diff --git a/DESCRIPTION b/DESCRIPTION
index bc50e410e..efdcd3378 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -4,6 +4,7 @@ Version: 0.1.0.9009
 Authors@R: c(
     person("Daniel", "Sjoberg", , "danield.sjoberg@gmail.com", role = c("aut", "cre")),
     person("Abinaya", "Yogasekaram", , "abinaya.yogasekaram@contractors.roche.com", role = "aut"),
+    person("Emily", "de la Rua", , "emily.de_la_rua@contractors.roche.com", role = c("aut")),
     person("F. Hoffmann-La Roche AG", role = c("cph", "fnd"))
   )
 Description: Create extra Analysis Results Data (ARD) summary objects.
@@ -32,6 +33,7 @@ Suggests:
     smd (>= 0.6.6),
     spelling,
     survey (>= 4.1),
+    survival (>= 3.2-11),    
     testthat (>= 3.2.0),
     withr
 Remotes: 
diff --git a/NAMESPACE b/NAMESPACE
index 77b5603b6..69efe18d3 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -21,6 +21,7 @@ export(ard_proptest)
 export(ard_regression)
 export(ard_regression_basic)
 export(ard_smd)
+export(ard_survfit)
 export(ard_svychisq)
 export(ard_svycontinuous)
 export(ard_svyranktest)
diff --git a/NEWS.md b/NEWS.md
index 54733e01c..f38a43aa7 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -6,6 +6,7 @@
   - `ard_proptest()` for tests of proportions using `stats::prop.test()`. (#64)
   - `ard_regression_basic()` for basic regression models. The function focuses on matching terms to underlying variables names. (#46)
   - `ard_smd()` for calculating standardized mean differences using `smd::smd()`. (#4)
+  - `ard_survfit()` for survival analyses using `survival::survfit()`. (#43)
   - `ard_svycontinuous()` for calculating univariate summary statistics from weighted/survey data using many functions from the {survey} package. (#68)
   - `ard_svychisq()` for weighted/survey chi-squared test using `survey::svychisq()`. (#72)
   - `ard_svyttest()` for weighted/survey t-tests using `survey::svyttest()`. (#70)
diff --git a/R/ard_proportion_ci.R b/R/ard_proportion_ci.R
index 9ceabe174..d7d08d1a4 100644
--- a/R/ard_proportion_ci.R
+++ b/R/ard_proportion_ci.R
@@ -14,7 +14,7 @@
 #'   Default is `0.95`
 #' @param method (`string`)\cr
 #'   string indicating the type of confidence interval to calculate.
-#'   Must be one of `r formals(ard_proportion_ci)[["method"]] |> eval() |> shQuote()`.
+#'   Must be one of `r formals(ard_proportion_ci)[["method"]] |> eval() |> shQuote("sh")`.
 #'   See `?proportion_ci` for details.
 #' @param strata,weights,max.iterations arguments passed to `proportion_ci_strat_wilson()`,
 #'   when `method='strat_wilson'`
diff --git a/R/ard_survfit.R b/R/ard_survfit.R
new file mode 100644
index 000000000..e13709af4
--- /dev/null
+++ b/R/ard_survfit.R
@@ -0,0 +1,343 @@
+#' ARD Survival Estimates
+#'
+#' @description
+#' Analysis results data for survival quantiles and x-year survival estimates, extracted
+#' from a [survival::survfit()] model.
+#'
+#' @param x ([survival::survfit()])\cr
+#'   a [survival::survfit()] object. See below for details.
+#' @param times (`numeric`)\cr
+#'   a vector of times for which to return survival probabilities.
+#' @param probs (`numeric`)\cr
+#'   a vector of probabilities with values in (0,1) specifying the survival quantiles to return.
+#' @param type (`string` or `NULL`)\cr
+#'   type of statistic to report. Available for Kaplan-Meier time estimates only, otherwise `type`
+#'   is ignored. Default is `NULL`.
+#'   Must be one of the following:
+#'   ```{r, echo = FALSE}
+#'   dplyr::tribble(
+#'     ~type,          ~transformation,
+#'     '`"survival"`', '`x`',
+#'     '`"risk"`',     '`1 - x`',
+#'     '`"cumhaz"`',   '`-log(x)`',
+#'   ) %>%
+#'   knitr::kable()
+#'   ```
+#'
+#' @return an ARD data frame of class 'card'
+#' @name ard_survfit
+#'
+#' @details
+#' * Only one of either the `times` or `probs` parameters can be specified.
