diff --git a/.claude/skills/write-tests.md b/.claude/skills/write-tests.md
index 98dfc01c1..01a9f3c20 100644
--- a/.claude/skills/write-tests.md
+++ b/.claude/skills/write-tests.md
@@ -16,11 +16,11 @@ This project uses **testit** for testing. testit assertions are plain R expressi
 tests/
 ├── test-all.R              # Runner: library(testit); test_pkg("gsDesign2")
 └── testit/
-    ├── helper.R            # Shared setup (sourced before test files)
-    ├── helper-*.R          # Additional helpers
+    ├── helper.R            # Shared setup (auto-sourced before test files)
+    ├── helper-*.R          # Additional helpers (also auto-sourced)
     ├── fixtures/           # Test data (.Rdata, .rds, etc.)
     ├── test-*.R            # Test files
-    └── test-*.md           # Snapshot files (paired with .R files)
+    └── test-*.md           # Snapshot files (standalone, no .R file needed)
 ```
 
 ## Core Pattern
@@ -131,7 +131,7 @@ assert("output structure is correct", {
 
 ## Snapshot Tests
 
-Create a `.md` file alongside the `.R` file (same base name). Format:
+A `.md` snapshot file is a standalone test — it does NOT require a paired `.R` file. The `.md` file contains both the code and the expected output. Format:
 
 ````markdown
 ## `function_name()` description
@@ -154,7 +154,7 @@ testit runs the R code block and compares output to the text block. To initializ
 3. **Use `all.equal(..., tolerance = t)` with the tightest tolerance that passes** — don't use overly loose tolerances.
 4. **Group related assertions in one `assert()` block** — each block should test one logical concept.
 5. **Use descriptive assert messages** — they appear in failure output.
-6. **Shared setup goes in `helper.R`** — it's sourced before all test files.
+6. **Shared setup goes in `helper*.R` files** — testit auto-sources all `helper*.R` files before test files. Never `source()` them manually.
 7. **Load fixture data with `load("fixtures/file.Rdata")`** — paths are relative to `tests/testit/`.
 8. **Use `all.equal()` only when exact comparison fails in CI** — typically macOS produces slightly different floating-point results while `identical()` works fine on Windows/Linux.
 
diff --git a/DESCRIPTION b/DESCRIPTION
index 0f18853b1..70e0b3c17 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -68,4 +68,4 @@ VignetteBuilder:
 LinkingTo:
     Rcpp
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.3
+Config/roxygen2/version: 8.0.0
diff --git a/R/as_gt.R b/R/as_gt.R
index c7a2b8bb0..858005369 100644
--- a/R/as_gt.R
+++ b/R/as_gt.R
@@ -111,7 +111,7 @@ as_gt.fixed_design_summary <- function(x, title = NULL, footnote = NULL, ...) {
 #'   "spanner")`; users can use the functions in the `gt` package to customize
 #'   the table. To disable footnotes, use `footnote = FALSE`.
 #' @param display_bound A vector of strings specifying the label of the bounds.
-#'   The default is `c("Efficacy", "Futility")`.
+#'   The default is `c("Efficacy", "Futility", "Harm")`.
 #' @param display_columns A vector of strings specifying the variables to be
 #'   displayed in the summary table.
 #' @param display_inf_bound Logical, whether to display the +/-inf bound.
@@ -128,7 +128,22 @@ as_gt.fixed_design_summary <- function(x, title = NULL, footnote = NULL, ...) {
 #' gs_design_ahr() |>
 #'   summary() |>
 #'   as_gt()
-#'
+#' 
+#' gs_design_ahr(
+#'   analysis_time = c(12, 24, 36),
+#'   upper = gs_spending_bound,
+#'   upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
+#'   test_upper = c(FALSE, TRUE, TRUE),
+#'   lower = gs_spending_bound,
+#'   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2),
+#'   test_lower = c(TRUE, TRUE, FALSE),
+#'   harm = gs_spending_bound,
+#'   hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4),
+#'   test_harm = c(TRUE, TRUE, FALSE)
+#'   ) |>
+#'  summary() |>
+#'  as_gt() 
+#' 
 #' gs_power_ahr(lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.1)) |>
 #'   summary() |>
 #'   as_gt()
@@ -193,7 +208,7 @@ as_gt.fixed_design_summary <- function(x, title = NULL, footnote = NULL, ...) {
 #'
 #' # Example 5 ----
 #' # Usage of display_bound = ...
-#' # to either show efficacy bound or futility bound, or both(default)
+#' # to show selected bounds
 #' gs_power_wlr(lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.1)) |>
 #'   summary() |>
 #'   as_gt(display_bound = "Efficacy")
@@ -212,7 +227,7 @@ as_gt.gs_design_summary <- function(
     colname_spanner = "Cumulative boundary crossing probability",
     colname_spannersub = c("Alternate hypothesis", "Null hypothesis"),
     footnote = NULL,
-    display_bound = c("Efficacy", "Futility"),
+    display_bound = c("Efficacy", "Futility", "Harm"),
     display_columns = NULL,
     display_inf_bound = FALSE,
     ...) {
@@ -364,6 +379,7 @@ gsd_parts <- function(
   x2 <- x2[, columns]
   x2 <- subset(x2, !is.na(`Alternate hypothesis`) & !is.na(`Null hypothesis`))
   x2 <- subset(x2, Bound %in% bound)
+  x2$Bound <- factor(x2$Bound, levels = bound)
 
   i <- match(c("Alternate hypothesis", "Null hypothesis"), names(x2))
   names(x2)[i] <- spannersub
@@ -382,10 +398,10 @@ gsd_parts <- function(
   )
 
   list(
-    x = arrange(x2, Analysis),
+    x = arrange(x2, Analysis, Bound),
     title = title, subtitle = subtitle,
     footnote = if (!isFALSE(footnote)) footnote %||% gsd_footnote(method, columns),
-    alpha = max(filter(x, Bound == bound[1])[["Null hypothesis"]])
+    alpha = max(filter(x, Bound == "Efficacy")[["Null hypothesis"]])
   )
 }
 
diff --git a/R/gs_bound_summary.R b/R/gs_bound_summary.R
index f2625b58a..35d333e2e 100644
--- a/R/gs_bound_summary.R
+++ b/R/gs_bound_summary.R
@@ -1,6 +1,6 @@
 #' Bound summary table
 #'
-#' Summarizes the efficacy and futility bounds for each analysis.
+#' Summarizes the efficacy, futility, and harm bounds for each analysis.
 #'
 #' @param x Design object.
 #' @param alpha Vector of alpha values to compute additional efficacy columns.
@@ -52,14 +52,25 @@ gs_bound_summary <- function(x, digits = 4, ddigits = 2, tdigits = 0, timename =
     }
   }
   out <- Reduce(cbind, outlist)
-  # Use of union() allows placement of column "Futility" at the far right, but
-  # only if it is returned by gs_bound_summary_single(). This is because
-  # one-sided designs do not produce a Futility column.
+  # Use of union() allows placement of columns "Futility" and "Harm" at the far
+  # right, but only if they are returned by gs_bound_summary_single(). This is
+  # because one-sided designs do not produce a Futility column, and designs
+  # without harm bounds do not produce a Harm column.
   column_order <- union(c("Analysis", "Value", col_efficacy_name), colnames(out))
   out <- out[, column_order]
   return(out)
 }
 
+gs_bound_summary_values <- function(bound, analysis, bound_name, columns) {
+  row_bound <- bound[
+    bound$analysis == analysis & bound$bound == bound_name,
+    columns,
+    drop = FALSE
+  ]
+  if (nrow(row_bound) == 0) return(rep(NA_real_, length(columns)))
+  as.numeric(unlist(row_bound[1, columns], use.names = FALSE))
+}
+
 gs_bound_summary_single <- function(x, col_efficacy_name = "Efficacy", digits,
                                     ddigits, tdigits, timename) {
   # Input
@@ -72,6 +83,8 @@ gs_bound_summary_single <- function(x, col_efficacy_name = "Efficacy", digits,
   col_value <- character()
   col_efficacy <- numeric()
   col_futility <- numeric()
+  col_harm <- numeric()
+  bound_columns <- c("z", "nominal p", "~hr at bound", "probability0", "probability")
 
   for (i in seq_len(nrow(analysis))) {
 
@@ -113,33 +126,31 @@ gs_bound_summary_single <- function(x, col_efficacy_name = "Efficacy", digits,
     )
 
     # Efficacy column
-    row_efficacy <- bound[
-      bound$analysis == i & bound$bound == "upper",
-      c("z", "nominal p", "~hr at bound", "probability0", "probability")
-    ]
-    col_efficacy <- c(col_efficacy, as.numeric(row_efficacy))
+    col_efficacy <- c(col_efficacy, gs_bound_summary_values(bound, i, "upper", bound_columns))
 
     # Futility column
-    row_futility <- bound[
-      bound$analysis == i & bound$bound == "lower",
-      c("z", "nominal p", "~hr at bound", "probability0", "probability")
-    ]
-    col_futility <- c(col_futility, as.numeric(row_futility))
+    col_futility <- c(col_futility, gs_bound_summary_values(bound, i, "lower", bound_columns))
+
+    # Harm column
+    col_harm <- c(col_harm, gs_bound_summary_values(bound, i, "harm", bound_columns))
   }
 
   col_efficacy <- round(col_efficacy, digits)
   col_futility <- round(col_futility, digits)
+  col_harm <- round(col_harm, digits)
 
   out <- data.frame(
     Analysis = col_analysis,
     Value = col_value,
     Efficacy = col_efficacy,
-    Futility = col_futility
+    Futility = col_futility,
+    Harm = col_harm
   )
   colnames(out)[3] <- col_efficacy_name
 
   # One-sided design should not include Futility column
   if (all(is.na(out[["Futility"]]))) out[["Futility"]] <- NULL
+  if (all(is.na(out[["Harm"]]))) out[["Harm"]] <- NULL
 
   return(out)
 }
diff --git a/R/gs_design_ahr.R b/R/gs_design_ahr.R
index ea1f7843e..65f044e5a 100644
--- a/R/gs_design_ahr.R
+++ b/R/gs_design_ahr.R
@@ -98,7 +98,7 @@
 #' # Example 2 ----
 #' # Single analysis
 #' gs_design_ahr(analysis_time = 40)
-#'
+#' 
 #' # Example 3 ----
 #' # Multiple analysis_time
 #' gs_design_ahr(analysis_time = c(12, 24, 36))
@@ -168,6 +168,22 @@
 #'   lpar = rep(-Inf, 3)
 #' )
 #' }
+#'
+#' # Example 8 ----
+#' # Design with an additional harm bound
+#' \donttest{
+#' gs_design_ahr(
+#'   analysis_time = c(12, 24, 36),
+#'   upper = gs_spending_bound,
+#'   upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
+#'   lower = gs_spending_bound,
+#'   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2, timing = NULL),
+#'   test_lower = c(TRUE, TRUE, FALSE),
+#'   harm = gs_spending_bound,
+#'   hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4, timing = NULL),
+#'   test_harm = c(TRUE, TRUE, FALSE)
+#' )
+#' }
 gs_design_ahr <- function(
     enroll_rate = define_enroll_rate(
       duration = c(2, 2, 10),
@@ -186,9 +202,12 @@ gs_design_ahr <- function(
     upar = list(sf = gsDesign::sfLDOF, total_spend = alpha),
     lower = gs_spending_bound,
     lpar = list(sf = gsDesign::sfLDOF, total_spend = beta),
+    harm = gs_b,
+    hpar = -Inf,
     h1_spending = TRUE,
     test_upper = TRUE,
     test_lower = TRUE,
+    test_harm = FALSE,
     info_scale = c("h0_h1_info", "h0_info", "h1_info"),
     r = 18,
     tol = 1e-6,
@@ -200,6 +219,10 @@ gs_design_ahr <- function(
   info_scale <- match.arg(info_scale)
   upper <- match.fun(upper)
   lower <- match.fun(lower)
+  harm <- match.fun(harm)
+
+  # Number of analyses (including final analysis)
+  n_analysis <- max(length(analysis_time), length(info_frac))
 
