diff --git a/.Rbuildignore b/.Rbuildignore
index ae7e12a9..4deffee4 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -10,3 +10,4 @@
 ^vignettes/articles$
 ^\.gitlab-ci\.yml$
 LICENSE
+.lintr
diff --git a/.lintr b/.lintr
new file mode 100644
index 00000000..c514f393
--- /dev/null
+++ b/.lintr
@@ -0,0 +1,6 @@
+linters: linters_with_defaults(
+  line_length_linter = line_length_linter(120),
+  cyclocomp_linter = NULL,
+  object_usage_linter = NULL,
+  object_length_linter = NULL
+  )
diff --git a/DESCRIPTION b/DESCRIPTION
index d020fde5..044e9802 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,15 +22,18 @@ BugReports: https://github.com/insightsengineering/crane/issues
 Depends: 
     gtsummary (>= 2.5.0),
     R (>= 4.2)
-Imports: 
+Imports:
     broom (>= 1.0.8),
     cards (>= 0.7.0),
     cardx (>= 0.3.0),
+    cowplot (>= 1.2.0),
     cli (>= 3.6.4),
     dplyr (>= 1.1.4),
     flextable (>= 0.9.7),
+    ggplot2 (>= 4.0.0),
     glue (>= 1.8.0),
     gt (>= 0.11.1),
+    labeling,
     lifecycle,
     rlang (>= 1.1.5),
     survival (>= 3.6-4),
diff --git a/NAMESPACE b/NAMESPACE
index 0ff9110d..63f10b27 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -12,13 +12,19 @@ export(add_blank_rows)
 export(add_difference_row)
 export(add_hierarchical_count_row)
 export(add_overall)
+export(annotate_coxph)
+export(annotate_riskdf)
+export(annotate_surv_med)
 export(filter_hierarchical)
+export(get_cox_pairwise_df)
+export(gg_km)
 export(label_roche_number)
 export(label_roche_percent)
 export(label_roche_pvalue)
 export(label_roche_ratio)
 export(modify_header_rm_md)
 export(modify_zero_recode)
+export(process_survfit)
 export(remove_duplicate_keys)
 export(sort_hierarchical)
 export(style_roche_number)
@@ -36,8 +42,13 @@ export(tbl_shift)
 export(tbl_survfit_quantiles)
 export(tbl_survfit_times)
 export(theme_gtsummary_roche)
+import(ggplot2)
 import(glue)
 import(rlang)
+importFrom(broom,tidy)
+importFrom(cowplot,draw_plot)
+importFrom(cowplot,ggdraw)
+importFrom(cowplot,plot_grid)
 importFrom(dplyr,"%>%")
 importFrom(dplyr,across)
 importFrom(dplyr,all_of)
@@ -54,3 +65,10 @@ importFrom(gtsummary,add_difference_row)
 importFrom(gtsummary,add_overall)
 importFrom(gtsummary,filter_hierarchical)
 importFrom(gtsummary,sort_hierarchical)
+importFrom(labeling,extended)
+importFrom(stats,pchisq)
+importFrom(survival,Surv)
+importFrom(survival,coxph)
+importFrom(survival,survdiff)
+importFrom(tidyr,pivot_wider)
+importFrom(utils,tail)
diff --git a/NEWS.md b/NEWS.md
index ab50d9b8..b622cd6d 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -8,6 +8,8 @@
 
 * Added functions `style_roche_number()` and `label_roche_number()` to replace their respective gtsummary versions, with additional `inf` argument for customization of `Inf`/`-Inf`/`NaN` values.
 
+* Added `gg_km()` function for creating Kaplan-Meier plots.
+
 * Added `list("assign_summary_type-arg:cat_threshold" = 0L)` to `theme_gtsummary_roche()`. Numeric variables with few levels will no longer default to summary type `'categorical'` in `gtsummary::tbl_summary()` and `tbl_roche_summary()`. (#79)
 
 ### Other Updates
diff --git a/R/annotate_gg_km.R b/R/annotate_gg_km.R
new file mode 100644
index 00000000..532bc90b
--- /dev/null
+++ b/R/annotate_gg_km.R
@@ -0,0 +1,284 @@
+#' Annotate Kaplan-Meier Plot
+#'
+#' @description
+#' These functions provide capabilities to annotate Kaplan-Meier plots ([gg_km()]) with additional summary tables,
+#' including median survival times, numbers at risk, and cox proportional hazards results.
+#' The annotations are added using the `cowplot` package for flexible placement.
+#'
+#' @param gg_plt (`ggplot2` or `cowplot`)\cr
+#'   The primary plot object (either a `ggplot2` or `cowplot` object) of the Kaplan-Meier plot.
+#' @param ... Additional arguments passed to the control list for the annotation box.
+#'   These arguments override the default values.
+#'   Accepted arguments include:
+#'   \itemize{
+#'     \item \code{x} (\code{numeric}): X-coordinate for the box anchor position (0 to 1). Default is
+#'       \code{0.8} (\code{0.29} for `annotate_coxph`).
+#'     \item \code{y} (\code{numeric}): Y-coordinate for the box anchor position (0 to 1). Default is
+#'       \code{0.85} (\code{0.51} for `annotate_coxph`).
+#'     \item \code{w} (\code{numeric}): Width of the annotation box (0 to 1). Default is
+#'       \code{0.32} (\code{0.4} for `annotate_coxph`).
+#'     \item \code{h} (\code{numeric}): Height of the annotation box (0 to 1). Default
+#'       is \code{0.16} (\code{0.125} for `annotate_coxph`).
+#'     \item \code{fill} (\code{logical}): Whether the annotation box should have a background fill. Default is
+#'       \code{TRUE}.
+#'     \item \code{font_size} (\code{numeric}): Base font size for the text inside the annotation box. Default
+#'       is \code{10}.
+#'   }
+#'
+#' @seealso [gg_km()], [process_survfit()], and [get_cox_pairwise_df()] for related functionalities.
+#'
+#' @examples
+#' # Preparing the Kaplan-Meier Plot
+#' use_lung <- survival::lung
+#' use_lung$arm <- factor(sample(c("A", "B", "C"), nrow(use_lung), replace = TRUE))
+#' use_lung$status <- use_lung$status - 1 # Convert status to 0/1
+#' use_lung <- na.omit(use_lung)
+#'
+#' formula <- survival::Surv(time, status) ~ arm
+#' fit_kmg01 <- survival::survfit(formula, use_lung)
+#' surv_plot_data <- process_survfit(fit_kmg01)
+#'
+#' plt_kmg01 <- gg_km(surv_plot_data)
+#'
+#' @name annotate_gg_km
+NULL
+
+
+#' @describeIn annotate_gg_km The function `annotate_riskdf` adds a "Numbers at Risk" table below a
+#'   Kaplan-Meier plot ([gg_km()]) using `cowplot::plot_grid`.
+#'
+#' @param fit_km (`survfit`)\cr
+#'   A fitted Kaplan-Meier object of class `survfit` (from the `survival` package). This object contains the necessary
+#'   survival data used to calculate and generate the content displayed in the annotation table.
+#' @param title (`string`)\cr
+#'   A single logical value indicating whether to include a above the table. Defaults to
+#'   `""Patients at Risk:""`. If `NULL`, no title is added.
+#' @param rel_height_plot (`numeric`)\cr
+#'   A single numeric value defining the **relative height** of the main Kaplan-Meier plot area compared
+#'   to the 'at-risk' table. This value should be between 0 and 1, where a value closer to 1 gives the main plot
+#'   more vertical space. Defaults to `0.75`.
+#' @param xlab (`character`)\cr
+#'   A single character string for the **x-axis label** on the 'at-risk' table. This typically represents
+#'   time (e.g., "Time (Days)").
+#' @return The function `annotate_riskdf` returns a `cowplot` object combining the KM plot and the 'Numbers at Risk'
+#'   table.