+#' * Times should be provided using the same scale as the time variable used to fit the provided
+#'   survival fit model.
+#'
+#' @examplesIf cards::is_pkg_installed(c("survival", "broom"), reference_pkg = "cardx")
+#' library(survival)
+#'
+#' survfit(Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+#'   ard_survfit(times = c(60, 180))
+#'
+#' survfit(Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+#'   ard_survfit(probs = c(0.25, 0.5, 0.75))
+#'
+#' # Competing Risks Example ---------------------------
+#' set.seed(1)
+#' ADTTE_MS <- cards::ADTTE %>%
+#'   dplyr::mutate(
+#'     CNSR = dplyr::case_when(
+#'       CNSR == 0 ~ "censor",
+#'       runif(dplyr::n()) < 0.5 ~ "death from cancer",
+#'       TRUE ~ "death other causes"
+#'     ) %>% factor()
+#'   )
+#'
+#' survfit(Surv(AVAL, CNSR) ~ TRTA, data = ADTTE_MS) %>%
+#'   ard_survfit(times = c(60, 180))
+NULL
+
+#' @rdname ard_survfit
+#' @export
+ard_survfit <- function(x, times = NULL, probs = NULL, type = NULL) {
+  # check installed packages ---------------------------------------------------
+  cards::check_pkg_installed(c("survival", "broom"), reference_pkg = "cardx")
+
+  # check/process inputs -------------------------------------------------------
+  check_not_missing(x)
+  check_class(x, cls = "survfit")
+  if (inherits(x, "survfitcox")) {
+    cli::cli_abort("Argument {.arg x} cannot be class {.cls survfitcox}.")
+  }
+
+  # competing risks models cannot use the type argument
+  if (inherits(x, c("survfitms", "survfitcoxms")) && !is.null(type)) {
+    cli::cli_abort("Cannot use {.arg type} argument with {.code survfit} models with class {.cls {c('survfitms', 'survfitcoxms')}}.")
+  }
+  if (!is.null(probs)) check_range(probs, c(0, 1))
+  if (sum(is.null(times), is.null(probs)) != 1) {
+    cli::cli_abort("One and only one of {.arg times} and {.arg probs} must be specified.")
+  }
+
+  # for regular KM estimators, we allow the type argument
+  if (!inherits(x, "survfitms") && !is.null(type)) {
+    type <- arg_match(type, values = c("survival", "risk", "cumhaz"))
+  }
+
+  # cannot specify type arg when probs supplied
+  if (!is.null(probs) && !is.null(type)) {
+    cli::cli_abort("Cannot use {.arg type} argument when {.arg probs} argument specifed.")
+  }
+
+  # build ARD ------------------------------------------------------------------
+  est_type <- ifelse(is.null(probs), "times", "probs")
+  tidy_survfit <- switch(est_type,
+    "times" = .process_survfit_time(x, times, type %||% "survival"),
+    "probs" = .process_survfit_probs(x, probs)
+  )
+
+  .format_survfit_results(tidy_survfit)
+}
+
+#' Process Survival Fit For Time Estimates
+#'
+#' @inheritParams cards::tidy_as_ard
+#' @inheritParams ard_survfit
+#'
+#' @return a `tibble`
+#'
+#' @examples
+#' survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+#'   cardx:::.process_survfit_time(times = c(60, 180), type = "risk")
+#'
+#' @keywords internal
+.process_survfit_time <- function(x, times, type) {
+  # tidy survfit results
+  tidy_x <- broom::tidy(x)
+
+  # process competing risks/multi-state models
+  multi_state <- inherits(x, "survfitms")
+
+  if (multi_state == TRUE) {
+    # selecting state to show
+    state <- setdiff(unique(tidy_x$state), "(s0)")[[1]]
+    cli::cli_inform("Multi-state model detected. Showing probabilities into state '{state}'.")