   # Check inputs ----
   check_analysis_time(analysis_time)
@@ -235,9 +258,6 @@ gs_design_ahr <- function(
   final_event <- y$event[nrow(y)]
   if_alt <- y$event / final_event
 
-  # Number of analyses (including final analysis)
-  n_analysis <- max(length(analysis_time), length(info_frac))
-
   # Initialize the next_time as the study duration
   next_time <- max(analysis_time)
 
@@ -308,6 +328,7 @@ gs_design_ahr <- function(
       alpha = alpha, beta = beta, binding = binding,
       upper = upper, upar = upar, test_upper = test_upper,
       lower = lower, lpar = lpar, test_lower = test_lower,
+      harm = harm, hpar = hpar, test_harm = test_harm,
       r = r, tol = tol
     )
   )
@@ -359,7 +380,6 @@ gs_design_ahr <- function(
       spending_time_upper <- info0 / info0_final
     }
 
-
     bound$spending_time[which(bound$bound == "upper")] <- spending_time_upper
   }
 
@@ -375,6 +395,16 @@ gs_design_ahr <- function(
     bound$spending_time[which(bound$bound == "lower")] <- spending_time_lower
   }
 
+  if (identical(harm, gs_spending_bound)) {
+    if (!is.null(hpar$timing)) {
+      spending_time_harm <- hpar$timing
+    } else {
+      spending_time_harm <- info0 / info0_final
+    }
+
+    bound$spending_time[which(bound$bound == "harm")] <- spending_time_harm
+  }
+
   if (all(is.na(bound$spending_time))){
     bound$spending_time <- NULL
   }
@@ -393,7 +423,8 @@ gs_design_ahr <- function(
     info_scale = info_scale,
     upper = upper, upar = upar,
     lower = lower, lpar = lpar,
-    test_upper = test_upper, test_lower = test_lower,
+    harm = harm, hpar = hpar,
+    test_upper = test_upper, test_lower = test_lower, test_harm = test_harm,
     h1_spending = h1_spending, binding = binding,
     info_scale = info_scale, r = r, tol = tol
   )
diff --git a/R/gs_design_npe.R b/R/gs_design_npe.R
index 3ee9bba6c..0055cf332 100644
--- a/R/gs_design_npe.R
+++ b/R/gs_design_npe.R
@@ -21,7 +21,7 @@
 #' @param beta Type II error.
 
 #' @details
-#' The bound specifications (`upper`, `lower`, `upar`, `lpar`) of \code{gs_design_npe()}
+#' The bound specifications (`upper`, `lower`, `harm`, `upar`, `lpar`, `hpar`) of \code{gs_design_npe()}
 #' will be used to ensure Type I error and other boundary properties are as specified.
 #' See the help file of \code{gs_spending_bound()} for details on spending function.
 #'
@@ -130,7 +130,7 @@
 #'   test_upper = c(FALSE, TRUE, TRUE)
 #' )
 #'
-#' # Example 4 ----
+#' # Example 4a ----
 #' # gs_design_npe with spending function bounds
 #' # 2-sided asymmetric bounds
 #' # Lower spending based on non-zero effect
@@ -144,6 +144,21 @@
 #'   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -1, timing = NULL)
 #' )
 #'
+#' # Example 4b ----
+#' # gs_design_npe with an additional harm bound under the null hypothesis
+#' gs_design_npe(
+#'   theta = c(.1, .2, .3),
+#'   info = (1:3) * 40,
+#'   info0 = (1:3) * 30,
+#'   upper = gs_spending_bound,
+#'   upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
+#'   lower = gs_spending_bound,
+#'   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2, timing = NULL),
+#'   harm = gs_spending_bound,
+#'   hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4, timing = NULL),
+#'   test_harm = c(TRUE, FALSE, TRUE)
+#' )
+#'
 #' # Example 5 ----
 #' # gs_design_npe with two-sided symmetric spend, O'Brien-Fleming spending
 #' # Typically, 2-sided bounds are binding
@@ -177,6 +192,7 @@ gs_design_npe <- function(
     upper = gs_b, upar = qnorm(.975),
     lower = gs_b, lpar = -Inf,
     test_upper = TRUE, test_lower = TRUE, binding = FALSE,
+    harm = gs_b, hpar = -Inf, test_harm = FALSE,
     r = 18, tol = 1e-6) {
   # Check & set up parameters ----
   n_analysis <- length(info)
@@ -191,9 +207,15 @@ gs_design_npe <- function(
 
   upper <- match.fun(upper)
   lower <- match.fun(lower)
+  harm <- match.fun(harm)
   # check test_upper & test_lower
   test_upper <- check_test_upper(test_upper, n_analysis)
   test_lower <- check_test_lower(test_lower, n_analysis)
+  test_harm <- check_test_ul(test_harm, n_analysis, "test_harm")
+
+  if (identical(harm, gs_b) && length(hpar) == 1 && n_analysis > 1) {
+    hpar <- rep(hpar, n_analysis)
+  }
 
   # Set up info ----
   if (is.null(info0)) {
@@ -248,6 +270,41 @@ gs_design_npe <- function(
   min_x <- ((qnorm(alpha) / sqrt(info0[n_analysis]) + qnorm(beta) / sqrt(info[n_analysis])) / theta[n_analysis])^2
   # for a fixed design, this is all you need.
   if (n_analysis == 1) {
+    if (any(test_harm)) {
+      ans_h1 <- gs_power_npe(
+        theta = theta, theta0 = theta0, theta1 = theta1,
+        info = info * min_x, info0 = info0 * min_x, info1 = info1 * min_x,
+        info_scale = info_scale,
+        upper = gs_b, upar = qnorm(1 - alpha), test_upper = test_upper,
+        lower = if (two_sided) lower else gs_b,
+        lpar = if (two_sided) lpar else rep(-Inf, n_analysis),
+        test_lower = test_lower, binding = binding,
+        harm = harm, hpar = hpar, test_harm = test_harm,
+        r = r, tol = tol
+      )
+      ans_h0 <- gs_power_npe(
+        theta = 0, theta0 = theta0, theta1 = theta1,
+        info = info0 * min_x, info0 = info0 * min_x, info1 = info1 * min_x,
+        info_scale = info_scale,
+        upper = gs_b, upar = qnorm(1 - alpha), test_upper = test_upper,
+        lower = if (two_sided) lower else gs_b,
+        lpar = if (two_sided) lpar else rep(-Inf, n_analysis),
+        test_lower = test_lower, binding = binding,
+        harm = harm, hpar = hpar, test_harm = test_harm,
+        r = r, tol = tol
+      )
+      suppressMessages(
+        ans <- ans_h1 |>
+          full_join(
+            ans_h0 |>
+              select(analysis, bound, probability) |>
+              rename(probability0 = probability)
+          )
+      )
+      ans <- ans |> select(analysis, bound, z, probability, probability0, theta, info_frac, info, info0, info1)
+      return(ans)
+    }
+
     ans <- tibble(
       analysis = 1, bound = "upper", z = qnorm(1 - alpha),
       probability = 1 - beta, probability0 = alpha, theta = theta,
@@ -265,7 +322,9 @@ gs_design_npe <- function(
     info = info * min_x, info0 = info0 * min_x, info1 = info * min_x, info_scale = info_scale,
     upper = upper, upar = upar, test_upper = test_upper,
     lower = lower, lpar = lpar, test_lower = test_lower,
-    binding = binding, r = r, tol = tol
+    binding = binding,
+    harm = harm, hpar = hpar, test_harm = test_harm,
+    r = r, tol = tol
   )
   min_power <- (min_temp[min_temp$bound == "upper" & min_temp$analysis == n_analysis, ])$probability
 
@@ -283,7 +342,9 @@ gs_design_npe <- function(
         info = info * max_x, info0 = info0 * max_x, info1 = info * max_x, info_scale = info_scale,
         upper = upper, upar = upar, test_upper = test_upper,
         lower = lower, lpar = lpar, test_lower = test_lower,
-        binding = binding, r = r, tol = tol
+        binding = binding,
+        harm = harm, hpar = hpar, test_harm = test_harm,
+        r = r, tol = tol
       )
       max_power <- (max_temp[max_temp$bound == "upper" & max_temp$analysis == n_analysis, ])$probability
 
@@ -308,7 +369,9 @@ gs_design_npe <- function(
         info = info * micro_x, info0 = info0 * micro_x, info1 = info * micro_x, info_scale = info_scale,
         upper = upper, upar = upar, test_upper = test_upper,
         lower = lower, lpar = lpar, test_lower = test_lower,
-        binding = binding, r = r, tol = tol
+        binding = binding,
+        harm = harm, hpar = hpar, test_harm = test_harm,
+        r = r, tol = tol
       )
       micro_power <- (micro_temp[micro_temp$bound == "upper" & micro_temp$analysis == n_analysis, ])$probability
 
@@ -333,8 +396,15 @@ gs_design_npe <- function(
   errbeta <- function(x) {
     # calculate the probability under H1
     ans_h1 <<- cache_fun(
-      gs_power_npe, theta, theta0, theta1, info * x, info0 * x, info1 * x,
-      info_scale, upper, upar, lower, lpar, test_upper, test_lower, binding, r, tol
+      gs_power_npe,
+      theta = theta, theta0 = theta0, theta1 = theta1,
+      info = info * x, info0 = info0 * x, info1 = info1 * x,
+      info_scale = info_scale,
+      upper = upper, upar = upar, test_upper = test_upper,
+      lower = lower, lpar = lpar, test_lower = test_lower,
+      binding = binding,
+      harm = harm, hpar = hpar, test_harm = test_harm,
+      r = r, tol = tol
     )
     power <- subset(ans_h1, bound == "upper" & analysis == n_analysis)$probability
     1 - beta - power
@@ -357,7 +427,9 @@ gs_design_npe <- function(
     lower = if (two_sided) lower else gs_b,
     lpar = if (two_sided) lpar else rep(-Inf, n_analysis),
     test_upper = test_upper, test_lower = test_lower,
-    binding = binding, r = r, tol = tol
+    binding = binding,
+    harm = harm, hpar = hpar, test_harm = test_harm,
+    r = r, tol = tol
   )
 
   # combine probability under H0 and H1 via direct merge on analysis+bound
diff --git a/R/gs_power_ahr.R b/R/gs_power_ahr.R
index 16e371589..971a517e8 100644
--- a/R/gs_power_ahr.R
+++ b/R/gs_power_ahr.R
@@ -130,6 +130,29 @@
 #'   lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025)
 #' )
 #' }
+#' # Example 5 ----
+#' # 2-sided asymmetric O'Brien-Fleming spending bound with harm bound
+#' # driven by both `event` and `analysis_time`, i.e.,
+#' # both `event` and `analysis_time` are not `NULL`,
+#' # then the analysis will driven by the maximal one, i.e.,
+#' # Time = max(analysis_time, calculated Time for targeted event)
+#' # Events = max(events, calculated events for targeted analysis_time)
+#' \donttest{
+#' gs_power_ahr(
+#'   analysis_time = c(12, 24, 36),
+#'   event = c(30, 40, 50), h1_spending = FALSE,
+#'   binding = TRUE,
+#'   upper = gs_spending_bound,
+#'   upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
+#'   test_upper = c(TRUE, TRUE, TRUE),
+#'   lower = gs_spending_bound,
+#'   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2),
+#'   test_lower = c(TRUE, TRUE, TRUE),
+#'   harm = gs_spending_bound,
+#'   hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4),
+#'   test_harm = c(TRUE, TRUE, TRUE)
+#' )
+#' }
 gs_power_ahr <- function(
     enroll_rate = define_enroll_rate(
       duration = c(2, 2, 10),
@@ -147,8 +170,11 @@ gs_power_ahr <- function(
     upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
     lower = gs_spending_bound,
     lpar = list(sf = gsDesign::sfLDOF, total_spend = NULL),
+    harm = gs_b,
+    hpar = -Inf,
     test_lower = TRUE,
     test_upper = TRUE,
+    test_harm = FALSE,
     ratio = 1,
     binding = FALSE,
     h1_spending = TRUE,
@@ -165,6 +191,7 @@ gs_power_ahr <- function(
 
   upper <- match.fun(upper)
   lower <- match.fun(lower)
+  harm <- match.fun(harm)
 