+#'
+#' @examples
+#' # Annotate Plot with Numbers at Risk Table
+#' annotate_riskdf(plt_kmg01, fit_kmg01)
+#'
+#' # Change order of y-axis (arm)
+#' use_lung2 <- use_lung
+#' use_lung2$arm <- factor(use_lung2$arm, levels = c("C", "B", "A"))
+#' fit_kmg01 <- survival::survfit(formula, use_lung2)
+#' annotate_riskdf(plt_kmg01, fit_kmg01) # rerun gg_km to change legend order
+#'
+#' @export
+annotate_riskdf <- function(gg_plt, fit_km, title = "Patients at Risk:",
+                            rel_height_plot = 0.75, xlab = "Days",
+                            ...) {
+  check_class(gg_plt, c("gg", "ggplot", "cowplot"))
+  check_class(fit_km, "survfit")
+  check_string(title, allow_empty = TRUE)
+  check_scalar(rel_height_plot)
+  check_numeric(rel_height_plot)
+  if (rel_height_plot <= 0 || rel_height_plot >= 1) {
+    cli::cli_abort(
+      "{.arg rel_height_plot} must be a single {.cls numeric} value between 0 and 1 (exclusive).",
+      call = get_cli_abort_call()
+    )
+  }
+  check_string(xlab)
+  default_eargs <- list(
+    font_size = 10
+  )
+  eargs <- list(...)
+  eargs <- utils::modifyList(default_eargs, eargs)
+  font_size <- eargs[["font_size"]]
+  check_numeric(font_size)
+
+  data <- broom::tidy(fit_km)
+  xticks <- h_xticks(data = data)
+  annot_tbl <- summary(fit_km, times = xticks, extend = TRUE)
+
+  # Placeholder for strata_levels, should be retrieved from fit_km or passed as argument
+  strata_levels <- if (is.null(fit_km$strata)) "All" else levels(fit_km$strata)
+
+  annot_tbl <- if (is.null(fit_km$strata)) {
+    data.frame(
+      n.risk = annot_tbl$n.risk,
+      time = annot_tbl$time,
+      strata = strata_levels
+    )
+  } else {
+    strata_lst <- strsplit(sub("=", "equals", levels(annot_tbl$strata)), "equals")
+    levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]
+    data.frame(
+      n.risk = annot_tbl$n.risk,
+      time = annot_tbl$time,
+      strata = annot_tbl$strata
+    )
+  }
+
+  at_risk_tbl <- as.data.frame(
+    tidyr::pivot_wider(annot_tbl, names_from = "time", values_from = "n.risk")[, -1]
+  )
+  at_risk_tbl[is.na(at_risk_tbl)] <- 0
+  rownames(at_risk_tbl) <- levels(annot_tbl$strata)
+
+  gg_at_risk <- df2gg(
+    at_risk_tbl,
+    font_size = eargs$font_size, col_labels = FALSE, hline = FALSE,
+    colwidths = rep(1, ncol(at_risk_tbl)),
+    add_proper_xaxis = TRUE
+  ) +
+    ggplot2::labs(title = if (!is.null(title)) title else NULL, x = xlab) +
+    ggplot2::theme_bw(base_size = eargs$font_size) +
+    ggplot2::theme(
+      plot.title = ggplot2::element_text(size = eargs$font_size, vjust = 3, face = "bold"),
+      panel.border = ggplot2::element_blank(),
+      panel.grid = ggplot2::element_blank(),
+      axis.title.y = ggplot2::element_blank(),
+      axis.ticks.y = ggplot2::element_blank(),
+      axis.text.y = ggplot2::element_text(size = eargs$font_size, face = "italic", hjust = 1),
+      axis.text.x = ggplot2::element_text(size = eargs$font_size),
+      axis.line.x = ggplot2::element_line()
+    ) +
+    ggplot2::coord_cartesian(clip = "off", ylim = c(0.5, nrow(at_risk_tbl)))
+
+  gg_plt <- cowplot::plot_grid(
+    gg_plt, gg_at_risk,
+    align = "vh", axis = "b", ncol = 1,
+    rel_heights = c(rel_height_plot, 1 - rel_height_plot)
+  )
+  gg_plt
+}
+
+#' @describeIn annotate_gg_km The `annotate_surv_med` function adds a median survival time summary table as an
+#'   annotation box.
+#'
+#' @return The function `annotate_surv_med` returns a `cowplot` object with the median survival table annotation
+#'   added, ready for final display or saving.
+#'
+#' @examples
+#' # Annotate Kaplan-Meier Plot with Median Survival Table
+#' annotate_surv_med(plt_kmg01, fit_kmg01)
+#'
+#' @export
+annotate_surv_med <- function(gg_plt, fit_km, ...) {
+  set_cli_abort_call()
+  default_eargs <- list(
+    x = 0.8,
+    y = 0.85,
+    w = 0.32,
+    h = 0.16,
+    font_size = 10,
+    fill = TRUE
+  )
+  eargs <- list(...)
+  eargs <- utils::modifyList(default_eargs, eargs)
+
+  # Checks
+  check_class(fit_km, "survfit")
+  check_class(gg_plt, c("gg", "ggplot", "cowplot"))
+
+  # Check position/size (x, y, w, h, font_size) - Must be single non-missing numeric
+  for (arg_name in c("x", "y", "w", "h", "font_size")) {
+    check_numeric(eargs[[arg_name]])
+  }
+  check_logical(eargs[["fill"]])
+
+  # Determine strata_levels for h_tbl_median_surv, assuming it's available in the calling environment or logic should
+  # be updated. For now, keeping as is, but this typically requires strata_levels or inferring it from fit_km
+  strata_levels <- if (is.null(fit_km$strata)) "All" else levels(fit_km$strata) # Placeholder for strata_levels
+
+  surv_med_tbl <- h_tbl_median_surv(fit_km = fit_km, strata_levels = strata_levels)
+  bg_fill <- if (isTRUE(eargs[["fill"]])) "#00000020" else eargs[["fill"]]
+
+  gg_surv_med <- df2gg(surv_med_tbl, font_size = eargs[["font_size"]], colwidths = c(1, 1, 2), bg_fill = bg_fill) +
+    ggplot2::theme(
+      axis.text.y = ggplot2::element_text(size = eargs[["font_size"]], face = "italic", hjust = 1),
+      plot.margin = ggplot2::margin(0, 2, 0, 5)
+    ) +
+    ggplot2::coord_cartesian(clip = "off", ylim = c(0.5, nrow(surv_med_tbl) + 1.5))
+  gg_surv_med <- suppressMessages(
+    gg_surv_med +
+      ggplot2::scale_x_continuous(expand = c(0.025, 0)) +
+      ggplot2::scale_y_continuous(labels = rev(rownames(surv_med_tbl)), breaks = seq_len(nrow(surv_med_tbl)))
+  )
+
+  gg_plt <- cowplot::ggdraw(gg_plt) +
+    cowplot::draw_plot(
+      gg_surv_med, eargs[["x"]], eargs[["y"]],
+      width = eargs[["w"]], height = eargs[["h"]],
+      vjust = 0.5, hjust = 0.5
+    )
+  gg_plt
+}
+
+#' @describeIn annotate_gg_km The function `annotate_coxph()` adds a Cox Proportional Hazards summary table created by
+#' the function [get_cox_pairwise_df()] as an annotation box.
+#'
+#' @param coxph_tbl (`data.frame`)\cr
+#'   A data frame containing the pre-calculated Cox-PH results, typically from a function like `get_cox_pairwise_df`.
+#'   This data is used to generate the annotation table content.
+#' @param ... Additional arguments passed to the control list for the annotation box.
+#'   These arguments override the default values.
+#'   Accepted arguments include:
+#'   \itemize{
+#'     \item \code{x} (`numeric`): X-coordinate for the box anchor position (0 to 1). Default is \code{0.29}.
+#'     \item \code{y} (`numeric`): Y-coordinate for the box anchor position (0 to 1). Default is \code{0.51}.
+#'     \item \code{w} (`numeric`): Width of the annotation box (0 to 1). Default is \code{0.4}.
+#'     \item \code{h} (`numeric`): Height of the annotation box (0 to 1). Default is \code{0.125}.
+#'   }
+#'
+#' @return The function `annotate_coxph` returns a `cowplot` object with the Cox-PH table annotation added.
+#'
+#' @examples
+#' # Annotate Kaplan-Meier Plot with Cox-PH Table
+#' coxph_tbl <- get_cox_pairwise_df(formula, data = use_lung, arm = "arm", ref_group = "A")
+#' annotate_coxph(plt_kmg01, coxph_tbl)
+#'
+#' @export
+annotate_coxph <- function(gg_plt, coxph_tbl, ...) {
+  set_cli_abort_call()
+  default_eargs <- list(
+    x = 0.29,
+    y = 0.51,
+    w = 0.4,
+    h = 0.125,
+    fill = TRUE,
+    font_size = 10
+  )
+  eargs <- list(...)