+    tidy_x <- dplyr::filter(tidy_x, .data$state == .env$state)
+  }
+
+  # adding time 0 to data frame
+  tidy_x <- tidy_x %>%
+    # make strata a fct to preserve ordering
+    dplyr::mutate(dplyr::across(dplyr::any_of("strata"), ~ factor(., levels = unique(.)))) %>%
+    # if CI is missing and SE is 0, use estimate as the CI
+    dplyr::mutate_at(
+      dplyr::vars("conf.high", "conf.low"),
+      ~ ifelse(is.na(.) & .data$std.error == 0, .data$estimate, .)
+    ) %>%
+    dplyr::select(dplyr::any_of(c("time", "estimate", "conf.high", "conf.low", "strata"))) %>%
+    # add data for time 0
+    dplyr::bind_rows(
+      dplyr::group_by_at(., dplyr::vars(dplyr::any_of("strata"))) %>%
+        dplyr::slice(1) %>%
+        dplyr::mutate(
+          time = 0,
+          estimate = ifelse(multi_state, 0, 1),
+          conf.low = ifelse(multi_state, 0, 1),
+          conf.high = ifelse(multi_state, 0, 1)
+        )
+    ) %>%
+    dplyr::ungroup()
+
+  strat <- "strata" %in% names(tidy_x)
+
+  # get requested estimates
+  df_stat <- tidy_x %>%
+    # find max time
+    dplyr::group_by_at(., dplyr::vars(dplyr::any_of("strata"))) %>%
+    dplyr::mutate(time_max = max(.data$time)) %>%
+    dplyr::ungroup() %>%
+    # add requested timepoints
+    dplyr::full_join(
+      tidy_x %>%
+        dplyr::select(any_of("strata")) %>%
+        dplyr::distinct() %>%
+        dplyr::mutate(
+          time = list(.env$times),
+          col_name = list(paste("stat", seq_len(length(.env$times)), sep = "_"))
+        ) %>%
+        tidyr::unnest(cols = c("time", "col_name")),
+      by = unlist(intersect(c("strata", "time"), names(tidy_x)))
+    )
+
+  if (strat) {
+    df_stat <- df_stat %>% dplyr::arrange(.data$strata)
+  }
+
+  df_stat <- df_stat %>%
+    # if user-specifed time is unobserved, fill estimate with previous value
+    dplyr::arrange(.data$time) %>%
+    dplyr::group_by_at(dplyr::vars(dplyr::any_of("strata"))) %>%
+    tidyr::fill(
+      "estimate", "conf.high", "conf.low", "time_max",
+      .direction = "down"
+    ) %>%
+    dplyr::ungroup() %>%
+    # keep only user-specified times
+    dplyr::filter(!is.na(.data$col_name)) %>%
+    # if user-specified time is after max time, make estimate NA
+    dplyr::mutate_at(
+      dplyr::vars("estimate", "conf.high", "conf.low"),
+      ~ ifelse(.data$time > .data$time_max, NA_real_, .)
+    ) %>%
+    dplyr::mutate(context = type) %>%
+    dplyr::select(!dplyr::any_of(c("time_max", "col_name")))
+
+  # convert estimates to requested type
+  if (type != "survival") {
+    df_stat <- df_stat %>%
+      dplyr::mutate(dplyr::across(
+        any_of(c("estimate", "conf.low", "conf.high")),
+        if (type == "cumhaz") ~ -log(.x) else ~ 1 - .x
+      )) %>%
+      dplyr::rename(conf.low = "conf.high", conf.high = "conf.low")
+  }
+
+  df_stat <- extract_multi_strata(x, df_stat)
+
+  df_stat
+}
+
+#' Process Survival Fit For Quantile Estimates
+#'
+#' @inheritParams cards::tidy_as_ard
+#' @inheritParams ard_survfit
+#'
+#' @return a `tibble`
+#'
+#' @examples
+#' survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+#'   cardx:::.process_survfit_probs(probs = c(0.25, 0.75))
+#'
+#' @keywords internal
+.process_survfit_probs <- function(x, probs) {
+  # calculate survival quantiles and add estimates to df
+  df_stat <- map2(
+    probs,
+    seq_along(probs),
+    ~ stats::quantile(x, probs = .x) %>%
+      as.data.frame() %>%
+      set_names(c("estimate", "conf.low", "conf.high")) %>%
+      dplyr::mutate(strata = row.names(.)) %>%
+      dplyr::select(dplyr::any_of(c("strata", "estimate", "conf.low", "conf.high"))) %>%
+      dplyr::mutate(prob = .x)
+  ) %>%
+    dplyr::bind_rows() %>%