   # Check if it is two-sided design or not
   if ((identical(lower, gs_b) && (!is.list(lpar))) || all(!test_lower)) {
@@ -190,6 +217,16 @@ gs_power_ahr <- function(
     }
   }
 
+  if (identical(harm, gs_spending_bound)) {
+    if (is.null(hpar$total_spend) && any(test_harm)) {
+      stop("gs_power_ahr: please input the total_spend to the harm spending function.")
+    }
+  }
+
+  if (n_analysis == 1 && test_harm) {
+    stop("gs_power_ahr() harm bound cannot be tested if there is only one analysis.")
+  }
+
   # Calculate the asymptotic variance and statistical information ----
   x <- gs_info_ahr(
     enroll_rate = enroll_rate, fail_rate = fail_rate,
@@ -224,6 +261,7 @@ gs_power_ahr <- function(
     info = x$info, info0 = x$info0, info1 = info1, info_scale = info_scale,
     upper = upper, upar = upar, test_upper = test_upper,
     lower = lower, lpar = lpar, test_lower = test_lower,
+    harm = harm, hpar = hpar, test_harm = test_harm,
     binding = binding, r = r, tol = tol
   )
 
@@ -242,6 +280,7 @@ gs_power_ahr <- function(
       lpar
     },
     test_lower = test_lower,
+    harm = harm, hpar = hpar, test_harm = test_harm,
     binding = binding, r = r, tol = tol
   )
 
@@ -286,7 +325,8 @@ gs_power_ahr <- function(
     alpha = if (identical(upper, gs_spending_bound)) {upar$total_spend} else {NULL},
     upper = upper, upar = upar,
     lower = lower, lpar = lpar,
-    test_lower = test_lower, test_upper = test_upper,
+    harm = harm, hpar = hpar,
+    test_lower = test_lower, test_upper = test_upper, test_harm = test_harm,
     ratio = ratio, binding = binding, h1_spending = h1_spending,
     info_scale = info_scale, r = r, tol = tol
   )
diff --git a/R/gs_power_npe.R b/R/gs_power_npe.R
index 7cafe7171..e05d4881f 100644
--- a/R/gs_power_npe.R
+++ b/R/gs_power_npe.R
@@ -73,6 +73,7 @@
 #'   - \code{gs_b()}: fixed efficacy bounds.
 #' @param lower Function to compute lower bound, which can be set up similarly as `upper`.
 #' See [this vignette](https://merck.github.io/gsDesign2/articles/story-seven-test-types.html).
+#' @param harm Function to compute harm bound, which can be set up similarly as `lower`.
 #' @param upar Parameters passed to `upper`.
 #'   - If `upper = gs_b`, then `upar` is a numerical vector specifying the fixed efficacy bounds per analysis.
 #'   - If `upper = gs_spending_bound`, then `upar` is a list including
@@ -81,6 +82,7 @@
 #'       - `param` for the parameter of the spending function.
 #'       - `timing` specifies spending time if different from information-based spending; see details.
 #' @param lpar Parameters passed to `lower`, which can be set up similarly as `upar.`
+#' @param hpar Parameters passed to `harm`, which can be set up similarly as `lpar.`
 #' @param test_upper Indicator of which analyses should include
 #'   an upper (efficacy) bound;
 #'   single value of `TRUE` (default) indicates all analyses; otherwise,
@@ -91,6 +93,13 @@
 #'   single value of `FALSE` indicated no lower bound; otherwise,
 #'   a logical vector of the same length as `info` should
 #'   indicate which analyses will have a lower bound.
+#' @param test_harm Indicator of which analyses should include a harm bound;
+#'   single value of `TRUE` (default) indicates all analyses;
+#'   single value of `FALSE` indicates no harm bound; otherwise,
+#'   a logical vector of the same length as `info` should
+#'   indicate which analyses will have a harm bound.
+#'   For fixed designs, the harm bound is typically not included.
+#'   For group sequential designs, the harm bound is always smaller than the lower bound (if any).
 #' @param r Integer value controlling grid for numerical integration as in
 #'   Jennison and Turnbull (2000); default is 18, range is 1 to 80.
 #'   Larger values provide larger number of grid points and greater accuracy.
@@ -99,7 +108,7 @@
 #'
 #' @return A tibble with columns of
 #'   - `analysis`: analysis index.
-#'   - `bound`: either of value `"upper"` or `"lower"`, indicating the upper and lower bound.
+#'   - `bound`: one of value `"upper"`, `"lower"`, or `"harm"`, indicating the upper, lower, and harm bound.
 #'   - `z`: the Z-score bounds.
 #'   - `probability`: cumulative probability of crossing the bound at or before the analysis.
 #'   - `theta`: same as the input.
@@ -188,7 +197,7 @@
 #'   lpar = c(qnorm(.1), -Inf, -Inf)
 #' )
 #'
-#' # Example 9 ----
+#' # Example 9a ----
 #' # gs_power_npe with spending function bounds
 #' # Lower spending based on non-zero effect
 #' gs_power_npe(
@@ -210,6 +219,21 @@
 #'   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -1, timing = NULL)
 #' )
 #'
+#' # Example 9b ----
+#' # gs_power_npe with an additional harm bound under the null hypothesis
+#' gs_power_npe(
+#'   theta = c(.1, .2, .3),
+#'   info = (1:3) * 40,
+#'   upper = gs_spending_bound,
+#'   upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
+#'   lower = gs_spending_bound,
+#'   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2, timing = NULL),
+#'   test_lower = c(TRUE, TRUE, TRUE),
+#'   harm = gs_spending_bound,
+#'   hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4, timing = NULL),
+#'   test_harm = c(TRUE, TRUE, TRUE)
+#' )
+#'
 #' # Example 10 ----
 #' # gs_power_npe with two-sided symmetric spend, O'Brien-Fleming spending
 #' # Typically, 2-sided bounds are binding
@@ -262,16 +286,28 @@ gs_power_npe <- function(theta = .1, theta0 = 0, theta1 = theta, # 3 theta
                          upper = gs_b, upar = qnorm(.975),
                          lower = gs_b, lpar = -Inf,
                          test_upper = TRUE, test_lower = TRUE, binding = FALSE,
-                         r = 18, tol = 1e-6) {
+                         harm = gs_b, hpar = -Inf, test_harm = FALSE,
+                         r = 18, tol = 1e-6
+                         ) {
   # Check & set up parameters ----
   n_analysis <- length(info)
+  if (n_analysis == 1 && test_harm) {
+    stop("gs_power_npe() harm bound cannot be tested if there is only one analysis.")
+  }
+  
   theta  <- check_theta(theta,  n_analysis)
   theta0 <- check_theta(theta0, n_analysis)
   theta1 <- check_theta(theta1, n_analysis)
   upper <- match.fun(upper)
   lower <- match.fun(lower)
+  harm <- match.fun(harm)
   test_upper <- check_test_upper(test_upper, n_analysis)
   test_lower <- check_test_lower(test_lower, n_analysis)
+  test_harm <- check_test_ul(test_harm, n_analysis, "test_harm")
+
+  if (identical(harm, gs_b) && length(hpar) == 1 && n_analysis > 1) {
+    hpar <- rep(hpar, n_analysis)
+  }
 
   # Set up info ----
   # impute info
@@ -305,24 +341,39 @@ gs_power_npe <- function(theta = .1, theta0 = 0, theta1 = theta, # 3 theta
   # Initialization ----
   a <- rep(-Inf, n_analysis)
   b <- rep(Inf, n_analysis)
+  harm_z <- rep(-Inf, n_analysis)
   hgm1_0 <- NULL
   hgm1_1 <- NULL
   hgm1 <- NULL
+  hgm1_harm0 <- NULL  # harm-bound grid under theta0
+  hgm1_harm <- NULL   # harm-bound grid under the input theta
   upper_prob <- rep(NA, n_analysis)
   lower_prob <- rep(NA, n_analysis)
+  harm_prob <- rep(NA, n_analysis)
 
   # Calculate crossing prob under H1 ----
   for (k in 1:n_analysis) {
-    # compute/update lower/upper bound
+    # compute/update lower/upper/harm bound
     a[k] <- lower(
       k = k, par = lpar, hgm1 = hgm1_1, info = info1, r = r, tol = tol, test_bound = test_lower,
       theta = theta1, efficacy = FALSE
     )
     b[k] <- upper(k = k, par = upar, hgm1 = hgm1_0, info = info0, r = r, tol = tol, test_bound = test_upper)
+    harm_z[k] <- harm(
+      k = k, par = hpar, hgm1 = hgm1_harm0, info = info0, r = r, tol = tol, test_bound = test_harm,
+      theta = theta0, efficacy = FALSE
+    )
+    if (!test_harm[k]) {
+      harm_z[k] <- -Inf
+    }
+    # Cap harm bound: if harm > futility, set to futility
+    if (!is.na(a[k]) && !is.na(harm_z[k]) && harm_z[k] > a[k]) {
+      harm_z[k] <- a[k]
+    }
 
     # if it is the first analysis
     if (k == 1) {
-      # compute the probability to cross upper/lower bound
+      # compute the probability to cross upper/lower/harm bound
       upper_prob[1] <- if (b[1] < Inf) {
         pnorm(sqrt(info[1]) * (theta[1] - b[1] / sqrt(info0[1])))
       } else {
@@ -333,6 +384,11 @@ gs_power_npe <- function(theta = .1, theta0 = 0, theta1 = theta, # 3 theta
       } else {
         0
       }
+      harm_prob[1] <- if (harm_z[1] > -Inf) {
+        pnorm(-sqrt(info[1]) * (theta[1] - harm_z[1] / sqrt(info0[1])))
+      } else {
+        0
+      }
       # update the grids
       hgm1_0 <- h1(r = r, theta = theta0[1], info = info0[1], a = if (binding) {
         a[1]
@@ -341,6 +397,8 @@ gs_power_npe <- function(theta = .1, theta0 = 0, theta1 = theta, # 3 theta
       }, b = b[1])
       hgm1_1 <- h1(r = r, theta = theta1[1], info = info1[1], a = a[1], b = b[1])
       hgm1 <- h1(r = r, theta = theta[1], info = info[1], a = a[1], b = b[1])
+      hgm1_harm0 <- h1(r = r, theta = theta0[1], info = info0[1], a = harm_z[1], b = b[1])
+      hgm1_harm <- h1(r = r, theta = theta[1], info = info[1], a = harm_z[1], b = b[1])
     } else {
       # compute the probability to cross upper bound
       upper_prob[k] <- if (b[k] < Inf) {
@@ -362,6 +420,16 @@ gs_power_npe <- function(theta = .1, theta0 = 0, theta1 = theta, # 3 theta
       } else {
         0
       }
+      # compute the probability to cross harm bound
+      harm_prob[k] <- if (harm_z[k] > -Inf) {
+        sum(hupdate(
+          theta = theta[k], thetam1 = theta[k - 1],
+          info = info[k], im1 = info[k - 1],
+          a = -Inf, b = harm_z[k], gm1 = hgm1_harm, r = r
+        )$h)
+      } else {
+        0
+      }
 
       # update the grids
       if (k < n_analysis) {
@@ -380,22 +448,52 @@ gs_power_npe <- function(theta = .1, theta0 = 0, theta1 = theta, # 3 theta
           a = a[k], b = b[k], thetam1 = theta[k - 1],
           im1 = info[k - 1], gm1 = hgm1
         )
+        hgm1_harm0 <- hupdate(
+          r = r, theta = theta0[k], info = info0[k],
+          a = harm_z[k], b = b[k], thetam1 = theta0[k - 1],
+          im1 = info0[k - 1], gm1 = hgm1_harm0
+        )
+        hgm1_harm <- hupdate(
+          r = r, theta = theta[k], info = info[k],
+          a = harm_z[k], b = b[k], thetam1 = theta[k - 1],
+          im1 = info[k - 1], gm1 = hgm1_harm
+        )
       }
     }
   }
 
