diff --git a/R/gs_update_ahr.R b/R/gs_update_ahr.R
index 023bc07f..79f9eaa1 100644
--- a/R/gs_update_ahr.R
+++ b/R/gs_update_ahr.R
@@ -118,6 +118,78 @@
 #'
 #' # Alpha is updated to 0.05
 #' gs_update_ahr(x = x, alpha = 0.05)
+#'
+#' # ------------------------------------------------- #
+#' # Two-sided asymmetric stratified design,
+#' # beta-spending with non-binding lower bound
+#' # ------------------------------------------------- #
+#' enroll_rate <- define_enroll_rate(stratum = c("A", "B"), duration = c(12, 12), rate = c(1, 1))
+#'
+#' # We assumme there are 2 strata, "A" and "B".
+#' # For each stratum, there are delayed effect for the first 3 months.
+#' # After the delayed effect, the HR is 0.8 for stratum A and 0.5 for stratum B.
+#' fail_rate <- define_fail_rate(stratum = c("A", "A", "B", "B"),
+#'                               duration = c(3, Inf, 3, Inf),
+#'                               fail_rate = log(2) / c(9, 9, 9, 15),
+#'                               hr = c(1, 0.8, 1, 0.5),
+#'                               dropout_rate = rep(0.001, 4))
+#'
+#' # The original design assumes there are 2 IAs and 1 FA cutting by calendar time.
+#' # The efficacy testing is conducted at IA2 and FA.
+#' # The futility testing is conducted at IA1.
+#' x <- gs_design_ahr(enroll_rate = enroll_rate,
+#'                   fail_rate = fail_rate,
+#'                   alpha = 0.0125,
+#'                   beta = 0.1,
+#'                   analysis = c(20, 28, 36),
+#'                   upper = "gs_spending_bound",
+#'                   upar = list(sf = "sfLDOF", total_spend = 0.0125),
+#'                   lower = "gs_spending_bound",
+#'                   lpar = list(sf = "sfHSD", total_spend = 0.1, param = -8),
+#'                   test_upper = c(FALSE, TRUE, TRUE),
+#'                   test_lower = c(TRUE, FALSE, FALSE)) |> to_integer()
+#'
+#' # At time of analysis
+#' # For IA1,
+#' # - There are 70 events observed during the delayed effect period for stratum A.
+#' # - There are 150 events observed after the delayed effect period for stratum A.
+#' # - There are 75 events observed during the delayed effect period for stratum B.
+#' # - There are 90 events observed after the delayed effect period for stratum B.
+#' # For IA2,
+#' # - There are 75 events observed during the delayed effect period for stratum A.
+#' # - There are 210 events observed after the delayed effect period for stratum A.
+#' # - There are 76 events observed during the delayed effect period for stratum B.
+#' # - There are 136 events observed after the delayed effect period for stratum B.
+#' # For FA,
+#' # - There are 77 events observed during the delayed effect period for stratum A.
+#' # - There are 245 events observed after the delayed effect period for stratum A.
+#' # - There are 77 events observed during the delayed effect period for stratum B.
+#' # - There are 170 events observed after the delayed effect period for stratum B.
+#' event_tbl <- data.frame(analysis = c(1, 1, 1, 1,
+#'                                     2, 2, 2, 2,
+#'                                     3, 3, 3, 3),
+#'                        stratum = c("A", "A", "B", "B", # IA1
+#'                                    "A", "A", "B", "B", # IA2
+#'                                    "A", "A", "B", "B"),# FA
+#'                                  # event per interval per stratum at IA1
+#'                        event = c(70, 150, 75, 90,
+#'                                  # event per interval per stratum at IA2
+#'                                  75, 210, 76, 136,
+#'                                  # event per interval per stratum at FA
+#'                                  77, 245, 77, 170))
+#' observed_event <- (event_tbl |> dplyr::group_by(analysis) |> dplyr::summarize(x = sum(event)))$x
+#'
+#' ustime <- pmin(x$analysis$event,
+#'                observed_event) / x$analysis$event[3]
+#' ustime[3] <- 1
+#' lstime <- ustime
+#'
+#' xu <- gs_update_ahr(x = x,
+#'                     alpha = 0.015,
+#'                     ustime = ustime,
+#'                     lstime = lstime,
+#'                     event_tbl = event_tbl
+#'                     )
 gs_update_ahr <- function(
     x = NULL,
     alpha = NULL,
diff --git a/man/gs_update_ahr.Rd b/man/gs_update_ahr.Rd
index 8a78bc15..3f3d5d42 100644
--- a/man/gs_update_ahr.Rd
+++ b/man/gs_update_ahr.Rd
@@ -119,4 +119,76 @@ gs_update_ahr(
 
