rate(s)     eta     0     0
-           Experimental dropout rate(s)    etaE     0  etaE
-     Event and dropout rate duration(s)       S  NULL     S
+                                   desc    item  value input
+                        Accrual rate(s)   gamma      1     1
+               Accrual rate duration(s)       R 49.063    18
+                 Control hazard rate(s) lambdaC  0.116 0.116
+                Control dropout rate(s)     eta      0     0
+           Experimental dropout rate(s)    etaE      0  etaE
+     Event and dropout rate duration(s)       S   NULL     S
 
 # Test: checking test.type > 1
 
diff --git a/tests/testthat/test-gsSurv-N-enrollment.R b/tests/testthat/test-gsSurv-N-enrollment.R
new file mode 100644
index 00000000..0a2a29e7
--- /dev/null
+++ b/tests/testthat/test-gsSurv-N-enrollment.R
@@ -0,0 +1,64 @@
+expect_gsSurv_N <- function(x) {
+  expect_s3_class(x, "gsSurv")
+  expect_equal(x$N, rowSums(x$eNC) + rowSums(x$eNE))
+  expect_length(x$N, x$k)
+}
+
+test_that("nSurv returns identical scalar n and N values", {
+  designs <- list(
+    nSurv(),
+    nSurv(
+      lambdaC = matrix(log(2) / c(10, 16), nrow = 1),
+      gamma = matrix(c(0.6, 0.4), nrow = 1)
+    )
+  )
+
+  for (design in designs) {
+    expect_length(design$n, 1)
+    expect_length(design$N, 1)
+    expect_identical(design$N, design$n)
+    expect_equal(design$N, sum(design$eNC + design$eNE))
+
+    integer <- toInteger(design)
+    expect_identical(integer$N, integer$n)
+  }
+})
+
+test_that("all gsSurv construction paths return cumulative total N", {
+  fixed <- gsSurv(k = 1)
+  sequential <- gsSurv(k = 2)
+  calendar <- gsSurvCalendar(calendarTime = c(12, 24))
+  power <- gsSurvPower(
+    k = 2,
+    plannedCalendarTime = c(12, 24)
+  )
+  integer <- toInteger(sequential)
+
+  lapply(
+    list(fixed, sequential, calendar, power, integer),
+    expect_gsSurv_N
+  )
+  expect_equal(tail(integer$N, 1), round(tail(integer$N, 1)))
+})
+
+test_that("gsSurv with fixed rates and follow-up solves enrollment duration", {
+  gamma <- 1:4
+  R <- rep(1, 4)
+  minfup <- 12
+
+  design <- gsSurv(
+    k = 2,
+    lambdaC = log(2) / 12,
+    hr = 0.7,
+    T = NULL,
+    minfup = minfup,
+    gamma = gamma,
+    R = R
+  )
+
+  expect_identical(design$variable, "Accrual duration")
+  expect_equal(as.vector(design$gamma), gamma)
+  expect_equal(design$R[seq_len(3)], R[seq_len(3)])
+  expect_gt(design$R[4], R[4])
+  expect_equal(max(design$T), sum(design$R) + minfup)
+})
diff --git a/vignettes/SurvivalEnrollmentPlanning.Rmd b/vignettes/SurvivalEnrollmentPlanning.Rmd
new file mode 100644
index 00000000..83ef5092
--- /dev/null
+++ b/vignettes/SurvivalEnrollmentPlanning.Rmd
@@ -0,0 +1,257 @@
+---
+title: "Planning enrollment and follow-up for survival designs"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Planning enrollment and follow-up for survival designs}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include=FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.width = 7,
+  fig.height = 4.5
+)
+options(width = 70)
+```
+
+## Overview
+
+Survival trial planning connects three time quantities:
+
+- enrollment duration;
+- minimum follow-up after the last participant enrolls; and
+- total study duration.
+
+The identity is
+
+```text
+total study duration = enrollment duration + minimum follow-up.
+```
+
+`gsSurv()` can solve one component of this plan while deriving a powered group
+sequential design. `gsSurvCalendar()` uses the same enrollment model but fixes
+analysis times on the calendar. `gsSurvPower()` answers the reverse question:
+given a fixed operational plan, what power will it achieve? Finally,
+`toInteger()` converts continuous expected events and enrollment to an
+integer-compatible plan.
+
+```{r setup}
+library(gsDesign)
+
+lambdaC <- log(2) / 12
+hr <- 0.7
+
+# Four enrollment periods with equal relative rate increments.
+gamma_ramp <- 1:4
+R_ramp <- rep(1, 4)
+```
+
+Every `nSurv` object contains identical scalar values `n` and `N` for total
+expected enrollment. Every `gsSurv` object contains `N` as a vector of
+cumulative total expected enrollment at each analysis. It is the row total of
+the control and experimental enrollment components:
+
+```r
+x$N == rowSums(x$eNC) + rowSums(x$eNE)
+```
+
+## Pattern 1: fix study duration and minimum follow-up
+
+This is the most common planning pattern. Specifying `T` and `minfup` fixes the
+enrollment duration at `T - minfup`. The values in `gamma` describe the
+relative ramp-up shape; `gsSurv()` scales all rates proportionally to power the
+trial. When the supplied `R` periods do not fill the enrollment duration, the
+last period is extended.
+
+```{r fixed-duration}
+fixed_duration <- gsSurv(
+  k = 3,
+  lambdaC = lambdaC,
+  hr = hr,
+  T = 26,
+  minfup = 12,
+  gamma = gamma_ramp,
+  R = R_ramp
+)
+
+data.frame(
+  period = seq_along(fixed_duration$R),
+  duration = fixed_duration$R,
+  rate = as.vector(fixed_duration$gamma)
+)
+
+fixed_duration$N
+```
+
+Here enrollment lasts 14 months. The first three ramp-up periods last one
+month each and the fourth rate continues through the remaining 11 months.