+    `rownames<-`(NULL) %>%
+    dplyr::mutate(context = "survival") %>%
+    dplyr::as_tibble()
+
+  if (length(x$n) == 1) df_stat <- df_stat %>% dplyr::select(-"strata")
+
+  df_stat <- extract_multi_strata(x, df_stat)
+
+  df_stat
+}
+
+# process multiple stratifying variables
+extract_multi_strata <- function(x, df_stat) {
+  x_terms <- attr(stats::terms(stats::as.formula(x$call$formula)), "term.labels")
+  x_terms <- gsub(".*\\(", "", gsub("\\)", "", x_terms))
+  if (length(x_terms) > 1) {
+    strata_lvls <- data.frame()
+
+    for (i in df_stat[["strata"]]) {
+      i <- gsub(".*\\(", "", gsub("\\)", "", i))
+      terms_str <- strsplit(i, paste(c(paste0(x_terms, "="), paste0(", ", x_terms, "=")), collapse = "|"))[[1]]
+      s_lvl <- terms_str[nchar(terms_str) > 0]
+      strata_lvls <- rbind(strata_lvls, s_lvl)
+    }
+    if (nrow(strata_lvls) > 0) {
+      strata_lvls <- cbind(strata_lvls, t(x_terms))
+      names(strata_lvls) <- c(
+        t(sapply(seq_along(x_terms), function(i) c(paste0("group", i, "_level"), paste0("group", i))))
+      )
+      df_stat <- cbind(df_stat, strata_lvls) %>%
+        dplyr::select(-"strata")
+    }
+  }
+  df_stat
+}
+
+#' Convert Tidied Survival Fit to ARD
+#'
+#' @inheritParams cards::tidy_as_ard
+#'
+#' @return an ARD data frame of class 'card'
+#'
+#' @examples
+#' cardx:::.format_survfit_results(
+#'   broom::tidy(survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE))
+#' )
+#'
+#' @keywords internal
+.format_survfit_results <- function(tidy_survfit) {
+  est <- if ("time" %in% names(tidy_survfit)) "time" else "prob"
+
+  ret <- tidy_survfit %>%
+    dplyr::mutate(dplyr::across(
+      dplyr::any_of(c("estimate", "conf.high", "conf.low", "time", "prob")), ~ as.list(.)
+    )) %>%
+    tidyr::pivot_longer(
+      cols = dplyr::any_of(c("estimate", "conf.high", "conf.low")),
+      names_to = "stat_name",
+      values_to = "stat"
+    ) %>%
+    dplyr::mutate(
+      variable = est,
+      variable_level = .data[[est]]
+    ) %>%
+    dplyr::select(-all_of(est))
+
+  if ("strata" %in% names(ret)) {
+    ret <- ret %>%
+      tidyr::separate_wider_delim("strata", "=", names = c("group1", "group1_level"))
+  }
+
+  ret %>%
+    dplyr::left_join(
+      .df_survfit_stat_labels(),
+      by = "stat_name"
+    ) %>%
+    dplyr::mutate(
+      fmt_fn = lapply(
+        .data$stat,
+        function(x) {
+          switch(is.integer(x),
+            0L
+          ) %||% switch(is.numeric(x),
+            1L
+          )
+        }
+      ),
+      stat_label = dplyr::coalesce(.data$stat_label, .data$stat_name)
+    ) %>%
+    dplyr::mutate(dplyr::across(matches("group[0-9]*_level"), ~ as.list(as.factor(.x)))) %>%
+    dplyr::mutate(
+      warning = list(NULL),
+      error = list(NULL)
+    ) %>%
+    structure(., class = c("card", class(.))) %>%
+    cards::tidy_ard_column_order() %>%
+    cards::tidy_ard_row_order()
+}
+
+.df_survfit_stat_labels <- function() {
+  dplyr::tribble(
+    ~stat_name, ~stat_label,
+    "estimate", "Survival Probability",
+    "conf.low", "CI Lower Bound",
+    "conf.high", "CI Upper Bound",
+    "conf.level", "CI Confidence Level",
+    "prob", "Quantile",
+    "time", "Time"
+  )
+}
diff --git a/R/ard_svycontinuous.R b/R/ard_svycontinuous.R
index cb719d27b..762069cbe 100644
--- a/R/ard_svycontinuous.R
+++ b/R/ard_svycontinuous.R
@@ -27,7 +27,7 @@
 #' @section statistic argument:
 #'
 #' The following statistics are available:
-#' `r cardx:::accepted_svy_stats(FALSE) |> shQuote() |> paste(collapse = ", ")`,
+#' `r cardx:::accepted_svy_stats(FALSE) |> shQuote("sh") |> paste(collapse = ", ")`,
 #' where 'p##' is are the percentiles and `##` is an integer between 0 and 100.