 # Alpha is updated to 0.05
 gs_update_ahr(x = x, alpha = 0.05)
+
+# ------------------------------------------------- #
+# Two-sided asymmetric stratified design,
+# beta-spending with non-binding lower bound
+# ------------------------------------------------- #
+enroll_rate <- define_enroll_rate(stratum = c("A", "B"), duration = c(12, 12), rate = c(1, 1))
+
+# We assumme there are 2 strata, "A" and "B".
+# For each stratum, there are delayed effect for the first 3 months.
+# After the delayed effect, the HR is 0.8 for stratum A and 0.5 for stratum B.
+fail_rate <- define_fail_rate(stratum = c("A", "A", "B", "B"),
+                              duration = c(3, Inf, 3, Inf),
+                              fail_rate = log(2) / c(9, 9, 9, 15),
+                              hr = c(1, 0.8, 1, 0.5),
+                              dropout_rate = rep(0.001, 4))
+
+# The original design assumes there are 2 IAs and 1 FA cutting by calendar time.
+# The efficacy testing is conducted at IA2 and FA.
+# The futility testing is conducted at IA1.
+x <- gs_design_ahr(enroll_rate = enroll_rate,
+                  fail_rate = fail_rate,
+                  alpha = 0.0125,
+                  beta = 0.1,
+                  analysis = c(20, 28, 36),
+                  upper = "gs_spending_bound",
+                  upar = list(sf = "sfLDOF", total_spend = 0.0125),
+                  lower = "gs_spending_bound",
+                  lpar = list(sf = "sfHSD", total_spend = 0.1, param = -8),
+                  test_upper = c(FALSE, TRUE, TRUE),
+                  test_lower = c(TRUE, FALSE, FALSE)) |> to_integer()
+
+# At time of analysis
+# For IA1,
+# - There are 70 events observed during the delayed effect period for stratum A.
+# - There are 150 events observed after the delayed effect period for stratum A.
+# - There are 75 events observed during the delayed effect period for stratum B.
+# - There are 90 events observed after the delayed effect period for stratum B.
+# For IA2,
+# - There are 75 events observed during the delayed effect period for stratum A.
+# - There are 210 events observed after the delayed effect period for stratum A.
+# - There are 76 events observed during the delayed effect period for stratum B.
+# - There are 136 events observed after the delayed effect period for stratum B.
+# For FA,
+# - There are 77 events observed during the delayed effect period for stratum A.
+# - There are 245 events observed after the delayed effect period for stratum A.
+# - There are 77 events observed during the delayed effect period for stratum B.
+# - There are 170 events observed after the delayed effect period for stratum B.
+event_tbl <- data.frame(analysis = c(1, 1, 1, 1,
+                                    2, 2, 2, 2,
+                                    3, 3, 3, 3),
+                       stratum = c("A", "A", "B", "B", # IA1
+                                   "A", "A", "B", "B", # IA2
+                                   "A", "A", "B", "B"),# FA
+                                 # event per interval per stratum at IA1
+                       event = c(70, 150, 75, 90,
+                                 # event per interval per stratum at IA2
+                                 75, 210, 76, 136,
+                                 # event per interval per stratum at FA
+                                 77, 245, 77, 170))
+observed_event <- (event_tbl |> dplyr::group_by(analysis) |> dplyr::summarize(x = sum(event)))$x
+
+ustime <- pmin(x$analysis$event,
+               observed_event) / x$analysis$event[3]
+ustime[3] <- 1
+lstime <- ustime
+
+xu <- gs_update_ahr(x = x,
+                    alpha = 0.015,
+                    ustime = ustime,
+                    lstime = lstime,
+                    event_tbl = event_tbl
+                    )
 }
diff --git a/tests/testthat/_snaps/independent_as_gt.md b/tests/testthat/_snaps/independent_as_gt.md
index 830eda20..fc230e8c 100644
--- a/tests/testthat/_snaps/independent_as_gt.md
+++ b/tests/testthat/_snaps/independent_as_gt.md
@@ -14,7 +14,7 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} Power computed with average hazard ratio method.\\
+    \textsuperscript{\textit{1}}Power computed with average hazard ratio method.\\
     \end{minipage}
     \end{table}
 