-  ans <- data.frame(
-    analysis = rep(1:n_analysis, 2),
-    bound = c(rep("upper", n_analysis), rep("lower", n_analysis)),
-    z = c(b, a),
-    probability = c(cumsum(upper_prob), cumsum(lower_prob)),
-    theta = rep(theta, 2),
-    theta1 = rep(theta1, 2),
-    info_frac = rep(info / max(info), 2),
-    info = rep(info, 2),
-    info0 = rep(info0, 2),
-    info1 = rep(info1, 2)
-  )
+  if (all(!test_harm)) {
+    ans <- data.frame(
+      analysis = rep(1:n_analysis, 2),
+      bound = c(rep("upper", n_analysis), rep("lower", n_analysis)),
+      z = c(b, a),
+      probability = c(cumsum(upper_prob), cumsum(lower_prob)),
+      theta = rep(theta, 2),
+      theta1 = rep(theta1, 2),
+      info_frac = rep(info / max(info), 2),
+      info = rep(info, 2),
+      info0 = rep(info0, 2),
+      info1 = rep(info1, 2)
+    )
+  } else {
+    # harm bound is only provided when futility is tested
+    inactive_lower <- is.infinite(a)
+    harm_z[inactive_lower] <- -Inf
+    harm_prob[inactive_lower] <- 0
+
+    ans <- data.frame(
+      analysis = rep(1:n_analysis, 3),
+      bound = c(rep("upper", n_analysis), rep("lower", n_analysis), rep("harm", n_analysis)),
+      z = c(b, a, harm_z),
+      probability = c(cumsum(upper_prob), cumsum(lower_prob), cumsum(harm_prob)),
+      theta = rep(theta, 3),
+      theta1 = rep(theta1, 3),
+      info_frac = rep(info / max(info), 3),
+      info = rep(info, 3),
+      info0 = rep(info0, 3),
+      info1 = rep(info1, 3)
+    )
+  }
 
   return(ans)
 }
diff --git a/R/summary.R b/R/summary.R
index 821944c9d..6c0717c51 100644
--- a/R/summary.R
+++ b/R/summary.R
@@ -103,7 +103,9 @@ summary.fixed_design <- function(object, ...) {
 #'   If the vector is unnamed, it must match the length of `col_vars`. If the
 #'   vector is named, you only have to specify the number of digits for the
 #'   columns you want to be displayed differently than the defaults.
-#' @param bound_names Names for bounds; default is `c("Efficacy", "Futility")`.
+#' @param bound_names Names for bounds; default is `c("Efficacy", "Futility", "Harm")`.
+#'   The first two values label upper and lower bounds. If a third value is provided,
+#'   it labels harm bounds; otherwise harm bounds are labeled `"Harm"`.
 #' @param display_spending_time A logical value (TRUE/FALSE) indicating if the spending time
 #' is summarized in the table.
 #'
@@ -257,7 +259,7 @@ summary.gs_design <- function(object,
                               analysis_decimals = NULL,
                               col_vars = NULL,
                               col_decimals = NULL,
-                              bound_names = c("Efficacy", "Futility"),
+                              bound_names = c("Efficacy", "Futility", "Harm"),
                               display_spending_time = FALSE,
                               ...) {
   x <- object
@@ -295,7 +297,8 @@ summary.gs_design <- function(object,
   xy <- x_bound
   # change Upper -> bound_names[1], e.g., Efficacy
   # change Lower -> bound_names[2], e.g., Futility
-  xy$bound <- replace_values(xy$bound, c(upper = bound_names[1], lower = bound_names[2]))
+  # change Harm -> bound_names[3], e.g., Harm
+  xy$bound <- replace_values(xy$bound, c(upper = bound_names[1], lower = bound_names[2], harm = bound_names[3]))
 
   if (!"probability0" %in% names(xy)) xy$probability0 <- "-"
   xy <- xy |> arrange(analysis, desc(bound))
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 732aad044..4e5bc10b5 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -167,6 +167,7 @@ articles:
   contents:
   - articles/story-nph-futility
   - articles/story-seven-test-types
+  - articles/story-harm-bound-schoenfeld
 
 - title: "Designs by AHR"
   desc: >
@@ -179,6 +180,7 @@ articles:
   - articles/story-spending-time-example
   - articles/story-update-boundary
   - articles/story-cp
+  - articles/story-harm-bound-schoenfeld
 
 - title: "Designs with binary endpoints"
   desc: >
diff --git a/man/as_gt.Rd b/man/as_gt.Rd
index 4912d73de..4b0c7d893 100644
--- a/man/as_gt.Rd
+++ b/man/as_gt.Rd
@@ -17,7 +17,7 @@ as_gt(x, ...)
   colname_spanner = "Cumulative boundary crossing probability",
   colname_spannersub = c("Alternate hypothesis", "Null hypothesis"),
   footnote = NULL,
-  display_bound = c("Efficacy", "Futility"),
+  display_bound = c("Efficacy", "Futility", "Harm"),
   display_columns = NULL,
   display_inf_bound = FALSE,
   ...
@@ -45,7 +45,7 @@ the table. To disable footnotes, use \code{footnote = FALSE}.}
 of the gt table.}
 
 \item{display_bound}{A vector of strings specifying the label of the bounds.
-The default is \code{c("Efficacy", "Futility")}.}
+The default is \code{c("Efficacy", "Futility", "Harm")}.}
 
 \item{display_columns}{A vector of strings specifying the variables to be
 displayed in the summary table.}
@@ -114,6 +114,21 @@ gs_design_ahr() |>
   summary() |>
   as_gt()
 
+gs_design_ahr(
+  analysis_time = c(12, 24, 36),
+  upper = gs_spending_bound,
+  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
+  test_upper = c(FALSE, TRUE, TRUE),
+  lower = gs_spending_bound,
+  lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2),
+  test_lower = c(TRUE, TRUE, FALSE),
+  harm = gs_spending_bound,
+  hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4),
+  test_harm = c(TRUE, TRUE, FALSE)
+  ) |>
+ summary() |>
+ as_gt() 
+
 gs_power_ahr(lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.1)) |>
   summary() |>
   as_gt()
@@ -178,7 +193,7 @@ gs_power_wlr(lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.1)) |>
 
 # Example 5 ----
 # Usage of display_bound = ...
-# to either show efficacy bound or futility bound, or both(default)
+# to show selected bounds
 gs_power_wlr(lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.1)) |>
   summary() |>
   as_gt(display_bound = "Efficacy")
diff --git a/man/gsDesign2-package.Rd b/man/gsDesign2-package.Rd
index c31fdc74c..e5a286eeb 100644
--- a/man/gsDesign2-package.Rd
+++ b/man/gsDesign2-package.Rd
@@ -24,6 +24,7 @@ Useful links:
 
 Authors:
 \itemize{
+  \item Yujie Zhao \email{yujie.zhao@merck.com}
   \item Keaven Anderson \email{keaven_anderson@merck.com}
   \item Yilong Zhang \email{elong0527@gmail.com}
   \item John Blischak \email{jdblischak@gmail.com}
diff --git a/man/gs_bound_summary.Rd b/man/gs_bound_summary.Rd
index e4fe3fc8f..91ff48ad3 100644
--- a/man/gs_bound_summary.Rd
+++ b/man/gs_bound_summary.Rd
@@ -33,7 +33,7 @@ estimated timing of each analysis.}
 A data frame
 }
 \description{
-Summarizes the efficacy and futility bounds for each analysis.
+Summarizes the efficacy, futility, and harm bounds for each analysis.
 }
 \examples{
 library(gsDesign2)
diff --git a/man/gs_design_ahr.Rd b/man/gs_design_ahr.Rd
index de4fc18c4..52d6f7950 100644
--- a/man/gs_design_ahr.Rd
+++ b/man/gs_design_ahr.Rd
@@ -18,9 +18,12 @@ gs_design_ahr(
   upar = list(sf = gsDesign::sfLDOF, total_spend = alpha),
   lower = gs_spending_bound,
   lpar = list(sf = gsDesign::sfLDOF, total_spend = beta),
+  harm = gs_b,
+  hpar = -Inf,
   h1_spending = TRUE,
   test_upper = TRUE,
   test_lower = TRUE,
+  test_harm = FALSE,
   info_scale = c("h0_h1_info", "h0_info", "h1_info"),
   r = 18,
   tol = 1e-06,
@@ -70,6 +73,10 @@ See \href{https://merck.github.io/gsDesign2/articles/story-seven-test-types.html
 
 \item{lpar}{Parameters passed to \code{lower}, which can be set up similarly as \code{upar.}}
 
+\item{harm}{Function to compute harm bound, which can be set up similarly as \code{lower}.}
+
+\item{hpar}{Parameters passed to \code{harm}, which can be set up similarly as \code{lpar.}}
+
 \item{h1_spending}{Indicator that lower bound to be set by spending
 under alternate hypothesis (input \code{fail_rate})
 if spending is used for lower bound.
@@ -89,6 +96,14 @@ single value of \code{FALSE} indicated no lower bound; otherwise,
 a logical vector of the same length as \code{info} should
 indicate which analyses will have a lower bound.}
 
+\item{test_harm}{Indicator of which analyses should include a harm bound;
+single value of \code{TRUE} (default) indicates all analyses;
+single value of \code{FALSE} indicates no harm bound; otherwise,
+a logical vector of the same length as \code{info} should
+indicate which analyses will have a harm bound.
+For fixed designs, the harm bound is typically not included.
+For group sequential designs, the harm bound is always smaller than the lower bound (if any).}
+
 \item{info_scale}{Information scale for calculation. Options are:
 \itemize{
 \item \code{"h0_h1_info"} (default): variance under both null and alternative hypotheses is used.
@@ -246,4 +261,20 @@ gs_design_ahr(
   lpar = rep(-Inf, 3)
 )
 }
+
+# Example 8 ----
+# Design with an additional harm bound
+\donttest{
+gs_design_ahr(
+  analysis_time = c(12, 24, 36),
+  upper = gs_spending_bound,
+  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
+  lower = gs_spending_bound,
+  lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2, timing = NULL),
+  test_lower = c(TRUE, TRUE, FALSE),
+  harm = gs_spending_bound,
+  hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4, timing = NULL),
+  test_harm = c(TRUE, TRUE, FALSE)
+)
+}
 }
diff --git a/man/gs_power_ahr.Rd b/man/gs_power_ahr.Rd
index eed8978a8..c9193476d 100644
--- a/man/gs_power_ahr.Rd
+++ b/man/gs_power_ahr.Rd
@@ -15,8 +15,11 @@ gs_power_ahr(
   upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
   lower = gs_spending_bound,
   lpar = list(sf = gsDesign::sfLDOF, total_spend = NULL),
+  harm = gs_b,
+  hpar = -Inf,
   test_lower = TRUE,
   test_upper = TRUE,
+  test_harm = FALSE,
   ratio = 1,
   binding = FALSE,
   h1_spending = TRUE,
@@ -61,6 +64,10 @@ See \href{https://merck.github.io/gsDesign2/articles/story-seven-test-types.html
 
 \item{lpar}{Parameters passed to \code{lower}, which can be set up similarly as \code{upar.}}
 
+\item{harm}{Function to compute harm bound, which can be set up similarly as \code{lower}.}
+
+\item{hpar}{Parameters passed to \code{harm}, which can be set up similarly as \code{lpar.}}
+
 \item{test_lower}{Indicator of which analyses should include a lower bound;
 single value of \code{TRUE} (default) indicates all analyses;
 single value of \code{FALSE} indicated no lower bound; otherwise,
@@ -73,6 +80,14 @@ single value of \code{TRUE} (default) indicates all analyses; otherwise,
 a logical vector of the same length as \code{info} should
 indicate which analyses will have an efficacy bound.}
 
+\item{test_harm}{Indicator of which analyses should include a harm bound;
+single value of \code{TRUE} (default) indicates all analyses;
+single value of \code{FALSE} indicates no harm bound; otherwise,
+a logical vector of the same length as \code{info} should
+indicate which analyses will have a harm bound.
+For fixed designs, the harm bound is typically not included.
+For group sequential designs, the harm bound is always smaller than the lower bound (if any).}
+
 \item{ratio}{Experimental:Control randomization ratio.}
 