+
+## Pattern 2: fix rates and minimum follow-up
+
+Set `T = NULL` to keep `gamma` fixed and solve how long enrollment must remain
+open. The final `R` period is extended to obtain the required sample size; the
+earlier ramp-up periods are unchanged.
+
+```{r fixed-rates}
+fixed_rates <- gsSurv(
+  k = 3,
+  lambdaC = lambdaC,
+  hr = hr,
+  T = NULL,
+  minfup = 12,
+  gamma = gamma_ramp,
+  R = R_ramp
+)
+
+data.frame(
+  period = seq_along(fixed_rates$R),
+  duration = fixed_rates$R,
+  rate = as.vector(fixed_rates$gamma)
+)
+
+c(
+  enrollment_duration = sum(fixed_rates$R),
+  minimum_follow_up = fixed_rates$minfup,
+  total_duration = max(fixed_rates$T)
+)
+```
+
+Absolute rates of 1, 2, 3, and 4 participants per month are deliberately low,
+so this example produces a long enrollment duration. In practice, multiply
+the ramp by realistic site-level or program-level rates.
+
+## Pattern 3: fix enrollment and solve follow-up
+
+With both `T = NULL` and `minfup = NULL`, enrollment rates and their durations
+are fixed. `gsSurv()` solves the follow-up duration needed to power the trial.
+This option can fail when the fixed enrollment plan is over-powered even with
+almost no follow-up, or under-powered regardless of follow-up.
+
+```{r fixed-enrollment}
+fixed_enrollment <- gsSurv(
+  k = 3,
+  lambdaC = lambdaC,
+  hr = hr,
+  T = NULL,
+  minfup = NULL,
+  gamma = 50 * gamma_ramp,
+  R = R_ramp
+)
+
+c(
+  enrollment_duration = sum(fixed_enrollment$R),
+  minimum_follow_up = fixed_enrollment$minfup,
+  total_duration = max(fixed_enrollment$T)
+)
+```
+
+When this solve is infeasible, revise the fixed enrollment plan, target power,
+or event assumptions rather than interpreting the error as a numerical failure.
+
+## Calendar-time analyses
+
+Use `gsSurvCalendar()` when interim analyses are specified as months from the
+start of enrollment. The final calendar time and `minfup` imply the enrollment
+duration, while the four-period ramp-up is scaled to power the trial.
+
+```{r calendar-design}
+calendar_design <- gsSurvCalendar(
+  calendarTime = c(12, 18, 26),
+  lambdaC = lambdaC,
+  hr = hr,
+  minfup = 12,
+  gamma = gamma_ramp,
+  R = R_ramp
+)
+
+data.frame(
+  analysis_month = calendar_design$T,
+  expected_events = calendar_design$n.I,
+  expected_enrollment = calendar_design$N
+)
+```
+
+Use `gsSurv()` instead when analyses are defined by event or information
+fractions rather than calendar dates.
+
+## Power for a fixed operational plan
+
+`gsSurvPower()` does not resize enrollment to hit target power. It evaluates
+power for the supplied rates, durations, treatment effect, and analysis timing.
+For example, the following sensitivity analysis evaluates 80% of the planned
+enrollment rates at the original calendar analysis times.
+
+```{r power-sensitivity}
+slower_enrollment <- gsSurvPower(
+  x = fixed_duration,
+  gamma = 0.8 * fixed_duration$gamma,
+  plannedCalendarTime = fixed_duration$T
+)
+
+c(
+  planned_power = 1 - fixed_duration$beta,
+  slower_enrollment_power = slower_enrollment$power
+)
+```
+
+Use `targetEvents = fixed_duration$n.I` instead of `plannedCalendarTime` when
+event counts, rather than dates, remain fixed and the analysis dates may move.
+
+## Integer event and enrollment plans
+
+Design calculations use expected counts and can therefore be non-integer.
+Apply `toInteger()` after deriving the design to obtain integer event targets
+and a final total enrollment compatible with the randomization allocation.
+
+```{r integer-plan}
+integer_design <- toInteger(fixed_duration)
+
+data.frame(
+  analysis = seq_len(integer_design$k),
+  events = integer_design$n.I,
+  enrollment = integer_design$N
+)
+```
+
+The input `ratio` is experimental-to-control randomization. For example,
+`ratio = 1` produces allocation-compatible even totals; `ratio = 2` produces
+totals compatible with 2:1 randomization.
+
+## Stratified enrollment
+
+For a stratified design, matrix columns identify strata. Align the columns of
+the control hazards, dropout rates, and enrollment rates. The example below
+uses two strata with different control medians and enrollment contributions.
+
+```{r stratified}
+lambda_strata <- matrix(log(2) / c(10, 16), nrow = 1)
+gamma_strata <- cbind(
+  0.6 * gamma_ramp,
+  0.4 * gamma_ramp
+)
+
+stratified_design <- gsSurv(
+  k = 3,
+  lambdaC = lambda_strata,
+  hr = hr,
+  eta = matrix(c(0.001, 0.001), nrow = 1),
+  T = 26,
+  minfup = 12,
+  gamma = gamma_strata,
+  R = R_ramp
+)
+
+data.frame(
+  analysis = seq_len(stratified_design$k),
+  control = rowSums(stratified_design$eNC),
+  experimental = rowSums(stratified_design$eNE),
+  total = stratified_design$N
+)
+```
+
+The same matrix conventions apply to `gsSurvCalendar()` and
+`gsSurvPower()`. For final operational planning, inspect both `N` and the
+stratum-specific `eNC` and `eNE` matrices before applying `toInteger()`.