@@ -34,7 +34,7 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} Custom footnote.\\
+    \textsuperscript{\textit{1}}Custom footnote.\\
     \end{minipage}
     \end{table}
 
@@ -54,7 +54,7 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} Power for Fleming-Harrington test  FH(0, 0) (logrank) using method of Yung and Liu.\\
+    \textsuperscript{\textit{1}}Power for Fleming-Harrington test  FH(0, 0) (logrank) using method of Yung and Liu.\\
     \end{minipage}
     \end{table}
 
@@ -68,7 +68,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}HR at bound\textsuperscript{\textit{2}} & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -79,8 +79,8 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} Approximate hazard ratio to cross bound.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}Approximate hazard ratio to cross bound.\\
     \end{minipage}
     \end{table}
 
@@ -94,7 +94,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}HR at bound\textsuperscript{\textit{2}} & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -116,8 +116,8 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} Approximate hazard ratio to cross bound.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}Approximate hazard ratio to cross bound.\\
     \end{minipage}
     \end{table}
 
@@ -131,7 +131,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}wHR at bound\textsuperscript{\textit{2}} & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -142,9 +142,9 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} Approximate hazard ratio to cross bound.\\
-    \textsuperscript{\textit{3}} wAHR is the weighted AHR.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}Approximate hazard ratio to cross bound.\\
+    \textsuperscript{\textit{3}}wAHR is the weighted AHR.\\
     \end{minipage}
     \end{table}
 
@@ -158,7 +158,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability\textsuperscript{\textit{2}}} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}}\textsuperscript{\textit{2}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p & \textasciitilde{}wHR at bound\textsuperscript{\textit{3}} & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -180,10 +180,10 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} this table is generated by gs\_power\_wlr.\\
-    \textsuperscript{\textit{2}} the crossing probability.\\
-    \textsuperscript{\textit{3}} approximate weighted hazard ratio to cross bound.\\
-    \textsuperscript{\textit{4}} wAHR is the weighted AHR.\\
+    \textsuperscript{\textit{1}}this table is generated by gs\_power\_wlr.\\
+    \textsuperscript{\textit{2}}the crossing probability.\\
+    \textsuperscript{\textit{3}}approximate weighted hazard ratio to cross bound.\\
+    \textsuperscript{\textit{4}}wAHR is the weighted AHR.\\
     \end{minipage}
     \end{table}
 
@@ -197,7 +197,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrr}
     \toprule
-     &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){4-5}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -219,8 +219,8 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} EF is event fraction. AHR is under regular weighted log rank test.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}EF is event fraction. AHR is under regular weighted log rank test.\\
     \end{minipage}
     \end{table}
 
@@ -234,7 +234,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}Risk difference at bound & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -245,7 +245,7 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
     \end{minipage}
     \end{table}
 
@@ -259,7 +259,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}Risk difference at bound & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -274,13 +274,13 @@
     \midrule\addlinespace[2.5pt]
     \multicolumn{6}{l}{Analysis: 3 N: 60 Risk difference: 0.05 Information fraction: 1} \\[2.5pt] 
     \midrule\addlinespace[2.5pt]
-    Efficacy & 1.99 & 0.0231 & 0.1951 & 0.0705 & 0.0238\textsuperscript{\textit{2}} \\ 
+    Efficacy & 1.99 & 0.0231 & 0.1951 & 0.0705 & \textsuperscript{\textit{2}}0.0238 \\ 
     \bottomrule
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} Cumulative alpha for final analysis (0.0238) is less than the full alpha (0.025) when the futility bound is non-binding. The smaller value subtracts the probability of crossing a futility bound before crossing an efficacy bound at a later analysis (0.025 - 0.0012 = 0.0238) under the null hypothesis.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}Cumulative alpha for final analysis (0.0238) is less than the full alpha (0.025) when the futility bound is non-binding. The smaller value subtracts the probability of crossing a futility bound before crossing an efficacy bound at a later analysis (0.025 - 0.0012 = 0.0238) under the null hypothesis.\\
     \end{minipage}
     \end{table}
 