+  eargs <- utils::modifyList(default_eargs, eargs)
+
+  # Check position/size (x, y, w, h, font_size) - Must be single non-missing numeric
+  for (arg_name in c("x", "y", "w", "h", "font_size")) {
+    check_numeric(eargs[[arg_name]])
+  }
+  check_logical(eargs[["fill"]])
+
+  bg_fill <- if (isTRUE(eargs[["fill"]])) "#00000020" else eargs[["fill"]]
+
+  gg_coxph <- df2gg(coxph_tbl, font_size = eargs$font_size, colwidths = c(1.1, 1, 3), bg_fill = bg_fill) +
+    ggplot2::theme(
+      axis.text.y = ggplot2::element_text(size = eargs$font_size, face = "italic", hjust = 1),
+      plot.margin = ggplot2::margin(0, 2, 0, 5)
+    ) +
+    ggplot2::coord_cartesian(clip = "off", ylim = c(0.5, nrow(coxph_tbl) + 1.5))
+  gg_coxph <- suppressMessages(
+    gg_coxph +
+      ggplot2::scale_x_continuous(expand = c(0.025, 0)) +
+      ggplot2::scale_y_continuous(labels = rev(rownames(coxph_tbl)), breaks = seq_len(nrow(coxph_tbl)))
+  )
+
+  gg_plt <- cowplot::ggdraw(gg_plt) +
+    cowplot::draw_plot(
+      gg_coxph, eargs[["x"]], eargs[["y"]],
+      width = eargs[["w"]], height = eargs[["h"]],
+      vjust = 0.5, hjust = 0.5
+    )
+
+  gg_plt
+}
diff --git a/R/crane-package.R b/R/crane-package.R
index 9d4c0225..cea5e9fa 100644
--- a/R/crane-package.R
+++ b/R/crane-package.R
@@ -1,8 +1,16 @@
 #' @keywords internal
 #' @import rlang
+#' @import ggplot2
 #' @import glue glue
+#' @importFrom broom tidy
+#' @importFrom cowplot plot_grid ggdraw draw_plot
 #' @importFrom dplyr across starts_with ends_with contains matches num_range
 #'                   all_of any_of everything last_col where
+#' @importFrom labeling extended
+#' @importFrom survival coxph Surv survdiff
+#' @importFrom stats pchisq
+#' @importFrom tidyr pivot_wider
+#' @importFrom utils tail
 "_PACKAGE"
 
 ## usethis namespace: start
diff --git a/R/get_cox_pairwise_df.R b/R/get_cox_pairwise_df.R
new file mode 100644
index 00000000..61518813
--- /dev/null
+++ b/R/get_cox_pairwise_df.R
@@ -0,0 +1,123 @@
+#' Generate Table of Pairwise Cox-PH and Log-Rank Results
+#'
+#' @description
+#' This function performs pairwise comparisons of treatment arms using the **Cox Proportional Hazards model** and
+#' calculates the corresponding **log-rank p-value**. Each comparison tests a non-reference group against a specified
+#' reference group.
+#'
+#' @param model_formula (`formula`)\cr
+#'   A `formula` object specifying the survival model, typically in the form `Surv(time, status) ~ arm + covariates`.
+#' @param data (`data.frame`)\cr
+#'   A `data.frame` containing the survival data, including time, status, and the arm variable.
+#' @param arm (`character`)\cr
+#'   A single character string specifying the name of the column in `data` that contains the grouping/treatment
+#'   **arm variable**. This column **must be a factor** for correct stratification and comparison.
+#' @param ref_group (`character` or `NULL`)\cr
+#'   A single character string specifying the level of the `arm` variable to be used as the **reference group** for
+#'   all pairwise comparisons. If `NULL` (the default), the **first unique level** of the `arm` column is automatically
+#'   selected as the reference group.
+#'
+#' @return A `data.frame` with the results of the pairwise comparisons. The columns include:
+#' \itemize{
+#'   \item `arm`: (rownames of the `data.frame`) The comparison arm (group) being tested against the reference group.
+#'   \item `hr`: The Hazard Ratio (HR) for the comparison arm vs. the reference arm, formatted to two decimal places.
+#'   \item `ci`: The 95% confidence interval for the HR, presented as a string in the format "(lower, upper)", with
+#'     values formatted to two decimal places.
+#'   \item `pval`: The log-rank p-value for the comparison.
+#' }
+#'
+#' @details The function iterates through each unique arm (excluding the reference group). For each iteration, it
+#'   filters the data to include only the current comparison arm and the reference arm, and then:
+#'   \itemize{
+#'     \item Fits a Cox model using `survival::coxph`.
+#'     \item Performs a log-rank test using `survival::survdiff`.
+#'   }
+#'   The Hazard Ratio and its 95% confidence interval are extracted from the Cox model summary, and the p-value is
+#'   extracted from the log-rank test.
+#'
+#' @seealso `annotate_gg_km()`, `gg_km()`, and the `survival` package functions `survival::coxph` and
+#'   `survival::survdiff`.
+#'
+#' @examples
+#' # Example data setup (assuming 'time' is event time, 'status' is event indicator (1=event),
+#' # and 'arm' is the treatment group)
+#' library(dplyr) # For better data handling
+#'
+#' # Prepare data in a modern dplyr-friendly way
+#' surv_data <- survival::lung |>
+#'   mutate(
+#'     arm = factor(sample(c("A", "B", "C"), n(), replace = TRUE)),
+#'     status = status - 1 # Convert status to 0/1
+#'   ) |>
+#'   filter(if_all(everything(), ~ !is.na(.)))
+#'
+#' formula <- survival::Surv(time, status) ~ arm
+#' results_tbl <- get_cox_pairwise_df(
+#'   model_formula = formula,
+#'   data = surv_data,
+#'   arm = "arm",
+#'   ref_group = "A"
+#' )
+#' print(results_tbl)
+#'
+#' @export
+get_cox_pairwise_df <- function(model_formula, data, arm, ref_group = NULL) {
+  set_cli_abort_call()
+  # Input checks
+  if (!rlang::is_formula(model_formula)) {
+    cli::cli_abort(
+      "{.arg model_formula} must be a {.cls formula}.",
+      call = get_cli_abort_call()
+    )
+  }
+  if (!is.factor(data[[arm]])) {
+    cli::cli_abort(
+      "Column {.arg {data}[[\"{.var {arm}}\"]]} must be a {.cls factor}.",
+      call = get_cli_abort_call()
+    )
+  }
+
+  # Determine reference and comparison groups
+  ref_group <- if (!is.null(ref_group)) {
+    ref_group
+  } else {
+    levels(data[[arm]])[1]
+  }
+  comp_group <- setdiff(levels(data[[arm]]), ref_group)
+
+  ret <- c()
+  for (current_arm in comp_group) {
+    subset_arm <- c(ref_group, current_arm)
+    if (length(subset_arm) != 2) {
+      cli::cli_abort(
+        "{.arg subset_arm} must contain exactly 2 arms/groups (current length is {length(subset_arm)}).",
+        call = get_cli_abort_call()
+      )
+    }
+    comp_df <- data[as.character(data[[arm]]) %in% subset_arm, ]
+    suppressWarnings(
+      coxph_ans <- coxph(formula = model_formula, data = comp_df) |> summary()
+    )
+    orginal_survdiff <- survdiff(formula = model_formula, data = comp_df)
+    log_rank_pvalue <- 1 - stats::pchisq(orginal_survdiff$chisq, length(orginal_survdiff$n) - 1)
+    current_row <- data.frame(
+      hr = sprintf("%.2f", coxph_ans$conf.int[1, 1]),
+      ci = paste0(
+        "(",
+        sprintf("%.2f", coxph_ans$conf.int[1, 3]),
+        ", ",
+        sprintf("%.2f", coxph_ans$conf.int[1, 4]),
+        ")"
+      ),
+      pval = log_rank_pvalue
+    )
+    rownames(current_row) <- current_arm
+    ret <- rbind(ret, current_row)
+  }
+  names(ret) <- c(
+    "HR",
+    "95% CI",
+    "p-value (log-rank)"
+  )
+  ret
+}
diff --git a/R/gg_km.R b/R/gg_km.R
new file mode 100644
index 00000000..8e4f50e3
--- /dev/null
+++ b/R/gg_km.R
@@ -0,0 +1,301 @@
+#' Kaplan-Meier Plot
+#'
+#' @description
+#' This set of functions facilitates the creation of Kaplan-Meier survival plots using `ggplot2`. Use
+#' `process_survfit()` to prepare the survival data from a fitted `survfit` object, and then
+#' `gg_km()` to generate the Kaplan-Meier plot with various customization options. Additional functions
+#' like `annot_surv_med()`, `annot_cox_ph()`, and `annotate_riskdf()` allow for adding summary tables and
+#' annotations to the plot.