 \item{binding}{Indicator of whether futility bound is binding;
@@ -218,4 +233,27 @@ gs_power_ahr(
   lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025)
 )
 }
+# Example 5 ----
+# 2-sided asymmetric O'Brien-Fleming spending bound with harm bound
+# driven by both `event` and `analysis_time`, i.e.,
+# both `event` and `analysis_time` are not `NULL`,
+# then the analysis will driven by the maximal one, i.e.,
+# Time = max(analysis_time, calculated Time for targeted event)
+# Events = max(events, calculated events for targeted analysis_time)
+\donttest{
+gs_power_ahr(
+  analysis_time = c(12, 24, 36),
+  event = c(30, 40, 50), h1_spending = FALSE,
+  binding = TRUE,
+  upper = gs_spending_bound,
+  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
+  test_upper = c(TRUE, TRUE, TRUE),
+  lower = gs_spending_bound,
+  lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2),
+  test_lower = c(TRUE, TRUE, TRUE),
+  harm = gs_spending_bound,
+  hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4),
+  test_harm = c(TRUE, TRUE, TRUE)
+)
+}
 }
diff --git a/man/gs_power_design_npe.Rd b/man/gs_power_design_npe.Rd
index f80bb25f3..b8137f122 100644
--- a/man/gs_power_design_npe.Rd
+++ b/man/gs_power_design_npe.Rd
@@ -23,6 +23,9 @@ gs_design_npe(
   test_upper = TRUE,
   test_lower = TRUE,
   binding = FALSE,
+  harm = gs_b,
+  hpar = -Inf,
+  test_harm = FALSE,
   r = 18,
   tol = 1e-06
 )
@@ -42,6 +45,9 @@ gs_power_npe(
   test_upper = TRUE,
   test_lower = TRUE,
   binding = FALSE,
+  harm = gs_b,
+  hpar = -Inf,
+  test_harm = FALSE,
   r = 18,
   tol = 1e-06
 )
@@ -126,6 +132,18 @@ indicate which analyses will have a lower bound.}
 \item{binding}{Indicator of whether futility bound is binding;
 default of \code{FALSE} is recommended.}
 
+\item{harm}{Function to compute harm bound, which can be set up similarly as \code{lower}.}
+
+\item{hpar}{Parameters passed to \code{harm}, which can be set up similarly as \code{lpar.}}
+
+\item{test_harm}{Indicator of which analyses should include a harm bound;
+single value of \code{TRUE} (default) indicates all analyses;
+single value of \code{FALSE} indicates no harm bound; otherwise,
+a logical vector of the same length as \code{info} should
+indicate which analyses will have a harm bound.
+For fixed designs, the harm bound is typically not included.
+For group sequential designs, the harm bound is always smaller than the lower bound (if any).}
+
 \item{r}{Integer value controlling grid for numerical integration as in
 Jennison and Turnbull (2000); default is 18, range is 1 to 80.
 Larger values provide larger number of grid points and greater accuracy.
@@ -137,7 +155,7 @@ Normally, \code{r} will not be changed by the user.}
 A tibble with columns of
 \itemize{
 \item \code{analysis}: analysis index.
-\item \code{bound}: either of value \code{"upper"} or \code{"lower"}, indicating the upper and lower bound.
+\item \code{bound}: one of value \code{"upper"}, \code{"lower"}, or \code{"harm"}, indicating the upper, lower, and harm bound.
 \item \code{z}: the Z-score bounds.
 \item \code{probability}: cumulative probability of crossing the bound at or before the analysis.
 \item \code{theta}: same as the input.
@@ -175,7 +193,7 @@ The only differences in arguments between the two functions are the \code{alpha}
 parameters used in the \code{gs_design_npe()}.
 }
 \details{
-The bound specifications (\code{upper}, \code{lower}, \code{upar}, \code{lpar}) of \code{gs_design_npe()}
+The bound specifications (\code{upper}, \code{lower}, \code{harm}, \code{upar}, \code{lpar}, \code{hpar}) of \code{gs_design_npe()}
 will be used to ensure Type I error and other boundary properties are as specified.
 See the help file of \code{gs_spending_bound()} for details on spending function.
 }
@@ -316,7 +334,7 @@ gs_design_npe(
   test_upper = c(FALSE, TRUE, TRUE)
 )
 
-# Example 4 ----
+# Example 4a ----
 # gs_design_npe with spending function bounds
 # 2-sided asymmetric bounds
 # Lower spending based on non-zero effect
@@ -330,6 +348,21 @@ gs_design_npe(
   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -1, timing = NULL)
 )
 
+# Example 4b ----
+# gs_design_npe with an additional harm bound under the null hypothesis
+gs_design_npe(
+  theta = c(.1, .2, .3),
+  info = (1:3) * 40,
+  info0 = (1:3) * 30,
+  upper = gs_spending_bound,
+  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
+  lower = gs_spending_bound,
+  lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2, timing = NULL),
+  harm = gs_spending_bound,
+  hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4, timing = NULL),
+  test_harm = c(TRUE, FALSE, TRUE)
+)
+
 # Example 5 ----
 # gs_design_npe with two-sided symmetric spend, O'Brien-Fleming spending
 # Typically, 2-sided bounds are binding
@@ -391,7 +424,7 @@ gs_power_npe(
   lpar = c(qnorm(.1), -Inf, -Inf)
 )
 
-# Example 9 ----
+# Example 9a ----
 # gs_power_npe with spending function bounds
 # Lower spending based on non-zero effect
 gs_power_npe(
@@ -413,6 +446,21 @@ gs_power_npe(
   lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -1, timing = NULL)
 )
 
+# Example 9b ----
+# gs_power_npe with an additional harm bound under the null hypothesis
+gs_power_npe(
+  theta = c(.1, .2, .3),
+  info = (1:3) * 40,
+  upper = gs_spending_bound,
+  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
+  lower = gs_spending_bound,
+  lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2, timing = NULL),
+  test_lower = c(TRUE, TRUE, TRUE),
+  harm = gs_spending_bound,
+  hpar = list(sf = gsDesign::sfHSD, total_spend = 0.2, param = -4, timing = NULL),
+  test_harm = c(TRUE, TRUE, TRUE)
+)
+
 # Example 10 ----
 # gs_power_npe with two-sided symmetric spend, O'Brien-Fleming spending
 # Typically, 2-sided bounds are binding
diff --git a/man/summary.Rd b/man/summary.Rd
index 4cdb08d84..fda3cc608 100644
--- a/man/summary.Rd
+++ b/man/summary.Rd
@@ -13,7 +13,7 @@
   analysis_decimals = NULL,
   col_vars = NULL,
   col_decimals = NULL,
-  bound_names = c("Efficacy", "Futility"),
+  bound_names = c("Efficacy", "Futility", "Harm"),
   display_spending_time = FALSE,
   ...
 )
@@ -37,7 +37,9 @@ If the vector is unnamed, it must match the length of \code{col_vars}. If the
 vector is named, you only have to specify the number of digits for the
 columns you want to be displayed differently than the defaults.}
 
-\item{bound_names}{Names for bounds; default is \code{c("Efficacy", "Futility")}.}
+\item{bound_names}{Names for bounds; default is \code{c("Efficacy", "Futility", "Harm")}.
+The first two values label upper and lower bounds. If a third value is provided,
+it labels harm bounds; otherwise harm bounds are labeled \code{"Harm"}.}
 