 #'
 #'
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 419b3d101..c58401c7b 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -45,6 +45,7 @@ reference:
       - ard_regression
       - ard_regression_basic
       - ard_smd
+      - ard_survfit
       - ard_svycontinuous
       - ard_vif
 
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 70899e5b5..a703d5cda 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -6,10 +6,12 @@ Clopper
 Codecov
 Hoffmann
 Jeffreys
+Kaplan
 Lifecycle
 McNemar's
 Newcombe
 Rao
+Rua
 Su
 VIF
 XG
@@ -17,6 +19,7 @@ Xin
 agresti
 clopper
 coull
+de
 deff
 funder
 jeffreys
diff --git a/man/ard_survfit.Rd b/man/ard_survfit.Rd
new file mode 100644
index 000000000..56eeac987
--- /dev/null
+++ b/man/ard_survfit.Rd
@@ -0,0 +1,67 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ard_survfit.R
+\name{ard_survfit}
+\alias{ard_survfit}
+\title{ARD Survival Estimates}
+\usage{
+ard_survfit(x, times = NULL, probs = NULL, type = NULL)
+}
+\arguments{
+\item{x}{(\code{\link[survival:survfit]{survival::survfit()}})\cr
+a \code{\link[survival:survfit]{survival::survfit()}} object. See below for details.}
+
+\item{times}{(\code{numeric})\cr
+a vector of times for which to return survival probabilities.}
+
+\item{probs}{(\code{numeric})\cr
+a vector of probabilities with values in (0,1) specifying the survival quantiles to return.}
+
+\item{type}{(\code{string} or \code{NULL})\cr
+type of statistic to report. Available for Kaplan-Meier time estimates only, otherwise \code{type}
+is ignored. Default is \code{NULL}.
+Must be one of the following:\tabular{ll}{
+   type \tab transformation \cr
+   \code{"survival"} \tab \code{x} \cr
+   \code{"risk"} \tab \code{1 - x} \cr
+   \code{"cumhaz"} \tab \code{-log(x)} \cr
+}}
+}
+\value{
+an ARD data frame of class 'card'
+}
+\description{
+Analysis results data for survival quantiles and x-year survival estimates, extracted
+from a \code{\link[survival:survfit]{survival::survfit()}} model.
+}
+\details{
+\itemize{
+\item Only one of either the \code{times} or \code{probs} parameters can be specified.
+\item Times should be provided using the same scale as the time variable used to fit the provided
+survival fit model.