+#'
+#' @name gg_km
+NULL
+
+#' @describeIn gg_km takes a fitted [survfit] object and processes it into a data frame
+#'   suitable for plotting a Kaplan-Meier curve with `ggplot2`. Time zero is also added to the data.
+#'
+#' @param fit_km A fitted Kaplan-Meier object of class `survfit`.
+#' @param strata_levels (`string`)\cr
+#'   A single character string used as the strata level if the input `fit_km` object
+#'   has no strata (e.g., `"All"`).
+#' @param max_time (`numeric` or `NULL`)\cr
+#'   A single numeric value defining the **maximum time point** to include in the data,
+#'   or `NULL` for no time limit.
+#'
+#' @return The function `process_survfit` returns a data frame containing the survival
+#'   curve steps, confidence intervals, and censoring info.
+#'
+#' @details
+#' Data setup assumes `"time"` is event time, `"status"` is event indicator (`1` represents an event),
+#' while `"arm"` is the treatment group.
+#'
+#' @examples
+#' # Data preparation for KM plot
+#' use_lung <- survival::lung
+#' use_lung$arm <- factor(sample(c("A", "B", "C"), nrow(use_lung), replace = TRUE))
+#' use_lung$status <- use_lung$status - 1 # Convert status to 0/1
+#' use_lung <- na.omit(use_lung)
+#'
+#' # Fit Kaplan-Meier model
+#' formula <- survival::Surv(time, status) ~ arm
+#' fit_kmg01 <- survival::survfit(formula, use_lung)
+#'
+#' # Process survfit data for plotting
+#' surv_plot_data <- process_survfit(fit_kmg01)
+#' head(surv_plot_data)
+#'
+#' @export
+process_survfit <- function(fit_km,
+                            strata_levels = "All",
+                            max_time = NULL) {
+  set_cli_abort_call()
+
+  # Input checks
+  if (!inherits(fit_km, "survfit")) {
+    cli::cli_abort(
+      "The input {.arg fit_km} must be a fitted Kaplan-Meier object of class {.cls survfit}.",
+      call = get_cli_abort_call()
+    )
+  }
+  check_string(strata_levels)
+  check_numeric(max_time, allow_empty = TRUE)
+
+  y <- broom::tidy(fit_km)
+
+  # Handle strata factor levels
+  if (!is.null(fit_km$strata)) {
+    fit_km_var_level <- strsplit(sub("=", "equals", names(fit_km$strata)), "equals")
+    strata_levels <- vapply(fit_km_var_level, FUN = "[", FUN.VALUE = "a", i = 2)
+    strata_var_level <- strsplit(sub("=", "equals", y$strata), "equals")
+    y$strata <- factor(
+      vapply(strata_var_level, FUN = "[", FUN.VALUE = "a", i = 2),
+      levels = strata_levels
+    )
+  } else {
+    y$strata <- strata_levels
+  }
+
+  # Extend to time zero
+  y_by_strata <- split(y, y$strata)
+  y_by_strata_extended <- lapply(
+    y_by_strata,
+    FUN = function(tbl) {
+      first_row <- tbl[1L, ]
+      first_row$time <- 0
+      first_row$n.risk <- sum(first_row[, c("n.risk", "n.event", "n.censor")])
+      first_row$n.event <- first_row$n.censor <- 0
+      first_row$estimate <- first_row$conf.high <- first_row$conf.low <- 1
+      first_row$std.error <- 0
+      rbind(
+        first_row,
+        tbl
+      )
+    }
+  )
+  y <- do.call(rbind, y_by_strata_extended)
+
+  # Censoring points
+  y$censor <- ifelse(y$n.censor > 0, y$estimate, NA)
+
+  # Apply max_time filter if provided
+  if (!is.null(max_time)) {
+    y <- y[y$time <= max(max_time), ]
+  }
+
+  y
+}
+
+
+#' @describeIn gg_km creates a Kaplan-Meier survival curve, with
+#'   support for various customizations like censoring marks, Confidence  Intervals (CIs), and axis control.
+#'
+#' @param surv_plot_data (`data.frame`)\cr
+#'   A data frame containing the pre-processed survival data, ready for plotting.
+#'   This data should be equivalent to the output of `process_survfit`.
+#' @param lty (`numeric` or `NULL`)\cr
+#'   A numeric vector of **line types** (e.g., `1` for solid, `2` for dashed) for the survival curves, or `NULL` for
+#'   `ggplot2` defaults. The length should match the number of arms/groups.
+#' @param lwd (`numeric`)\cr
+#'   A single numeric value specifying the **line width** for the survival curves.
+#' @param censor_show (`logical`)\cr
+#'   A single logical value indicating whether to display **censoring marks** on the plot. Defaults to `TRUE`.
+#' @param size (`numeric`)\cr
+#'   A single numeric value specifying the **size** of the censoring marks.
+#' @param max_time (`numeric`)\cr
+#'   A single numeric value defining the **maximum time point** to display on the x-axis.
+#' @param xticks (`numeric` or `NULL`)\cr
+#'   A numeric vector of explicit **x-axis tick positions**, or a single numeric value representing the **interval**
+#'   between ticks, or `NULL` for automatic `ggplot2` scaling.
+#' @param yval (`character`)\cr
+#'   A single character string, either `"Survival"` or `"Failure"` to plot the corresponding probability.
+#' @param ylim (`numeric`)\cr
+#'   A **numeric vector of length 2** defining the lower and upper limits of the y-axis (e.g., `c(0, 1)`).
+#' @param font_size (`numeric`)\cr
+#'   A single numeric value specifying the **base font size** for the plot theme elements.
+#' @param legend_pos (`numeric` or `NULL`)\cr
+#'   A **numeric vector of length 2** defining the **legend position** as (x, y) coordinates relative to the plot
+#'   area (ranging from 0 to 1), or `NULL` for automatic placement.
+#'
+#' @return The function `gg_km` returns a `ggplot2` object of the KM plot.
+#'
+#' @examples
+#' # Example of making the KM plot
+#' plt_kmg01 <- gg_km(surv_plot_data)
+#'
+#' # Confidence Interval as Ribbon
+#' plt_kmg01 +
+#'   ggplot2::geom_ribbon(alpha = 0.3, lty = 0, na.rm = TRUE)
+#'
+#' # Adding Title and Footnotes
+#' plt_kmg01 +
+#'   ggplot2::labs(title = "title", caption = "footnotes")
+#'
+#' # Changing xlab and ylab
+#' plt_kmg01 +
+#'   ggplot2::xlab("Another Day") +
+#'   ggplot2::ylab("THE Survival Probability")
+#'
+#' @export
+gg_km <- function(surv_plot_data,
+                  lty = NULL,
+                  lwd = 0.5,
+                  censor_show = TRUE,
+                  size = 2,
+                  max_time = NULL,
+                  xticks = NULL,
+                  yval = c("Survival", "Failure"),
+                  ylim = NULL,
+                  font_size = 10,
+                  legend_pos = NULL) {
+  set_cli_abort_call()
+
+  # Input checks
+  check_data_frame(surv_plot_data)
+  needed_cols <- c("time", "estimate", "conf.low", "conf.high", "strata", "n.censor", "censor")
+  if (!all(needed_cols %in% colnames(surv_plot_data))) {
+    cli::cli_abort(
+      "The input {.arg surv_plot_data} must contain the following columns: ",
+      "{.code time}, {.code estimate}, {.code conf.low}, {.code conf.high}, ",
+      "{.code strata}, {.code n.censor}, and {.code censor}.",
+      call = get_cli_abort_call()
+    )
+  }
+  if (nrow(surv_plot_data) < 1) {
+    cli::cli_abort(
+      "The input {.arg surv_plot_data} must contain at least one row of data.",
+      call = get_cli_abort_call()
+    )
+  }
+  check_numeric(ylim, allow_empty = TRUE)
+  check_scalar_logical(censor_show)
+
+  data <- surv_plot_data
+  strata_levels <- levels(data$strata)
+
+  yval <- match.arg(yval)
+  if (yval == "Failure") {
+    data[c("estimate", "conf.low", "conf.high", "censor")] <- list(
+      1 - data$estimate, 1 - data$conf.low, 1 - data$conf.high, 1 - data$censor
+    )
+  }
+
+  if (is.null(ylim)) {
+    if (!is.null(max_time)) {
+      y_lwr <- min(data[data$time < max_time, ][["estimate"]])
+      y_upr <- max(data[data$time < max_time, ][["estimate"]])
+    } else {
+      y_lwr <- min(data[["estimate"]])
+      y_upr <- max(data[["estimate"]])
+    }
+    ylim <- c(y_lwr, y_upr)
+  }
+
+  xticks <- h_xticks(data = data, xticks = xticks, max_time = max_time)
+
+  gg_plt <- ggplot2::ggplot(
+    data = data,
+    mapping = ggplot2::aes(