 \item{display_spending_time}{A logical value (TRUE/FALSE) indicating if the spending time
 is summarized in the table.}
diff --git a/tests/testit/test-developer-gs_design_ahr.R b/tests/testit/test-developer-gs_design_ahr.R
index fa4249b21..cd1c14b72 100644
--- a/tests/testit/test-developer-gs_design_ahr.R
+++ b/tests/testit/test-developer-gs_design_ahr.R
@@ -271,3 +271,53 @@ assert("Spending time when some analyses are skipped", {
   expected <- x$analysis$info[2:3] / max(x$analysis$info)
   (filter(x$bound, bound == "lower")$spending_time %==% expected)
 })
+
+assert("Harm bound is not provided when it is a fixed design", {
+  x1 <- gs_power_npe(theta = 0.5, theta1 = 0.5, theta0 = 0,
+                      info = 10, info1 = 10, info0 = 11,
+                      upper = gs_b, upar = qnorm(1 - 0.025), test_upper = TRUE,
+                      lower = gs_b, lpar = -Inf, test_lower = FALSE)
+  x2 <- gs_design_npe(theta = 0.5, theta1 = 0.5, theta0 = 0,
+                      info = 10, info1 = 10, info0 = 11,
+                      upper = gs_b, upar = qnorm(1 - 0.025), test_upper = TRUE,
+                      lower = gs_b, lpar = -Inf, test_lower = FALSE)
+  x3 <- gs_design_ahr(analysis_time = 36, info_frac = NULL,
+                      upper = gs_b, upar = qnorm(1 - 0.025), test_upper = TRUE,
+                      lower = gs_b, lpar = -Inf, test_lower = FALSE)
+  x4 <- gs_power_ahr(analysis_time = 36, event = NULL,
+                      upper = gs_b, upar = qnorm(1 - 0.025), test_upper = TRUE,
+                      lower = gs_b, lpar = -Inf, test_lower = FALSE)
+
+  (x1$bound[abs(x1$z) != Inf] == "upper")
+  (x2$bound[abs(x2$z) != Inf] == "upper")
+  (x3$bound$bound[abs(x3$bound$z) != Inf] == "upper")
+  (x4$bound$bound[abs(x4$bound$z) != Inf] == "upper")
+  (has_error(gs_design_ahr(analysis_time = 40, test_harm = TRUE)))
+  (has_error(gs_design_ahr(analysis_time = 40, hpar = -2, test_harm = TRUE)))
+})
+
+assert("Harm bound is always below the lower bound in a group sequential design", {
+  x1 <- gs_design_ahr(analysis_time = c(12, 24, 36), info_frac = NULL,
+                      upper = gs_spending_bound, 
+                      upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025), 
+                      test_upper = TRUE,
+                      lower = gs_spending_bound, 
+                      lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2), 
+                      test_lower = TRUE,
+                      harm = gs_spending_bound,
+                      hpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -4),
+                      test_harm = TRUE)
+  x2 <- gs_power_ahr(analysis_time = NULL, event = c(10, 50, 70),
+                      upper = gs_spending_bound, 
+                      upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025), 
+                      test_upper = TRUE,
+                      lower = gs_spending_bound, 
+                      lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2), 
+                      test_lower = TRUE,
+                      harm = gs_spending_bound,
+                      hpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -4),
+                      test_harm = TRUE)
+
+  (all(x1$bound$z[x1$bound$bound == "lower"] - x1$bound$z[x1$bound$bound == "harm"] >= 0))
+  (all(x2$bound$z[x2$bound$bound == "lower"] - x2$bound$z[x2$bound$bound == "harm"] >= 0))
+})
\ No newline at end of file
diff --git a/tests/testit/test-developer-gs_design_npe.R b/tests/testit/test-developer-gs_design_npe.R
index 619c0b84d..561959c15 100644
--- a/tests/testit/test-developer-gs_design_npe.R
+++ b/tests/testit/test-developer-gs_design_npe.R
@@ -268,3 +268,13 @@ assert("2-sided symmetric spend", {
     dplyr::arrange(analysis, bound)
   (as.data.frame(x1_c) %==% as.data.frame(x2))
 })
+
+assert("Harm bound is not provided for fixed designs", {
+  (has_error(
+    gs_design_npe(
+      theta = 0.1, info = 40,
+      upper = gs_b, upar = -qnorm(0.025), test_upper = TRUE,
+      lower = gs_b, lpar = -Inf, test_lower = FALSE,
+      harm = gs_b, hpar = -2, test_harm = TRUE)
+  ))
+})
\ No newline at end of file
diff --git a/tests/testit/test-developer-gs_power_npe.R b/tests/testit/test-developer-gs_power_npe.R
index acb9f08b6..a7e85589e 100644
--- a/tests/testit/test-developer-gs_power_npe.R
+++ b/tests/testit/test-developer-gs_power_npe.R
@@ -320,3 +320,49 @@ assert("Expect equal with gsDesign::gsProbability outcome for efficacy bounds",
   (all.equal(x$z[x$bound == "upper"], z$upper$bound, tolerance = 2e-6))
   (all.equal(x$probability[x$bound == "upper"], cumsum(z$upper$prob), tolerance = 6e-6))
 })
+
+assert("Harm bound is not provided for fixed designs", {
+  (has_error(
+    gs_power_npe(
+      theta = 0.1, info = 40,
+      upper = gs_b, upar = -qnorm(0.025), test_upper = TRUE,
+      lower = gs_b, lpar = -Inf, test_lower = FALSE,
+      harm = gs_b, hpar = -2, test_harm = TRUE)
+  ))
+})
+
+assert("Harm bound - Cap harm bound at futility bound", {
+  x <- gs_power_npe(
+    theta = c(.1, .2, .3),
+    info = (1:3) * 40,
+    upper = gs_spending_bound,
+    upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
+    lower = gs_spending_bound,
+    lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2, timing = NULL),
+    test_lower = c(TRUE, TRUE, TRUE),
+    harm = gs_spending_bound,
+    hpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -4, timing = NULL),
+    test_harm = c(TRUE, TRUE, TRUE)
+  )
+  harm_bound <- x |> dplyr::filter(bound == "harm") |> dplyr::pull(z)
+  futility_bound <- x |> dplyr::filter(bound == "lower") |> dplyr::pull(z)
+  (harm_bound <= futility_bound)
+
+  x <- gs_power_npe(
+    theta = c(.1, .2, .3),
+    info = (1:3) * 40,
+    upper = gs_spending_bound,
+    upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
+    lower = gs_spending_bound,
+    lpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -2, timing = NULL),
+    test_lower = c(TRUE, TRUE, FALSE),
+    harm = gs_spending_bound,
+    hpar = list(sf = gsDesign::sfHSD, total_spend = 0.1, param = -4, timing = NULL),
+    test_harm = c(TRUE, TRUE, TRUE)
+  )
+  harm_analysis <- x |> dplyr::filter(bound == "harm", !is.infinite(z)) |> dplyr::pull(analysis)
+  harm_bound <- x |> dplyr::filter(bound == "harm") |> dplyr::pull(z)
+  futility_bound <- x |> dplyr::filter(bound == "lower", analysis %in% harm_analysis) |> dplyr::pull(z)
+  (harm_bound <= futility_bound)
+  (harm_analysis %==% 1:2)
+})
diff --git a/tests/testit/test-independent-as_gt.md b/tests/testit/test-independent-as_gt.md
index c47fb1e89..1da54ef05 100644
--- a/tests/testit/test-independent-as_gt.md
+++ b/tests/testit/test-independent-as_gt.md
@@ -147,7 +147,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  AHR approximations of \textasciitilde{}HR at bound\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){5-6}
@@ -182,7 +182,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  AHR approximations of \textasciitilde{}HR at bound\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){5-6}
@@ -190,18 +190,18 @@ Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}HR at bound
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 1 Time: 14.9 N: 108 Events: 30 AHR: 0.79 Information fraction: 0.6} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -1.17 & 0.8792 & 1.5336 & 0.0349 & 0.1208 \\ 
 Efficacy & 2.67 & 0.0038 & 0.3774 & 0.0231 & 0.0038 \\ 
+Futility & -1.17 & 0.8792 & 1.5336 & 0.0349 & 0.1208 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.74 Information fraction: 0.8} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.66 & 0.7462 & 1.2331 & 0.0668 & 0.2655 \\ 
 Efficacy & 2.29 & 0.0110 & 0.4849 & 0.0897 & 0.0122 \\ 
+Futility & -0.66 & 0.7462 & 1.2331 & 0.0668 & 0.2655 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.23 & 0.5897 & 1.0662 & 0.1008 & 0.4303 \\ 
 Efficacy & 2.03 & 0.0211 & 0.5631 & 0.2070 & 0.0250 \\ 
+Futility & -0.23 & 0.5897 & 1.0662 & 0.1008 & 0.4303 \\ 
 \bottomrule
 \end{tabular*}
 \begin{minipage}{\linewidth}
@@ -228,7 +228,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  WLR approximation of \textasciitilde{}wHR at bound\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){5-6}
@@ -275,7 +275,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  WLR approximation of \textasciitilde{}wHR at bound\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}}\textsuperscript{\textit{2}} \\ 
 \cmidrule(lr){5-6}
@@ -283,18 +283,18 @@ Bound & Z & Nominal p & \textasciitilde{}wHR at bound\textsuperscript{\textit{3}
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 1 Time: 14.9 N: 108 Events: 30 AHR: 0.79 Information fraction: 0.6\textsuperscript{\textit{4}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -1.17 & 0.8798 & 1.5353 & 0.0341 & 0.1202 \\ 
 Efficacy & 2.68 & 0.0037 & 0.3765 & 0.0217 & 0.0037 \\ 
+Futility & -1.17 & 0.8798 & 1.5353 & 0.0341 & 0.1202 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8\textsuperscript{\textit{4}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.66 & 0.7452 & 1.2319 & 0.0664 & 0.2664 \\ 
 Efficacy & 2.29 & 0.0110 & 0.4846 & 0.0886 & 0.0121 \\ 
+Futility & -0.66 & 0.7452 & 1.2319 & 0.0664 & 0.2664 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1\textsuperscript{\textit{4}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.22 & 0.5881 & 1.0650 & 0.1002 & 0.4319 \\ 
 Efficacy & 2.03 & 0.0212 & 0.5631 & 0.2071 & 0.0250 \\ 
+Futility & -0.22 & 0.5881 & 1.0650 & 0.1002 & 0.4319 \\ 
 \bottomrule
 \end{tabular*}
 \begin{minipage}{\linewidth}
@@ -337,7 +337,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  MaxCombo approximation\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrr}
 \toprule
  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){4-5}
@@ -345,18 +345,18 @@ Bound & Z & Nominal p\textsuperscript{\textit{1}} & Alternate hypothesis & Null
 \midrule\addlinespace[2.5pt]
 \multicolumn{5}{l}{Analysis: 1 Time: 12 N: 500 Events: 107.4 AHR: 0.84 Event fraction: 0.32\textsuperscript{\textit{2}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -1 & 0.8413 & 0.0293 & 0.0000 \\ 
 Efficacy & 3 & 0.0013 & 0.0175 & 0.0013 \\ 
+Futility & -1 & 0.8413 & 0.0293 & 0.0000 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{5}{l}{Analysis: 2 Time: 24 N: 500 Events: 246.3 AHR: 0.72 Event fraction: 0.74\textsuperscript{\textit{2}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & 0 & 0.5000 & 0.0314 & 0.0000 \\ 
 Efficacy & 2 & 0.0228 & 0.7261 & 0.0233 \\ 
+Futility & 0 & 0.5000 & 0.0314 & 0.0000 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{5}{l}{Analysis: 3 Time: 36 N: 500 Events: 331.3 AHR: 0.68 Event fraction: 1\textsuperscript{\textit{2}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & 1 & 0.1587 & 0.0326 & 0.0000 \\ 
 Efficacy & 1 & 0.1587 & 0.9674 & 0.1956 \\ 
+Futility & 1 & 0.1587 & 0.0326 & 0.0000 \\ 
 \bottomrule
 \end{tabular*}
 \begin{minipage}{\linewidth}
@@ -383,7 +383,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  measured by risk difference\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){5-6}
@@ -417,7 +417,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  measured by risk difference\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){5-6}
@@ -425,8 +425,8 @@ Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}Risk differ
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 1 N: 40 Risk difference: 0.05 Information fraction: 0.67} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -1.28 & 0.9000 & -0.1537 & 0.0444 & 0.1000 \\ 
 Efficacy & 3.71 & 0.0001 & 0.4448 & 0.0005 & 0.0001 \\ 
+Futility & -1.28 & 0.9000 & -0.1537 & 0.0444 & 0.1000 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 2 N: 50 Risk difference: 0.05 Information fraction: 0.83} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
@@ -461,7 +461,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  from gs\_power\_wlr\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){5-6}
@@ -469,18 +469,18 @@ Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}wHR at boun
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 1 Time: 14.9 N: 108 Events: 30 AHR: 0.79 Information fraction: 0.6\textsuperscript{\textit{3}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -1.17 & 0.8798 & 1.5353 & 0.0341 & 0.1202 \\ 
 Efficacy & 2.68 & 0.0037 & 0.3765 & 0.0217 & 0.0037 \\ 
+Futility & -1.17 & 0.8798 & 1.5353 & 0.0341 & 0.1202 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8\textsuperscript{\textit{3}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.66 & 0.7452 & 1.2319 & 0.0664 & 0.2664 \\ 
 Efficacy & 2.29 & 0.0110 & 0.4846 & 0.0886 & 0.0121 \\ 
+Futility & -0.66 & 0.7452 & 1.2319 & 0.0664 & 0.2664 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1\textsuperscript{\textit{3}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.22 & 0.5881 & 1.0650 & 0.1002 & 0.4319 \\ 
 Efficacy & 2.03 & 0.0212 & 0.5631 & 0.2071 & 0.0250 \\ 
+Futility & -0.22 & 0.5881 & 1.0650 & 0.1002 & 0.4319 \\ 
 \bottomrule
 \end{tabular*}
 \begin{minipage}{\linewidth}
@@ -511,7 +511,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  WLR approximation of \textasciitilde{}wHR at bound\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative probability to cross boundaries}} \\ 
 \cmidrule(lr){5-6}
@@ -519,18 +519,18 @@ Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}wHR at boun
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 1 Time: 14.9 N: 108 Events: 30 AHR: 0.79 Information fraction: 0.6\textsuperscript{\textit{3}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -1.17 & 0.8798 & 1.5353 & 0.0341 & 0.1202 \\ 
 Efficacy & 2.68 & 0.0037 & 0.3765 & 0.0217 & 0.0037 \\ 
+Futility & -1.17 & 0.8798 & 1.5353 & 0.0341 & 0.1202 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8\textsuperscript{\textit{3}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.66 & 0.7452 & 1.2319 & 0.0664 & 0.2664 \\ 
 Efficacy & 2.29 & 0.0110 & 0.4846 & 0.0886 & 0.0121 \\ 
+Futility & -0.66 & 0.7452 & 1.2319 & 0.0664 & 0.2664 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1\textsuperscript{\textit{3}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.22 & 0.5881 & 1.0650 & 0.1002 & 0.4319 \\ 
 Efficacy & 2.03 & 0.0212 & 0.5631 & 0.2071 & 0.0250 \\ 
+Futility & -0.22 & 0.5881 & 1.0650 & 0.1002 & 0.4319 \\ 
 \bottomrule
 \end{tabular*}
 \begin{minipage}{\linewidth}
@@ -569,7 +569,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  WLR approximation of \textasciitilde{}wHR at bound\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}}\textsuperscript{\textit{2}} \\ 