+}
+}
+\examples{
+\dontshow{if (cards::is_pkg_installed(c("survival", "broom"), reference_pkg = "cardx")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+library(survival)
+
+survfit(Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+  ard_survfit(times = c(60, 180))
+
+survfit(Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+  ard_survfit(probs = c(0.25, 0.5, 0.75))
+
+# Competing Risks Example ---------------------------
+set.seed(1)
+ADTTE_MS <- cards::ADTTE \%>\%
+  dplyr::mutate(
+    CNSR = dplyr::case_when(
+      CNSR == 0 ~ "censor",
+      runif(dplyr::n()) < 0.5 ~ "death from cancer",
+      TRUE ~ "death other causes"
+    ) \%>\% factor()
+  )
+
+survfit(Surv(AVAL, CNSR) ~ TRTA, data = ADTTE_MS) \%>\%
+  ard_survfit(times = c(60, 180))
+\dontshow{\}) # examplesIf}
+}
diff --git a/man/cardx-package.Rd b/man/cardx-package.Rd
index 545747c76..2afc46c23 100644
--- a/man/cardx-package.Rd
+++ b/man/cardx-package.Rd
@@ -24,6 +24,7 @@ Useful links:
 Authors:
 \itemize{
   \item Abinaya Yogasekaram \email{abinaya.yogasekaram@contractors.roche.com}
+  \item Emily de la Rua \email{emily.de_la_rua@contractors.roche.com}
 }
 
 Other contributors:
diff --git a/man/dot-format_survfit_results.Rd b/man/dot-format_survfit_results.Rd
new file mode 100644
index 000000000..d1a1e36fb
--- /dev/null
+++ b/man/dot-format_survfit_results.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ard_survfit.R
+\name{.format_survfit_results}
+\alias{.format_survfit_results}
+\title{Convert Tidied Survival Fit to ARD}
+\usage{
+.format_survfit_results(tidy_survfit)
+}
+\value{
+an ARD data frame of class 'card'
+}
+\description{
+Convert Tidied Survival Fit to ARD
+}
+\examples{
+cardx:::.format_survfit_results(
+  broom::tidy(survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE))
+)
+
+}
+\keyword{internal}
diff --git a/man/dot-process_survfit_probs.Rd b/man/dot-process_survfit_probs.Rd
new file mode 100644
index 000000000..4e2a89923
--- /dev/null
+++ b/man/dot-process_survfit_probs.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ard_survfit.R
+\name{.process_survfit_probs}
+\alias{.process_survfit_probs}
+\title{Process Survival Fit For Quantile Estimates}
+\usage{
+.process_survfit_probs(x, probs)
+}
+\arguments{
+\item{x}{(\code{\link[survival:survfit]{survival::survfit()}})\cr
+a \code{\link[survival:survfit]{survival::survfit()}} object. See below for details.}
+
+\item{probs}{(\code{numeric})\cr
+a vector of probabilities with values in (0,1) specifying the survival quantiles to return.}
+}
+\value{
+a \code{tibble}
+}
+\description{
+Process Survival Fit For Quantile Estimates
+}
+\examples{
+survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+  cardx:::.process_survfit_probs(probs = c(0.25, 0.75))
+
+}
+\keyword{internal}
diff --git a/man/dot-process_survfit_time.Rd b/man/dot-process_survfit_time.Rd
new file mode 100644
index 000000000..6fe6a0dd7
--- /dev/null
+++ b/man/dot-process_survfit_time.Rd
@@ -0,0 +1,37 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ard_survfit.R
+\name{.process_survfit_time}
+\alias{.process_survfit_time}
+\title{Process Survival Fit For Time Estimates}
+\usage{
+.process_survfit_time(x, times, type)
+}
+\arguments{
+\item{x}{(\code{\link[survival:survfit]{survival::survfit()}})\cr
+a \code{\link[survival:survfit]{survival::survfit()}} object. See below for details.}
+
+\item{times}{(\code{numeric})\cr
+a vector of times for which to return survival probabilities.}
+
+\item{type}{(\code{string} or \code{NULL})\cr
+type of statistic to report. Available for Kaplan-Meier time estimates only, otherwise \code{type}
+is ignored. Default is \code{NULL}.