+      x = .data[["time"]], y = .data[["estimate"]], ymin = .data[["conf.low"]],
+      ymax = .data[["conf.high"]], color = .data[["strata"]], fill = .data[["strata"]]
+    )
+  ) +
+    ggplot2::theme_bw(base_size = font_size) +
+    ggplot2::scale_y_continuous(limits = ylim, expand = c(0.025, 0)) +
+    ggplot2::labs(x = "Days", y = paste(yval, "Probability")) +
+    ggplot2::theme(
+      axis.text = ggplot2::element_text(size = font_size),
+      axis.title = ggplot2::element_text(size = font_size),
+      legend.title = ggplot2::element_blank(),
+      legend.text = ggplot2::element_text(size = font_size),
+      legend.box.background = ggplot2::element_rect(fill = "white", linewidth = 0.5),
+      legend.background = ggplot2::element_blank(),
+      legend.position = "inside",
+      legend.spacing.y = ggplot2::unit(-0.02, "npc"),
+      panel.grid.major = ggplot2::element_blank(),
+      panel.grid.minor = ggplot2::element_blank()
+    )
+
+  if (!is.null(max_time) && !is.null(xticks)) {
+    gg_plt <- gg_plt + ggplot2::scale_x_continuous(
+      breaks = xticks, limits = c(min(0, xticks), max(c(xticks, max_time))), expand = c(0.025, 0)
+    )
+  } else if (!is.null(xticks)) {
+    if (max(data$time) <= max(xticks)) {
+      gg_plt <- gg_plt + ggplot2::scale_x_continuous(
+        breaks = xticks, limits = c(min(0, min(xticks)), max(xticks)), expand = c(0.025, 0)
+      )
+    } else {
+      gg_plt <- gg_plt + ggplot2::scale_x_continuous(breaks = xticks, expand = c(0.025, 0))
+    }
+  } else if (!is.null(max_time)) {
+    gg_plt <- gg_plt + ggplot2::scale_x_continuous(limits = c(0, max_time), expand = c(0.025, 0))
+  }
+
+  if (!is.null(legend_pos)) {
+    gg_plt <- gg_plt + ggplot2::theme(legend.position.inside = legend_pos)
+  } else {
+    max_time2 <- sort(
+      data$time,
+      partial = nrow(data) - length(strata_levels) - 1
+    )[nrow(data) - length(strata_levels) - 1]
+
+    y_rng <- ylim[2] - ylim[1]
+
+    if (yval == "Survival" && all(data$estimate[data$time == max_time2] > ylim[1] + 0.09 * y_rng) &&
+      all(data$estimate[data$time == max_time2] < ylim[1] + 0.5 * y_rng)) { # nolint
+      gg_plt <- gg_plt +
+        ggplot2::theme(
+          legend.position.inside = c(1, 0.5),
+          legend.justification = c(1.1, 0.6)
+        )
+    } else {
+      gg_plt <- gg_plt +
+        ggplot2::theme(
+          legend.position.inside = c(1, 0),
+          legend.justification = c(1.1, -0.4)
+        )
+    }
+  }
+
+  gg_plt <- if (is.null(lty)) {
+    gg_plt + ggplot2::geom_step(linewidth = lwd, na.rm = TRUE)
+  } else if (length(lty) == 1) {
+    gg_plt + ggplot2::geom_step(linewidth = lwd, lty = lty, na.rm = TRUE)
+  } else {
+    gg_plt +
+      ggplot2::geom_step(ggplot2::aes(lty = .data[["strata"]]), linewidth = lwd, na.rm = TRUE) +
+      ggplot2::scale_linetype_manual(values = lty)
+  }
+
+  if (isTRUE(censor_show)) {
+    gg_plt <- gg_plt + ggplot2::geom_point(
+      data = data[data$n.censor != 0, ],
+      ggplot2::aes(x = .data[["time"]], y = .data[["censor"]], shape = "Censored"),
+      size = size,
+      na.rm = TRUE
+    ) +
+      ggplot2::scale_shape_manual(values = 3) +
+      ggplot2::guides(fill = ggplot2::guide_legend(override.aes = list(shape = NA)))
+  }
+
+  gg_plt
+}
diff --git a/R/gg_km_utils.R b/R/gg_km_utils.R
new file mode 100644
index 00000000..d6eb431f
--- /dev/null
+++ b/R/gg_km_utils.R
@@ -0,0 +1,184 @@
+#' Convert Data Frame to ggplot2 Table Graphic
+#'
+#' @description Creates a `ggplot2` object that renders a data frame as a table graphic.
+#'
+#' @param df The data frame to render.
+#' @param colwidths Numeric vector of relative column widths. If \code{NULL}, determined by max character length.
+#' @param font_size Numeric base font size.
+#' @param col_labels Logical, whether to display column labels (header).
+#' @param col_lab_fontface Character string for the font face of column labels (e.g., "bold").
+#' @param hline Logical, whether to draw a horizontal line below the column labels.
+#' @param bg_fill Optional color string for the plot background.
+#' @param add_proper_xaxis Logical, whether to add a proper x-axis with column values.
+#'
+#' @return A \code{ggplot2} object representing the table.
+#'
+#' @keywords internal
+df2gg <- function(df, colwidths = NULL, font_size = 10, col_labels = TRUE,
+                  col_lab_fontface = "bold", hline = TRUE, bg_fill = NULL, add_proper_xaxis = FALSE) {
+  # Convert all values to character, replacing NAs with "NA"
+  df <- as.data.frame(apply(df, 1:2, function(x) {
+    if (is.na(x)) {
+      "NA"
+    } else {
+      as.character(x)
+    }
+  }))
+
+  # Add column labels as first row if specified
+  if (col_labels) {
+    df <- as.matrix(df)
+    df <- rbind(colnames(df), df)
+  }
+
+  # Create ggplot2 object with x-axis specified in df
+  if (add_proper_xaxis) {
+    # Determine column widths if not provided
+    if (is.null(colwidths)) {
+      tot_width <- max(colnames(df) |> as.numeric(), na.rm = TRUE)
+      colwidths <- rep(floor(tot_width / ncol(df)), ncol(df))
+    } else {
+      tot_width <- sum(colwidths)
+    }
+
+    df_long <- df |>
+      as.data.frame() |>
+      # 1. Ensure the row names ('A', 'B', 'C') are a column named 'row_name'
+      dplyr::mutate(row_name = row.names(df)) |>
+      # 2. Pivot the remaining columns (starting from '0' to the end) longer
+      tidyr::pivot_longer(
+        cols = -.data$row_name, # Select all columns EXCEPT 'row_name'
+        names_to = "col_name", # Name the new column containing the old column headers
+        values_to = "value" # Name the new column containing the data values
+      ) |>
+      dplyr::arrange(.data$row_name, .data$col_name) |>
+      dplyr::mutate(
+        col_name = as.numeric(.data$col_name),
+        row_name = factor(.data$row_name, levels = row.names(df))
+      )
+    res <- ggplot2::ggplot(data = df_long) +
+      ggplot2::theme_void() +
+      ggplot2::annotate("text",
+        x = df_long$col_name, y = rev(df_long$row_name), # why rev?
+        label = df_long$value, size = font_size / .pt
+      )
+
+    # Create ggplot2 object with a specific x-axis based on column widths
+  } else {
+    # Determine column widths if not provided
+    if (is.null(colwidths)) {
+      colwidths <- apply(df, 2, function(x) max(nchar(x), na.rm = TRUE))
+    }
+    tot_width <- sum(colwidths)
+
+    res <- ggplot2::ggplot(data = df) +
+      ggplot2::theme_void() +
+      ggplot2::scale_x_continuous(limits = c(0, tot_width)) +
+      ggplot2::scale_y_continuous(limits = c(1, nrow(df)))
+
+
+    for (i in seq_len(ncol(df))) {
+      line_pos <- c(
+        if (i == 1) {
+          0
+        } else {
+          sum(colwidths[1:(i - 1)])
+        },
+        sum(colwidths[1:i])
+      )
+      res <- res + ggplot2::annotate("text",
+        x = mean(line_pos), y = rev(seq_len(nrow(df))),
+        label = df[, i], size = font_size / .pt, fontface = if (col_labels) {
+          c(col_lab_fontface, rep("plain", nrow(df) - 1))
+        } else {
+          rep("plain", nrow(df))
+        }
+      )
+    }
+  }
+
+  # Add horizontal line if specified
+  if (hline) {
+    res <- res + ggplot2::annotate(
+      "segment",
+      x = 0 + 0.2 * colwidths[2], xend = tot_width - 0.1 * tail(colwidths, 1),
+      y = nrow(df) - 0.5, yend = nrow(df) - 0.5
+    )
+  }
+
+  # Set background fill if specified
+  if (!is.null(bg_fill)) {
+    res <- res + ggplot2::theme(plot.background = ggplot2::element_rect(fill = bg_fill))
+  }
+
+  res
+}
+
+#' Calculate X-axis Ticks
+#'
+#' @description Determines the positions for x-axis ticks based on the data and user input.