 \cmidrule(lr){5-6}
@@ -577,18 +577,18 @@ Bound & Z & Nominal p & \textasciitilde{}wHR at bound\textsuperscript{\textit{3}
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 1 Time: 14.9 N: 108 Events: 30 AHR: 0.79 Information fraction: 0.6\textsuperscript{\textit{4}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -1.17 & 0.8798 & 1.5353 & 0.0341 & 0.1202 \\ 
 Efficacy & 2.68 & 0.0037 & 0.3765 & 0.0217 & 0.0037 \\ 
+Futility & -1.17 & 0.8798 & 1.5353 & 0.0341 & 0.1202 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8\textsuperscript{\textit{4}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.66 & 0.7452 & 1.2319 & 0.0664 & 0.2664 \\ 
 Efficacy & 2.29 & 0.0110 & 0.4846 & 0.0886 & 0.0121 \\ 
+Futility & -0.66 & 0.7452 & 1.2319 & 0.0664 & 0.2664 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{6}{l}{Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1\textsuperscript{\textit{4}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & -0.22 & 0.5881 & 1.0650 & 0.1002 & 0.4319 \\ 
 Efficacy & 2.03 & 0.0212 & 0.5631 & 0.2071 & 0.0250 \\ 
+Futility & -0.22 & 0.5881 & 1.0650 & 0.1002 & 0.4319 \\ 
 \bottomrule
 \end{tabular*}
 \begin{minipage}{\linewidth}
@@ -617,7 +617,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  WLR approximation of \textasciitilde{}wHR at bound\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrrr}
 \toprule
  &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){5-6}
@@ -661,7 +661,7 @@ gt_to_latex(output)
 {\fontsize{14}{17}\selectfont  WLR approximation of \textasciitilde{}wHR at bound\fontsize{12}{15}\selectfont }
 } 
 \fontsize{12.0pt}{14.0pt}\selectfont
-\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrr}
+\begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}crrrr}
 \toprule
  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
 \cmidrule(lr){4-5}
@@ -669,18 +669,18 @@ Bound & Nominal p\textsuperscript{\textit{1}} & Z & Alternate hypothesis & Null
 \midrule\addlinespace[2.5pt]
 \multicolumn{5}{l}{Analysis: 1 Time: 14.9 N: 108 Events: 30 AHR: 0.79 Information fraction: 0.6\textsuperscript{\textit{2}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & 0.8798 & -1.17 & 0.0341 & 0.1202 \\ 
 Efficacy & 0.0037 & 2.68 & 0.0217 & 0.0037 \\ 
+Futility & 0.8798 & -1.17 & 0.0341 & 0.1202 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{5}{l}{Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8\textsuperscript{\textit{2}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & 0.7452 & -0.66 & 0.0664 & 0.2664 \\ 
 Efficacy & 0.0110 & 2.29 & 0.0886 & 0.0121 \\ 
+Futility & 0.7452 & -0.66 & 0.0664 & 0.2664 \\ 
 \midrule\addlinespace[2.5pt]
 \multicolumn{5}{l}{Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1\textsuperscript{\textit{2}}} \\[2.5pt] 
 \midrule\addlinespace[2.5pt]
-Futility & 0.5881 & -0.22 & 0.1002 & 0.4319 \\ 
 Efficacy & 0.0212 & 2.03 & 0.2071 & 0.0250 \\ 
+Futility & 0.5881 & -0.22 & 0.1002 & 0.4319 \\ 
 \bottomrule
 \end{tabular*}
 \begin{minipage}{\linewidth}
diff --git a/tests/testit/test-independent-as_rtf.md b/tests/testit/test-independent-as_rtf.md
index e08267cd6..c14df378e 100644
--- a/tests/testit/test-independent-as_rtf.md
+++ b/tests/testit/test-independent-as_rtf.md
@@ -462,20 +462,6 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.535}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Efficacy}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 2.68}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
@@ -484,10 +470,6 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super c}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
@@ -495,11 +477,15 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.232}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.535}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super c}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
@@ -516,10 +502,6 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0121}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super c}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
@@ -527,11 +509,15 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.065}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.232}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super c}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
@@ -548,6 +534,20 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.025}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.065}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrdb\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 {\super a} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\line{\super b} Approximate hazard ratio to cross bound.\line{\super c} wAHR is the weighted AHR.}\cell
 \intbl\row\pard
@@ -704,20 +704,6 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.535}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Efficacy}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 2.68}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
@@ -726,10 +712,6 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super c}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
@@ -737,11 +719,15 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.232}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.535}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super c}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
@@ -758,10 +744,6 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0121}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super c}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
@@ -769,11 +751,15 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.065}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.232}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super c}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
@@ -790,6 +776,20 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.025}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.065}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrdb\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 {\super a} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\line{\super b} Approximate hazard ratio to cross bound.\line{\super c} wAHR is the weighted AHR.}\cell
 \intbl\row\pard
@@ -866,20 +866,6 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.535}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Efficacy}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 2.68}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
@@ -888,10 +874,6 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super c}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
@@ -899,11 +881,15 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.232}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.535}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super c}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
@@ -920,10 +906,6 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0121}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super c}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
@@ -931,11 +913,15 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.065}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.232}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super c}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
@@ -952,6 +938,20 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.025}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.065}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrdb\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 {\super a} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\line{\super b} Approximate hazard ratio to cross bound.\line{\super c} wAHR is the weighted AHR.}\cell
 \intbl\row\pard
@@ -1036,20 +1036,6 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.535}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Efficacy}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 2.68}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
@@ -1058,10 +1044,6 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super b}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
@@ -1069,11 +1051,15 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.232}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.535}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super b}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
@@ -1090,10 +1076,6 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0121}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super b}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
@@ -1101,11 +1083,15 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.065}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.232}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super b}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
@@ -1122,6 +1108,20 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.025}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3000
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx4500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx6000
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7500
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 1.065}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrdb\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 {\super a} approximate weighted hazard ratio to cross bound.\line{\super b} wAHR is the weighted AHR.\line{\super c} the crossing probability.\line{\super d} this table is generated by gs_power_wlr.}\cell
 \intbl\row\pard
@@ -1194,18 +1194,6 @@ cat(readLines(path), sep = "\n")
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx5400
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7200
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1800
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3600
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx5400
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7200
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Efficacy}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 2.68}\cell
@@ -1213,20 +1201,20 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0037}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super b}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1800
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3600
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx5400
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7200
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.8798}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -1.17}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0341}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1202}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 2 Time: 19.2 N: 108 Events: 40 AHR: 0.75 Information fraction: 0.8 {\super b}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1800
@@ -1241,20 +1229,20 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0121}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
-\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super b}}\cell
-\intbl\row\pard
-\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1800
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3600
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx5400
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7200
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
-\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.7452}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.66}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.0664}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.2664}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Analysis: 3 Time: 24.5 N: 108 Events: 50 AHR: 0.71 Information fraction: 1 {\super b}}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1800
@@ -1269,6 +1257,18 @@ cat(readLines(path), sep = "\n")
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.025}\cell
 \intbl\row\pard
 \trowd\trgaph108\trleft0\trqc
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx1800
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx3600
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx5400
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx7200
+\clbrdrl\brdrs\brdrw15\clbrdrt\brdrs\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrs\brdrw15\clvertalt\cellx9000
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 Futility}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.5881}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 -0.22}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.1002}\cell
+\pard\hyphpar0\sb15\sa15\fi0\li0\ri0\qc\fs18{\f0 0.4319}\cell
+\intbl\row\pard
+\trowd\trgaph108\trleft0\trqc
 \clbrdrl\brdrs\brdrw15\clbrdrt\brdrw15\clbrdrr\brdrs\brdrw15\clbrdrb\brdrdb\brdrw15\clvertalt\cellx9000
 \pard\hyphpar0\sb15\sa15\fi0\li0\ri0\ql\fs18{\f0 {\super a} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\line{\super b} wAHR is the weighted AHR.}\cell
 \intbl\row\pard
diff --git a/vignettes/articles/story-harm-bound-schoenfeld.Rmd b/vignettes/articles/story-harm-bound-schoenfeld.Rmd
new file mode 100644
index 000000000..9eff5f2d2
--- /dev/null
+++ b/vignettes/articles/story-harm-bound-schoenfeld.Rmd
@@ -0,0 +1,546 @@
+---
+title: "Reproducing gsSurv Schoenfeld bounds with harm bounds"
+author: "Keaven Anderson"
+output:
+  rmarkdown::html_document:
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    number_sections: true
+    highlight: "textmate"
+    css: "custom.css"
+vignette: >
+  %\VignetteIndexEntry{Reproducing gsSurv Schoenfeld bounds with harm bounds}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include=FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r, message=FALSE, warning=FALSE}
+library(gsDesign2)
+library(dplyr)
+library(gt)
+```
+
+# Overview
+
+This vignette shows that `gs_design_ahr()` can reproduce the Z-boundaries from
+`gsDesign::gsSurv(method = "Schoenfeld")` for a proportional hazards design
+with a single hazard ratio.
+The Schoenfeld approximation uses the null hypothesis variance, so the
+corresponding `gs_design_ahr()` calls use `info_scale = "h0_info"`.
+
+The comparison covers `gsSurv()` `test.type` values 1 through 8.
+Types 7 and 8 include the additional harm bound.
+We use the same spending functions, spending times, and numerical integration
+settings in both packages.
+
+# Design assumptions
+
+We use a 3-analysis design with information fractions 35%, 70%, and 100%.