+Must be one of the following:\tabular{ll}{
+   type \tab transformation \cr
+   \code{"survival"} \tab \code{x} \cr
+   \code{"risk"} \tab \code{1 - x} \cr
+   \code{"cumhaz"} \tab \code{-log(x)} \cr
+}}
+}
+\value{
+a \code{tibble}
+}
+\description{
+Process Survival Fit For Time Estimates
+}
+\examples{
+survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+  cardx:::.process_survfit_time(times = c(60, 180), type = "risk")
+
+}
+\keyword{internal}
diff --git a/tests/testthat/_snaps/ard_survfit.md b/tests/testthat/_snaps/ard_survfit.md
new file mode 100644
index 000000000..5d209bb98
--- /dev/null
+++ b/tests/testthat/_snaps/ard_survfit.md
@@ -0,0 +1,261 @@
+# ard_survfit() works with times provided
+
+    Code
+      print(dplyr::mutate(ard_survfit(survival::survfit(survival::Surv(AVAL, CNSR) ~
+        TRTA, cards::ADTTE), times = c(60, 180)), stat = lapply(stat, function(x)
+        ifelse(is.numeric(x), cards::round5(x, 3), x))), n = Inf)
+    Message
+      {cards} data frame: 18 x 11
+    Output
+         group1 group1_level variable variable_level stat_name stat_label  stat
+      1    TRTA      Placebo     time             60  estimate  Survival… 0.893
+      2    TRTA      Placebo     time             60 conf.high  CI Upper… 0.966
+      3    TRTA      Placebo     time             60  conf.low  CI Lower… 0.825
+      4    TRTA      Placebo     time            180  estimate  Survival… 0.651
+      5    TRTA      Placebo     time            180 conf.high  CI Upper… 0.783
+      6    TRTA      Placebo     time            180  conf.low  CI Lower… 0.541
+      7    TRTA    Xanomeli…     time             60  estimate  Survival… 0.694
+      8    TRTA    Xanomeli…     time             60 conf.high  CI Upper… 0.849
+      9    TRTA    Xanomeli…     time             60  conf.low  CI Lower… 0.568
+      10   TRTA    Xanomeli…     time            180  estimate  Survival… 0.262
+      11   TRTA    Xanomeli…     time            180 conf.high  CI Upper… 0.749
+      12   TRTA    Xanomeli…     time            180  conf.low  CI Lower… 0.092
+      13   TRTA    Xanomeli…     time             60  estimate  Survival… 0.732
+      14   TRTA    Xanomeli…     time             60 conf.high  CI Upper… 0.878
+      15   TRTA    Xanomeli…     time             60  conf.low  CI Lower…  0.61
+      16   TRTA    Xanomeli…     time            180  estimate  Survival… 0.381
+      17   TRTA    Xanomeli…     time            180 conf.high  CI Upper… 0.743
+      18   TRTA    Xanomeli…     time            180  conf.low  CI Lower… 0.195
+    Message
+      i 4 more variables: context, fmt_fn, warning, error
+
+# ard_survfit() works with different type
+
+    Code
+      print(dplyr::mutate(ard_survfit(survival::survfit(survival::Surv(AVAL, CNSR) ~
+        TRTA, cards::ADTTE), times = c(60, 180), type = "risk"), stat = lapply(stat,
+        function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))), n = Inf)
+    Message
+      {cards} data frame: 18 x 11
+    Output
+         group1 group1_level variable variable_level stat_name stat_label  stat
+      1    TRTA      Placebo     time             60  estimate  Survival… 0.107
+      2    TRTA      Placebo     time             60 conf.high  CI Upper… 0.175
+      3    TRTA      Placebo     time             60  conf.low  CI Lower… 0.034
+      4    TRTA      Placebo     time            180  estimate  Survival… 0.349
+      5    TRTA      Placebo     time            180 conf.high  CI Upper… 0.459
+      6    TRTA      Placebo     time            180  conf.low  CI Lower… 0.217
+      7    TRTA    Xanomeli…     time             60  estimate  Survival… 0.306
+      8    TRTA    Xanomeli…     time             60 conf.high  CI Upper… 0.432
+      9    TRTA    Xanomeli…     time             60  conf.low  CI Lower… 0.151
+      10   TRTA    Xanomeli…     time            180  estimate  Survival… 0.738
+      11   TRTA    Xanomeli…     time            180 conf.high  CI Upper… 0.908
+      12   TRTA    Xanomeli…     time            180  conf.low  CI Lower… 0.251
+      13   TRTA    Xanomeli…     time             60  estimate  Survival… 0.268
+      14   TRTA    Xanomeli…     time             60 conf.high  CI Upper…  0.39
+      15   TRTA    Xanomeli…     time             60  conf.low  CI Lower… 0.122
+      16   TRTA    Xanomeli…     time            180  estimate  Survival… 0.619