+#'
+#' @param data A data frame containing a `"time""` column.
+#' @param xticks A numeric vector of specific tick positions, a single number for the interval, or
+#'   `NULL` for auto-calculation.
+#' @param max_time Optional numeric value specifying the maximum time to consider for tick range.
+#'
+#' @return A numeric vector of x-axis tick positions.
+#'
+#' @keywords internal
+h_xticks <- function(data, xticks = NULL, max_time = NULL) {
+  if (is.null(xticks)) {
+    if (is.null(max_time)) {
+      labeling::extended(range(data$time)[1], range(data$time)[2], m = 5)
+    } else {
+      labeling::extended(range(data$time)[1], max(range(data$time)[2], max_time), m = 5)
+    }
+  } else if (is.numeric(xticks) && length(xticks) == 1 && !is.na(xticks)) {
+    if (is.null(max_time)) {
+      seq(0, max(data$time), xticks)
+    } else {
+      seq(0, max(data$time, max_time), xticks)
+    }
+  } else if (is.numeric(xticks)) {
+    xticks
+  } else {
+    stop(
+      paste(
+        "xticks should be either `NULL`",
+        "or a single number (interval between x ticks)",
+        "or a numeric vector (position of ticks on the x axis)"
+      )
+    )
+  }
+}
+
+#' @title Median Survival Summary Table
+#'
+#' @description Extracts and formats the median survival time and its confidence interval
+#' from a fitted Kaplan-Meier object.
+#'
+#' @inheritParams gg_km
+#'
+#' @return A data frame with columns "N", "Median", and the confidence interval label.
+#'
+#' @keywords internal
+h_tbl_median_surv <- function(fit_km, strata_levels = "All") {
+  y <- if (is.null(fit_km$strata)) {
+    as.data.frame(t(summary(fit_km)$table), row.names = strata_levels)
+  } else {
+    tbl <- summary(fit_km)$table
+    rownames_lst <- strsplit(sub("=", "equals", rownames(tbl)), "equals")
+    rownames(tbl) <- matrix(unlist(rownames_lst), ncol = 2, byrow = TRUE)[, 2]
+    as.data.frame(tbl)
+  }
+  conf.int <- summary(fit_km)$conf.int # nolint
+  y$records <- round(y$records)
+  y$median <- signif(y$median, 4)
+  y$`CI` <- paste0(
+    "(", signif(y[[paste0(conf.int, "LCL")]], 4), ", ", signif(y[[paste0(conf.int, "UCL")]], 4), ")"
+  )
+  stats::setNames(
+    y[c("records", "median", "CI")],
+    c("N", "Median", paste0(conf.int * 100, "% CI"))
+  )
+}
diff --git a/R/tbl_null_report.R b/R/tbl_null_report.R
index d7d47cd0..a86c0ba3 100644
--- a/R/tbl_null_report.R
+++ b/R/tbl_null_report.R
@@ -10,8 +10,7 @@
 #'
 #' @export
 #' @rdname tbl_null_report
-tbl_null_report <- function(
-    label = "No observations met the reporting criteria for this output.") {
+tbl_null_report <- function(label = "No observations met the reporting criteria for this output.") {
   set_cli_abort_call()
 
   # Check input label ----------------------------------------------------------
diff --git a/_pkgdown.yml b/_pkgdown.yml
index c850f898..3015c9d7 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -41,3 +41,9 @@ reference:
       - modify_zero_recode
       - add_blank_rows
       - label_roche
+  - title: "Kaplan-Meier Plot"
+    contents:
+      - process_survfit
+      - gg_km
+      - annotate_gg_km
+      - get_cox_pairwise_df
diff --git a/inst/WORDLIST b/inst/WORDLIST
index b95a577a..123b296b 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -8,16 +8,20 @@ Kaplan
 Ns
 ORCID
 RStudio
+Recode
 Rua
 SOCs
 Tidyverse
 cardx
+customizations
 de
 flextable
 funder
+ggplot
 gtsummary
 pharma
 pre
+recodes
 rlang's
 survfit
 tbl
diff --git a/man/annotate_gg_km.Rd b/man/annotate_gg_km.Rd
new file mode 100644
index 00000000..f25d6668
--- /dev/null
+++ b/man/annotate_gg_km.Rd
@@ -0,0 +1,116 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/annotate_gg_km.R
+\name{annotate_gg_km}
+\alias{annotate_gg_km}
+\alias{annotate_riskdf}
+\alias{annotate_surv_med}
+\alias{annotate_coxph}
+\title{Annotate Kaplan-Meier Plot}
+\usage{
+annotate_riskdf(
+  gg_plt,
+  fit_km,
+  title = "Patients at Risk:",
+  rel_height_plot = 0.75,
+  xlab = "Days",
+  ...
+)
+
+annotate_surv_med(gg_plt, fit_km, ...)
+
+annotate_coxph(gg_plt, coxph_tbl, ...)
+}
+\arguments{
+\item{gg_plt}{(\code{ggplot2} or \code{cowplot})\cr
+The primary plot object (either a \code{ggplot2} or \code{cowplot} object) of the Kaplan-Meier plot.}
+
+\item{fit_km}{(\code{survfit})\cr
+A fitted Kaplan-Meier object of class \code{survfit} (from the \code{survival} package). This object contains the necessary
+survival data used to calculate and generate the content displayed in the annotation table.}
+
+\item{title}{(\code{string})\cr
+A single logical value indicating whether to include a above the table. Defaults to
+\verb{""Patients at Risk:""}. If \code{NULL}, no title is added.}
+
+\item{rel_height_plot}{(\code{numeric})\cr
+A single numeric value defining the \strong{relative height} of the main Kaplan-Meier plot area compared
+to the 'at-risk' table. This value should be between 0 and 1, where a value closer to 1 gives the main plot
+more vertical space. Defaults to \code{0.75}.}
+
+\item{xlab}{(\code{character})\cr
+A single character string for the \strong{x-axis label} on the 'at-risk' table. This typically represents
+time (e.g., "Time (Days)").}
+
+\item{...}{Additional arguments passed to the control list for the annotation box.
+These arguments override the default values.
+Accepted arguments include:
+\itemize{
+\item \code{x} (\code{numeric}): X-coordinate for the box anchor position (0 to 1). Default is \code{0.29}.
+\item \code{y} (\code{numeric}): Y-coordinate for the box anchor position (0 to 1). Default is \code{0.51}.
+\item \code{w} (\code{numeric}): Width of the annotation box (0 to 1). Default is \code{0.4}.
+\item \code{h} (\code{numeric}): Height of the annotation box (0 to 1). Default is \code{0.125}.
+}}
+
+\item{coxph_tbl}{(\code{data.frame})\cr
+A data frame containing the pre-calculated Cox-PH results, typically from a function like \code{get_cox_pairwise_df}.
+This data is used to generate the annotation table content.}
+}
+\value{
+The function \code{annotate_riskdf} returns a \code{cowplot} object combining the KM plot and the 'Numbers at Risk'
+table.
+
+The function \code{annotate_surv_med} returns a \code{cowplot} object with the median survival table annotation
+added, ready for final display or saving.
+
+The function \code{annotate_coxph} returns a \code{cowplot} object with the Cox-PH table annotation added.
+}
+\description{
+These functions provide capabilities to annotate Kaplan-Meier plots (\code{\link[=gg_km]{gg_km()}}) with additional summary tables,
+including median survival times, numbers at risk, and cox proportional hazards results.
+The annotations are added using the \code{cowplot} package for flexible placement.