@@ -294,7 +294,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}wHR at bound\textsuperscript{\textit{2}} & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -316,9 +316,9 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} Approximate hazard ratio to cross bound.\\
-    \textsuperscript{\textit{3}} wAHR is the weighted AHR.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}Approximate hazard ratio to cross bound.\\
+    \textsuperscript{\textit{3}}wAHR is the weighted AHR.\\
     \end{minipage}
     \end{table}
 
@@ -332,7 +332,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative probability to cross boundaries} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative probability to cross boundaries}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}wHR at bound\textsuperscript{\textit{2}} & under H1 & under H0 \\ 
     \midrule\addlinespace[2.5pt]
@@ -354,9 +354,9 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} Approximate hazard ratio to cross bound.\\
-    \textsuperscript{\textit{3}} wAHR is the weighted AHR.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}Approximate hazard ratio to cross bound.\\
+    \textsuperscript{\textit{3}}wAHR is the weighted AHR.\\
     \end{minipage}
     \end{table}
 
@@ -370,7 +370,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability\textsuperscript{\textit{2}}} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}}\textsuperscript{\textit{2}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p & \textasciitilde{}wHR at bound\textsuperscript{\textit{3}} & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -392,10 +392,10 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} this table is generated by gs\_power\_wlr.\\
-    \textsuperscript{\textit{2}} the crossing probability.\\
-    \textsuperscript{\textit{3}} approximate weighted hazard ratio to cross bound.\\
-    \textsuperscript{\textit{4}} wAHR is the weighted AHR.\\
+    \textsuperscript{\textit{1}}this table is generated by gs\_power\_wlr.\\
+    \textsuperscript{\textit{2}}the crossing probability.\\
+    \textsuperscript{\textit{3}}approximate weighted hazard ratio to cross bound.\\
+    \textsuperscript{\textit{4}}wAHR is the weighted AHR.\\
     \end{minipage}
     \end{table}
 
@@ -409,7 +409,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrrr}
     \toprule
-     &  &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){5-6}
     Bound & Z & Nominal p\textsuperscript{\textit{1}} & \textasciitilde{}wHR at bound\textsuperscript{\textit{2}} & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -428,9 +428,9 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} Approximate hazard ratio to cross bound.\\
-    \textsuperscript{\textit{3}} wAHR is the weighted AHR.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}Approximate hazard ratio to cross bound.\\
+    \textsuperscript{\textit{3}}wAHR is the weighted AHR.\\
     \end{minipage}
     \end{table}
 
@@ -444,7 +444,7 @@
     \fontsize{12.0pt}{14.0pt}\selectfont
     \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}lrrrr}
     \toprule
-     &  &  & \multicolumn{2}{c}{Cumulative boundary crossing probability} \\ 
+     &  &  & \multicolumn{2}{c}{{Cumulative boundary crossing probability}} \\ 
     \cmidrule(lr){4-5}
     Bound & Nominal p\textsuperscript{\textit{1}} & Z & Alternate hypothesis & Null hypothesis \\ 
     \midrule\addlinespace[2.5pt]
@@ -466,8 +466,8 @@
     \end{tabular*}
     \begin{minipage}{\linewidth}
     \vspace{.05em}
-    \textsuperscript{\textit{1}} One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
-    \textsuperscript{\textit{2}} wAHR is the weighted AHR.\\
+    \textsuperscript{\textit{1}}One-sided p-value for experimental vs control treatment. Value < 0.5 favors experimental, > 0.5 favors control.\\
+    \textsuperscript{\textit{2}}wAHR is the weighted AHR.\\
     \end{minipage}
     \end{table}