+Enrollment is uniform for 16 months, the total trial duration is 36 months, and
+the control arm has exponential failure with a 12-month median.
+The experimental-to-control hazard ratio is constant at 0.7.
+
+```{r}
+trial_duration <- 36
+info_frac <- c(.35, .7, 1)
+
+enroll_rate <- define_enroll_rate(duration = 16, rate = 1)
+minfup <- trial_duration - sum(enroll_rate$duration)
+
+fail_rate <- define_fail_rate(
+  duration = Inf,
+  fail_rate = log(2) / 12,
+  hr = .7,
+  dropout_rate = -log(.99) / 12
+)
+
+alpha <- 0.025
+beta <- 0.15
+astar_candidates <- c(.10, .15, .20)
+astar <- .20
+ratio <- 1
+r <- 32
+tol <- 1e-8
+```
+
+The `astar` argument is used for null-spending lower bounds in test types 5 and
+6 and for harm bounds in test types 7 and 8.
+For harm bounds, `astar` controls the cumulative null probability of crossing a
+lower-tail boundary, representing evidence that experimental treatment is worse
+than control.
+We compare `astar` values of 0.10, 0.15, and 0.20 and select `astar = 0.20`.
+This choice is intentionally somewhat more liberal than `astar = 0.15` because
+the harm bound is meant to warn of a potentially important trend in the wrong
+direction, not to require definitive evidence of harm before raising concern.
+It still keeps all harm Z-bounds below 0.
+
+```{r}
+candidate_harm_bounds <- lapply(astar_candidates, function(astar_candidate) {
+  gs_harm <- gsDesign::gsSurv(
+    k = length(info_frac),
+    test.type = 7,
+    alpha = alpha,
+    beta = beta,
+    astar = astar_candidate,
+    timing = info_frac,
+    T = trial_duration,
+    minfup = minfup,
+    lambdaC = fail_rate$fail_rate,
+    eta = fail_rate$dropout_rate,
+    hr = fail_rate$hr,
+    ratio = ratio,
+    sfu = gsDesign::sfHSD,
+    sfupar = -4,
+    sfl = gsDesign::sfHSD,
+    sflpar = -2,
+    sfharm = gsDesign::sfHSD,
+    sfharmparam = -2,
+    r = r,
+    tol = tol,
+    method = "Schoenfeld"
+  )
+
+  data.frame(
+    astar = astar_candidate,
+    analysis = seq_along(gs_harm$harm$bound),
+    harm_z = as.numeric(gs_harm$harm$bound),
+    harm_p_lower_tail = pnorm(as.numeric(gs_harm$harm$bound))
+  )
+}) |>
+  do.call(what = rbind) |>
+  mutate(
+    analysis = factor(
+      analysis,
+      levels = seq_along(info_frac),
+      labels = c(paste("IA", seq_len(length(info_frac) - 1)), "Final")
+    )
+  )
+
+candidate_harm_bounds |>
+  gt() |>
+  fmt_number(
+    columns = c("astar", "harm_z", "harm_p_lower_tail"),
+    decimals = 3
+  )
+```
+
+# Boundary specifications
+
+We use the same Hwang-Shih-DeCani spending functions in both packages.
+The upper boundary is O'Brien-Fleming-like with `param = -4`.
+The lower futility and harm boundaries use `param = -2`.
+
+```{r}
+upper_par <- list(
+  sf = gsDesign::sfHSD,
+  total_spend = alpha,
+  param = -4,
+  timing = info_frac
+)
+
+lower_beta_par <- list(
+  sf = gsDesign::sfHSD,
+  total_spend = beta,
+  param = -2,
+  timing = info_frac
+)
+
+lower_null_par <- list(
+  sf = gsDesign::sfHSD,
+  total_spend = astar,
+  param = -2,
+  timing = info_frac
+)
+
+harm_par <- lower_null_par
+```
+
+# Design constructors
+
+The `gsSurv()` constructor is direct.
+For `gs_design_ahr()`, the mapping is:
+
+- `test.type = 1`: one-sided efficacy only.
+- `test.type = 2`: symmetric two-sided design; use the upper spending rule for
+  the lower bound and set `h1_spending = FALSE`.
+- `test.type = 3` and `4`: beta-spending futility, with binding and
+  non-binding lower bounds, respectively.
+- `test.type = 5` and `6`: null-spending futility, with binding and
+  non-binding lower bounds, respectively.
+- `test.type = 7` and `8`: beta-spending futility plus null-spending harm,
+  with binding and non-binding lower/harm bounds, respectively.
+
+```{r}
+make_gs_surv <- function(test_type) {
+  gsDesign::gsSurv(
+    k = length(info_frac),
+    test.type = test_type,
+    alpha = alpha,
+    beta = beta,
+    astar = astar,
+    timing = info_frac,
+    T = trial_duration,
+    minfup = minfup,
+    lambdaC = fail_rate$fail_rate,
+    eta = fail_rate$dropout_rate,
+    hr = fail_rate$hr,
+    ratio = ratio,
+    sfu = gsDesign::sfHSD,
+    sfupar = -4,
+    sfl = gsDesign::sfHSD,
+    sflpar = -2,
+    sfharm = gsDesign::sfHSD,
+    sfharmparam = -2,
+    r = r,
+    tol = tol,
+    method = "Schoenfeld"
+  )
+}
+
+make_gs_design_ahr <- function(test_type) {
+  args <- list(
+    enroll_rate = enroll_rate,
+    fail_rate = fail_rate,
+    alpha = alpha,
+    beta = beta,
+    ratio = ratio,
+    info_frac = info_frac,
+    analysis_time = trial_duration,
+    r = r,
+    tol = tol,
+    info_scale = "h0_info",
+    upper = gs_spending_bound,
+    upar = upper_par,
+    lower = gs_b,
+    lpar = rep(-Inf, length(info_frac)),
+    test_lower = FALSE,
+    harm = gs_b,
+    hpar = rep(-Inf, length(info_frac)),
+    test_harm = FALSE,
+    binding = FALSE,
+    h1_spending = TRUE
+  )
+
+  if (test_type == 2) {
+    args$lower <- gs_spending_bound
+    args$lpar <- upper_par
+    args$test_lower <- TRUE
+    args$binding <- TRUE
+    args$h1_spending <- FALSE
+  }
+
+  if (test_type %in% 3:4) {
+    args$lower <- gs_spending_bound
+    args$lpar <- lower_beta_par
+    args$test_lower <- TRUE
+    args$binding <- test_type == 3
+    args$h1_spending <- TRUE
+  }
+
+  if (test_type %in% 5:6) {
+    args$lower <- gs_spending_bound
+    args$lpar <- lower_null_par
+    args$test_lower <- TRUE
+    args$binding <- test_type == 5
+    args$h1_spending <- FALSE
+  }
+
+  if (test_type %in% 7:8) {
+    args$lower <- gs_spending_bound
+    args$lpar <- lower_beta_par
+    args$test_lower <- TRUE
+    args$binding <- test_type == 7
+    args$h1_spending <- TRUE
+    args$harm <- gs_spending_bound
+    args$hpar <- harm_par
+    args$test_harm <- TRUE
+  }
+
+  do.call(gs_design_ahr, args)
+}
+```
+
+# Bound comparison
+
+We extract upper, lower, and harm Z-boundaries from both packages and compare
+them analysis by analysis.
+Infinite bounds are not included.
+
+```{r}
+extract_gs_surv_bounds <- function(x, test_type) {
+  add_bound <- function(bound, z) {
+    if (is.null(z)) {
+      return(NULL)
+    }
+
+    z <- as.numeric(z)
+    keep <- is.finite(z) & abs(z) < 20
+
+    data.frame(
+      test_type = test_type,
+      analysis = seq_along(z)[keep],
+      bound = bound,
+      gsSurv = z[keep]
+    )
+  }
+
+  do.call(
+    rbind,
+    Filter(
+      Negate(is.null),
+      list(
+        add_bound("upper", x$upper$bound),
+        if (test_type != 1) add_bound("lower", x$lower$bound),
+        if (test_type %in% 7:8) add_bound("harm", x$harm$bound)
+      )
+    )
+  )
+}
+
+extract_gs_design_ahr_bounds <- function(x, test_type) {
+  x$bound |>
+    transmute(
+      test_type = test_type,
+      analysis,
+      bound,
+      gs_design_ahr = z
+    )
+}
+
+comparison <- lapply(1:8, function(test_type) {
+  gs_surv <- make_gs_surv(test_type)
+  gs_ahr <- make_gs_design_ahr(test_type)
+
+  merge(
+    extract_gs_surv_bounds(gs_surv, test_type),
+    extract_gs_design_ahr_bounds(gs_ahr, test_type),
+    by = c("test_type", "analysis", "bound"),
+    all = TRUE
+  )
+}) |>
+  do.call(what = rbind) |>
+  mutate(
+    difference = gs_design_ahr - gsSurv,
+    abs_difference = abs(difference),
+    bound = factor(bound, levels = c("upper", "lower", "harm"))
+  ) |>
+  arrange(test_type, analysis, bound)
+
+stopifnot(max(comparison$abs_difference, na.rm = TRUE) < 1e-5)
+stopifnot(all(comparison$gs_design_ahr[comparison$bound == "harm"] < 0))
+```
+
+The maximum absolute Z-boundary difference is below `1e-5` for every test type.
+All harm bounds in the selected design are negative.
+
+```{r}
+test_type_labels <- data.frame(
+  test_type = 1:8,
+  description = c(
+    "One-sided efficacy",
+    "Two-sided symmetric",
+    "Beta-spending futility, binding",
+    "Beta-spending futility, non-binding",
+    "Null-spending futility, binding",
+    "Null-spending futility, non-binding",
+    "Binding futility and harm",
+    "Non-binding futility and harm"
+  )
+)
+
+comparison |>
+  group_by(test_type) |>
+  summarize(max_abs_z_difference = max(abs_difference), .groups = "drop") |>
+  left_join(test_type_labels, by = "test_type") |>
+  select(test_type, description, max_abs_z_difference) |>
+  gt() |>
+  fmt_scientific(columns = max_abs_z_difference, decimals = 2)
+```
+
+The detailed Z-boundary comparison is shown below.
+
+```{r}
+comparison |>
+  left_join(test_type_labels, by = "test_type") |>
+  mutate(
+    test_type = paste0("test.type ", test_type, ": ", description),
+    bound = as.character(bound)
+  ) |>
+  select(-description) |>
+  gt(groupname_col = "test_type") |>
+  fmt_number(columns = c(gsSurv, gs_design_ahr), decimals = 6) |>
+  fmt_scientific(columns = c(difference, abs_difference), decimals = 2)
+```
+
+# Sample size and event counts
+
+Test types 7 and 8 add a harm bound to the corresponding beta-spending futility
+designs in test types 3 and 4.
+With the current specification, the harm bound is below the futility bound at
+each analysis, and adding the harm bound does not change the efficacy bound,
+futility bound, sample size, or event count.
+
+```{r}
+harm_effect_comparison <- lapply(
+  list(
+    c(futility_only = 3, with_harm = 7),
+    c(futility_only = 4, with_harm = 8)
+  ),
+  function(test_types) {
+    futility_only <- make_gs_surv(test_types[["futility_only"]])
+    with_harm <- make_gs_surv(test_types[["with_harm"]])
+
+    data.frame(
+      comparison = paste0(
+        "test.type ", test_types[["futility_only"]],
+        " vs test.type ", test_types[["with_harm"]]
+      ),
+      max_upper_z_difference = max(abs(with_harm$upper$bound - futility_only$upper$bound)),
+      max_futility_z_difference = max(abs(with_harm$lower$bound - futility_only$lower$bound)),
+      final_n_difference = tail(as.numeric(with_harm$eNC + with_harm$eNE), 1) -
+        tail(as.numeric(futility_only$eNC + futility_only$eNE), 1),
+      final_events_difference = tail(with_harm$n.I, 1) - tail(futility_only$n.I, 1),
+      largest_harm_z = max(with_harm$harm$bound)
+    )
+  }
+) |>
+  do.call(what = rbind)
+
+harm_effect_comparison |>
+  gt() |>
+  fmt_number(
+    columns = c(
+      max_upper_z_difference,
+      max_futility_z_difference,
+      final_n_difference,
+      final_events_difference,
+      largest_harm_z
+    ),
+    decimals = 6
+  )
+```
+
+Thus, the sample size and event count changes seen for test types 7 and 8
+relative to some other test types are inherited from their underlying
+beta-spending futility designs, not from the addition of the harm bound.
+
+The Z-boundaries match closely, but the displayed sample sizes and event counts
+from `gsSurv()` and `gs_design_ahr()` are not identical.
+The table below compares final analysis values from the two packages.
+
+```{r}
+sample_size_comparison <- lapply(1:8, function(test_type) {
+  gs_surv <- make_gs_surv(test_type)
+  gs_ahr <- make_gs_design_ahr(test_type)
+
+  data.frame(
+    test_type = test_type,
+    analysis = seq_along(info_frac),
+    n_gsSurv = as.numeric(gs_surv$eNC + gs_surv$eNE),
+    n_gs_design_ahr = gs_ahr$analysis$n,
+    events_gsSurv = as.numeric(gs_surv$n.I),
+    events_gs_design_ahr = gs_ahr$analysis$event,
+    time_gsSurv = as.numeric(gs_surv$T),
+    time_gs_design_ahr = gs_ahr$analysis$time
+  )
+}) |>
+  do.call(what = rbind) |>
+  mutate(
+    n_difference = n_gs_design_ahr - n_gsSurv,
+    events_difference = events_gs_design_ahr - events_gsSurv,
+    n_percent_difference = 100 * n_difference / n_gsSurv,
+    events_percent_difference = 100 * events_difference / events_gsSurv
+  )
+
+sample_size_comparison |>
+  filter(analysis == length(info_frac)) |>
+  left_join(test_type_labels, by = "test_type") |>
+  select(
+    test_type,
+    description,
+    n_gsSurv,
+    n_gs_design_ahr,
+    n_difference,
+    events_gsSurv,
+    events_gs_design_ahr,
+    events_difference,
+    events_percent_difference
+  ) |>
+  gt() |>
+  fmt_number(
+    columns = c(
+      n_gsSurv,
+      n_gs_design_ahr,
+      n_difference,
+      events_gsSurv,
+      events_gs_design_ahr,
+      events_difference
+    ),
+    decimals = 2
+  ) |>
+  fmt_number(columns = events_percent_difference, decimals = 3)
+```
+
+The differences are a reporting conversion issue rather than a boundary issue.
+For this proportional hazards example with 1:1 randomization, the Schoenfeld
+null-variance information rate is exactly events / 4.
+The AHR calculation also computes information under the alternative hypothesis.
+Because the experimental arm has fewer events when the hazard ratio is 0.7, the
+AHR information per event under the alternative is slightly smaller.
+
+```{r}
+information_rate <- gs_info_ahr(
+  enroll_rate = enroll_rate,
+  fail_rate = fail_rate,
+  ratio = ratio,
+  analysis_time = trial_duration
+)
+
+information_rate_comparison <- data.frame(
+  quantity = c(
+    "H1 AHR information per event",
+    "H0/Schoenfeld information per event",
+    "H0 divided by H1 information per event"
+  ),
+  value = c(
+    information_rate$info / information_rate$event,
+    information_rate$info0 / information_rate$event,
+    (information_rate$info0 / information_rate$event) /
+      (information_rate$info / information_rate$event)
+  )
+)
+
+information_rate_comparison |>
+  gt() |>
+  fmt_number(columns = value, decimals = 6)
+```
+
+Thus both packages agree on the required H0 information target for the
+Schoenfeld design.
+The current `gs_design_ahr()` output converts that H0 information target back to
+reported events and sample size using the AHR alternative-hypothesis information
+rate.
+This makes the displayed `gs_design_ahr()` event counts and sample sizes about
+0.71% larger than the corresponding `gsSurv()` values in this example.
+Making these reported counts align exactly is a display/conversion issue that
+can be addressed separately from the boundary comparison.
+
+# Interpretation
+
+For a single hazard ratio, `gs_design_ahr()` with `info_scale = "h0_info"`
+matches `gsSurv(method = "Schoenfeld")` boundaries to numerical integration
+tolerance.
+The harm-bound test types are obtained by using the usual beta-spending lower
+bound for futility and an additional null-spending lower-tail bound through the
+`harm`, `hpar`, and `test_harm` arguments.
+With `astar = 0.20`, the harm boundary represents an interim lower-tail evidence
+threshold that experimental treatment may be worse than control, while keeping
+the harm Z-boundaries below 0.