+}
+\section{Functions}{
+\itemize{
+\item \code{annotate_riskdf()}: The function \code{annotate_riskdf} adds a "Numbers at Risk" table below a
+Kaplan-Meier plot (\code{\link[=gg_km]{gg_km()}}) using \code{cowplot::plot_grid}.
+
+\item \code{annotate_surv_med()}: The \code{annotate_surv_med} function adds a median survival time summary table as an
+annotation box.
+
+\item \code{annotate_coxph()}: The function \code{annotate_coxph()} adds a Cox Proportional Hazards summary table created by
+the function \code{\link[=get_cox_pairwise_df]{get_cox_pairwise_df()}} as an annotation box.
+
+}}
+\examples{
+# Preparing the Kaplan-Meier Plot
+use_lung <- survival::lung
+use_lung$arm <- factor(sample(c("A", "B", "C"), nrow(use_lung), replace = TRUE))
+use_lung$status <- use_lung$status - 1 # Convert status to 0/1
+use_lung <- na.omit(use_lung)
+
+formula <- survival::Surv(time, status) ~ arm
+fit_kmg01 <- survival::survfit(formula, use_lung)
+surv_plot_data <- process_survfit(fit_kmg01)
+
+plt_kmg01 <- gg_km(surv_plot_data)
+
+# Annotate Plot with Numbers at Risk Table
+annotate_riskdf(plt_kmg01, fit_kmg01)
+
+# Change order of y-axis (arm)
+use_lung2 <- use_lung
+use_lung2$arm <- factor(use_lung2$arm, levels = c("C", "B", "A"))
+fit_kmg01 <- survival::survfit(formula, use_lung2)
+annotate_riskdf(plt_kmg01, fit_kmg01) # rerun gg_km to change legend order
+
+# Annotate Kaplan-Meier Plot with Median Survival Table
+annotate_surv_med(plt_kmg01, fit_kmg01)
+
+# Annotate Kaplan-Meier Plot with Cox-PH Table
+coxph_tbl <- get_cox_pairwise_df(formula, data = use_lung, arm = "arm", ref_group = "A")
+annotate_coxph(plt_kmg01, coxph_tbl)
+
+}
+\seealso{
+\code{\link[=gg_km]{gg_km()}}, \code{\link[=process_survfit]{process_survfit()}}, and \code{\link[=get_cox_pairwise_df]{get_cox_pairwise_df()}} for related functionalities.
+}
diff --git a/man/df2gg.Rd b/man/df2gg.Rd
new file mode 100644
index 00000000..ace83168
--- /dev/null
+++ b/man/df2gg.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gg_km_utils.R
+\name{df2gg}
+\alias{df2gg}
+\title{Convert Data Frame to ggplot2 Table Graphic}
+\usage{
+df2gg(
+  df,
+  colwidths = NULL,
+  font_size = 10,
+  col_labels = TRUE,
+  col_lab_fontface = "bold",
+  hline = TRUE,
+  bg_fill = NULL,
+  add_proper_xaxis = FALSE
+)
+}
+\arguments{
+\item{df}{The data frame to render.}
+
+\item{colwidths}{Numeric vector of relative column widths. If \code{NULL}, determined by max character length.}
+
+\item{font_size}{Numeric base font size.}
+
+\item{col_labels}{Logical, whether to display column labels (header).}
+
+\item{col_lab_fontface}{Character string for the font face of column labels (e.g., "bold").}
+
+\item{hline}{Logical, whether to draw a horizontal line below the column labels.}
+
+\item{bg_fill}{Optional color string for the plot background.}
+
+\item{add_proper_xaxis}{Logical, whether to add a proper x-axis with column values.}
+}
+\value{
+A \code{ggplot2} object representing the table.
+}
+\description{
+Creates a \code{ggplot2} object that renders a data frame as a table graphic.
+}
+\keyword{internal}
diff --git a/man/get_cox_pairwise_df.Rd b/man/get_cox_pairwise_df.Rd
new file mode 100644
index 00000000..beaad285
--- /dev/null
+++ b/man/get_cox_pairwise_df.Rd
@@ -0,0 +1,76 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/get_cox_pairwise_df.R
+\name{get_cox_pairwise_df}
+\alias{get_cox_pairwise_df}
+\title{Generate Table of Pairwise Cox-PH and Log-Rank Results}
+\usage{
+get_cox_pairwise_df(model_formula, data, arm, ref_group = NULL)
+}
+\arguments{
+\item{model_formula}{(\code{formula})\cr
+A \code{formula} object specifying the survival model, typically in the form \code{Surv(time, status) ~ arm + covariates}.}
+
+\item{data}{(\code{data.frame})\cr
+A \code{data.frame} containing the survival data, including time, status, and the arm variable.}
+
+\item{arm}{(\code{character})\cr
+A single character string specifying the name of the column in \code{data} that contains the grouping/treatment
+\strong{arm variable}. This column \strong{must be a factor} for correct stratification and comparison.}
+
+\item{ref_group}{(\code{character} or \code{NULL})\cr
+A single character string specifying the level of the \code{arm} variable to be used as the \strong{reference group} for
+all pairwise comparisons. If \code{NULL} (the default), the \strong{first unique level} of the \code{arm} column is automatically
+selected as the reference group.}
+}
+\value{
+A \code{data.frame} with the results of the pairwise comparisons. The columns include:
+\itemize{
+\item \code{arm}: (rownames of the \code{data.frame}) The comparison arm (group) being tested against the reference group.
+\item \code{hr}: The Hazard Ratio (HR) for the comparison arm vs. the reference arm, formatted to two decimal places.
+\item \code{ci}: The 95\% confidence interval for the HR, presented as a string in the format "(lower, upper)", with
+values formatted to two decimal places.
+\item \code{pval}: The log-rank p-value for the comparison.
+}
+}
+\description{
+This function performs pairwise comparisons of treatment arms using the \strong{Cox Proportional Hazards model} and
+calculates the corresponding \strong{log-rank p-value}. Each comparison tests a non-reference group against a specified
+reference group.
+}
+\details{
+The function iterates through each unique arm (excluding the reference group). For each iteration, it
+filters the data to include only the current comparison arm and the reference arm, and then:
+\itemize{
+\item Fits a Cox model using \code{survival::coxph}.
+\item Performs a log-rank test using \code{survival::survdiff}.
+}
+The Hazard Ratio and its 95\% confidence interval are extracted from the Cox model summary, and the p-value is
+extracted from the log-rank test.
+}
+\examples{
+# Example data setup (assuming 'time' is event time, 'status' is event indicator (1=event),
+# and 'arm' is the treatment group)
+library(dplyr) # For better data handling
+
+# Prepare data in a modern dplyr-friendly way
+surv_data <- survival::lung |>
+  mutate(
+    arm = factor(sample(c("A", "B", "C"), n(), replace = TRUE)),
+    status = status - 1 # Convert status to 0/1
+  ) |>
+  filter(if_all(everything(), ~ !is.na(.)))
+
+formula <- survival::Surv(time, status) ~ arm
+results_tbl <- get_cox_pairwise_df(
+  model_formula = formula,
+  data = surv_data,
+  arm = "arm",
+  ref_group = "A"
+)
+print(results_tbl)
+
+}
+\seealso{
+\code{annotate_gg_km()}, \code{gg_km()}, and the \code{survival} package functions \code{survival::coxph} and
+\code{survival::survdiff}.
+}
diff --git a/man/gg_km.Rd b/man/gg_km.Rd
new file mode 100644
index 00000000..1583f690
--- /dev/null
+++ b/man/gg_km.Rd
@@ -0,0 +1,125 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gg_km.R
+\name{gg_km}
+\alias{gg_km}
+\alias{process_survfit}
+\title{Kaplan-Meier Plot}
+\usage{
+process_survfit(fit_km, strata_levels = "All", max_time = NULL)
+
+gg_km(
+  surv_plot_data,
+  lty = NULL,
+  lwd = 0.5,
+  censor_show = TRUE,
+  size = 2,
+  max_time = NULL,
+  xticks = NULL,
+  yval = c("Survival", "Failure"),
+  ylim = NULL,
+  font_size = 10,
+  legend_pos = NULL
+)
+}
+\arguments{
+\item{fit_km}{A fitted Kaplan-Meier object of class \code{survfit}.}
+
+\item{strata_levels}{(\code{string})\cr
+A single character string used as the strata level if the input \code{fit_km} object
+has no strata (e.g., \code{"All"}).}
+
+\item{max_time}{(\code{numeric})\cr
+A single numeric value defining the \strong{maximum time point} to display on the x-axis.}
+
+\item{surv_plot_data}{(\code{data.frame})\cr
+A data frame containing the pre-processed survival data, ready for plotting.
+This data should be equivalent to the output of \code{process_survfit}.}
+
+\item{lty}{(\code{numeric} or \code{NULL})\cr
+A numeric vector of \strong{line types} (e.g., \code{1} for solid, \code{2} for dashed) for the survival curves, or \code{NULL} for
+\code{ggplot2} defaults. The length should match the number of arms/groups.}
+
+\item{lwd}{(\code{numeric})\cr
+A single numeric value specifying the \strong{line width} for the survival curves.}
+
+\item{censor_show}{(\code{logical})\cr
+A single logical value indicating whether to display \strong{censoring marks} on the plot. Defaults to \code{TRUE}.}
+
+\item{size}{(\code{numeric})\cr
+A single numeric value specifying the \strong{size} of the censoring marks.}
+
+\item{xticks}{(\code{numeric} or \code{NULL})\cr
+A numeric vector of explicit \strong{x-axis tick positions}, or a single numeric value representing the \strong{interval}
+between ticks, or \code{NULL} for automatic \code{ggplot2} scaling.}
+
+\item{yval}{(\code{character})\cr
+A single character string, either \code{"Survival"} or \code{"Failure"} to plot the corresponding probability.}
+
+\item{ylim}{(\code{numeric})\cr
+A \strong{numeric vector of length 2} defining the lower and upper limits of the y-axis (e.g., \code{c(0, 1)}).}
+
+\item{font_size}{(\code{numeric})\cr
+A single numeric value specifying the \strong{base font size} for the plot theme elements.}
+
+\item{legend_pos}{(\code{numeric} or \code{NULL})\cr
+A \strong{numeric vector of length 2} defining the \strong{legend position} as (x, y) coordinates relative to the plot
+area (ranging from 0 to 1), or \code{NULL} for automatic placement.}
+}
+\value{
+The function \code{process_survfit} returns a data frame containing the survival
+curve steps, confidence intervals, and censoring info.
+
+The function \code{gg_km} returns a \code{ggplot2} object of the KM plot.
+}
+\description{
+This set of functions facilitates the creation of Kaplan-Meier survival plots using \code{ggplot2}. Use
+\code{process_survfit()} to prepare the survival data from a fitted \code{survfit} object, and then
+\code{gg_km()} to generate the Kaplan-Meier plot with various customization options. Additional functions
+like \code{annot_surv_med()}, \code{annot_cox_ph()}, and \code{annotate_riskdf()} allow for adding summary tables and
+annotations to the plot.
+}
+\details{
+Data setup assumes \code{"time"} is event time, \code{"status"} is event indicator (\code{1} represents an event),
+while \code{"arm"} is the treatment group.
+}
+\section{Functions}{
+\itemize{
+\item \code{process_survfit()}: takes a fitted \link[survival:survfit]{survival::survfit} object and processes it into a data frame
+suitable for plotting a Kaplan-Meier curve with \code{ggplot2}. Time zero is also added to the data.
+
+\item \code{gg_km()}: creates a Kaplan-Meier survival curve, with
+support for various customizations like censoring marks, Confidence  Intervals (CIs), and axis control.
+
+}}
+\examples{
+# Data preparation for KM plot
+use_lung <- survival::lung
+use_lung$arm <- factor(sample(c("A", "B", "C"), nrow(use_lung), replace = TRUE))
+use_lung$status <- use_lung$status - 1 # Convert status to 0/1
+use_lung <- na.omit(use_lung)
+
+# Fit Kaplan-Meier model
+formula <- survival::Surv(time, status) ~ arm
+fit_kmg01 <- survival::survfit(formula, use_lung)
+
+# Process survfit data for plotting
+surv_plot_data <- process_survfit(fit_kmg01)
+head(surv_plot_data)
+
+# Example of making the KM plot
+plt_kmg01 <- gg_km(surv_plot_data)
+
+# Confidence Interval as Ribbon
+plt_kmg01 +
+  ggplot2::geom_ribbon(alpha = 0.3, lty = 0, na.rm = TRUE)
+
+# Adding Title and Footnotes
+plt_kmg01 +
+  ggplot2::labs(title = "title", caption = "footnotes")
+
+# Changing xlab and ylab
+plt_kmg01 +
+  ggplot2::xlab("Another Day") +
+  ggplot2::ylab("THE Survival Probability")
+
+}
diff --git a/man/h_tbl_median_surv.Rd b/man/h_tbl_median_surv.Rd
new file mode 100644
index 00000000..17812e95
--- /dev/null
+++ b/man/h_tbl_median_surv.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gg_km_utils.R
+\name{h_tbl_median_surv}
+\alias{h_tbl_median_surv}
+\title{Median Survival Summary Table}
+\usage{
+h_tbl_median_surv(fit_km, strata_levels = "All")
+}
+\arguments{
+\item{fit_km}{A fitted Kaplan-Meier object of class \code{survfit}.}
+
+\item{strata_levels}{(\code{string})\cr
+A single character string used as the strata level if the input \code{fit_km} object
+has no strata (e.g., \code{"All"}).}
+}
+\value{
+A data frame with columns "N", "Median", and the confidence interval label.
+}
+\description{
+Extracts and formats the median survival time and its confidence interval
+from a fitted Kaplan-Meier object.
+}
+\keyword{internal}
diff --git a/man/h_xticks.Rd b/man/h_xticks.Rd
new file mode 100644
index 00000000..e61f4a50
--- /dev/null
+++ b/man/h_xticks.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gg_km_utils.R
+\name{h_xticks}
+\alias{h_xticks}
+\title{Calculate X-axis Ticks}
+\usage{
+h_xticks(data, xticks = NULL, max_time = NULL)
+}
+\arguments{
+\item{data}{A data frame containing a \verb{"time""} column.}
+
+\item{xticks}{A numeric vector of specific tick positions, a single number for the interval, or
+\code{NULL} for auto-calculation.}
+
+\item{max_time}{Optional numeric value specifying the maximum time to consider for tick range.}
+}
+\value{
+A numeric vector of x-axis tick positions.
+}
+\description{
+Determines the positions for x-axis ticks based on the data and user input.
+}
+\keyword{internal}
diff --git a/tests/testthat/test-gg_km.R b/tests/testthat/test-gg_km.R
new file mode 100644
index 00000000..9b8e392e
--- /dev/null
+++ b/tests/testthat/test-gg_km.R
@@ -0,0 +1,62 @@
+test_that("gg_km() works with default inputs", {
+  anl <- cards::ADTTE |>
+    dplyr::mutate(is_event = CNSR == 0)
+  by <- "TRTP"
+  anl[[by]] <- factor(anl[[by]], levels = c(
+    "Placebo",
+    "Xanomeline Low Dose",
+    "Xanomeline High Dose"
+  ))
+  group_sym <- rlang::sym(by)
+  model_formula <- rlang::new_formula(
+    lhs = rlang::expr(Surv(AVAL, is_event)),
+    rhs = rlang::expr(!!group_sym)
+  )
+
+  fit_kmg01 <- survival::survfit(model_formula, anl)
+
+  expect_no_error(
+    surv_plot_data <- process_survfit(fit_kmg01)
+  )
+
+  expect_no_error(
+    suppressWarnings(
+      coxph_tbl <- get_cox_pairwise_df(
+        model_formula,
+        data = anl,
+        arm = by
+      )
+    )
+  )
+
+  expect_no_error(
+    plt_kmg01 <- gg_km(surv_plot_data) |>
+      annotate_surv_med(fit_kmg01) |>
+      annotate_coxph(coxph_tbl) |>
+      annotate_riskdf(fit_kmg01)
+  )
+})
+
+test_that("df2gg() works with proper x-axis and without", {
+  # Example using proper x-axis
+  df <- as.data.frame(matrix(c(
+    #  0,  250, 500, 750, 1000  <-- (Reference)
+    54,  28,  10,   3,    0,
+    59,  35,  16,   5,    1,
+    54,  25,   4,   0,    0
+  ), nrow = 3, byrow = TRUE))
+
+  # Set names manually
+  colnames(df) <- c("0", "250", "500", "750", "1000")
+  rownames(df) <- c("A", "B", "C")
+
+  # Example with proper x-axis
+  expect_no_error(
+    null <- df2gg(df, font_size = 8, add_proper_xaxis = TRUE)
+  )
+
+  # Example without proper x-axis
+  expect_no_error(
+    null <- df2gg(df, font_size = 8, add_proper_xaxis = FALSE, hline = FALSE)
+  )
+})