+      17   TRTA    Xanomeli…     time            180 conf.high  CI Upper… 0.805
+      18   TRTA    Xanomeli…     time            180  conf.low  CI Lower… 0.257
+    Message
+      i 4 more variables: context, fmt_fn, warning, error
+
+# ard_survfit() works with probs provided
+
+    Code
+      print(dplyr::mutate(ard_survfit(survival::survfit(survival::Surv(AVAL, CNSR) ~
+        TRTA, cards::ADTTE), probs = c(0.25, 0.75)), stat = lapply(stat, function(x)
+        ifelse(is.numeric(x), cards::round5(x, 3), x))), n = Inf)
+    Message
+      {cards} data frame: 18 x 11
+    Output
+         group1 group1_level variable variable_level stat_name stat_label stat
+      1    TRTA      Placebo     prob           0.25  estimate  Survival…  142
+      2    TRTA      Placebo     prob           0.25 conf.high  CI Upper…  181
+      3    TRTA      Placebo     prob           0.25  conf.low  CI Lower…   70
+      4    TRTA      Placebo     prob           0.75  estimate  Survival…  184
+      5    TRTA      Placebo     prob           0.75 conf.high  CI Upper…  191
+      6    TRTA      Placebo     prob           0.75  conf.low  CI Lower…  183
+      7    TRTA    Xanomeli…     prob           0.25  estimate  Survival…   44
+      8    TRTA    Xanomeli…     prob           0.25 conf.high  CI Upper…  180
+      9    TRTA    Xanomeli…     prob           0.25  conf.low  CI Lower…   22
+      10   TRTA    Xanomeli…     prob           0.75  estimate  Survival…  188
+      11   TRTA    Xanomeli…     prob           0.75 conf.high  CI Upper…   NA
+      12   TRTA    Xanomeli…     prob           0.75  conf.low  CI Lower…  167
+      13   TRTA    Xanomeli…     prob           0.25  estimate  Survival…   49
+      14   TRTA    Xanomeli…     prob           0.25 conf.high  CI Upper…  180
+      15   TRTA    Xanomeli…     prob           0.25  conf.low  CI Lower…   37
+      16   TRTA    Xanomeli…     prob           0.75  estimate  Survival…  184
+      17   TRTA    Xanomeli…     prob           0.75 conf.high  CI Upper…   NA
+      18   TRTA    Xanomeli…     prob           0.75  conf.low  CI Lower…  180
+    Message
+      i 4 more variables: context, fmt_fn, warning, error
+
+# ard_survfit() works with unstratified model
+
+    Code
+      print(dplyr::mutate(ard_survfit(survival::survfit(survival::Surv(time, status) ~
+        1, data = survival::lung), times = c(60, 180)), stat = lapply(stat, function(
+        x) ifelse(is.numeric(x), cards::round5(x, 3), x))), n = Inf)
+    Message
+      {cards} data frame: 6 x 9
+    Output
+        variable variable_level  context stat_name stat_label  stat
+      1     time             60 survival  estimate  Survival… 0.925
+      2     time             60 survival conf.high  CI Upper…  0.96
+      3     time             60 survival  conf.low  CI Lower… 0.892
+      4     time            180 survival  estimate  Survival… 0.722
+      5     time            180 survival conf.high  CI Upper… 0.783
+      6     time            180 survival  conf.low  CI Lower… 0.666
+    Message
+      i 3 more variables: fmt_fn, warning, error
+
+---
+
+    Code
+      print(dplyr::mutate(ard_survfit(survival::survfit(survival::Surv(time, status) ~
+        1, data = survival::lung), probs = c(0.5, 0.75)), stat = lapply(stat,
+        function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))), n = Inf)
+    Message
+      {cards} data frame: 6 x 9
+    Output
+        variable variable_level  context stat_name stat_label stat
+      1     prob            0.5 survival  estimate  Survival…  310
+      2     prob            0.5 survival conf.high  CI Upper…  363
+      3     prob            0.5 survival  conf.low  CI Lower…  285
+      4     prob           0.75 survival  estimate  Survival…  550
+      5     prob           0.75 survival conf.high  CI Upper…  654
+      6     prob           0.75 survival  conf.low  CI Lower…  460
+    Message
+      i 3 more variables: fmt_fn, warning, error
+
+# ard_survfit() works with multiple stratification variables
+
+    Code
+      print(head(dplyr::select(dplyr::mutate(ard_survfit(survival::survfit(survival::Surv(
+        time, status) ~ sex + ph.ecog, data = survival::lung), times = c(60, 180)),
+      stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))),
+      "group1", "group1_level", "group2", "group2_level"), 20), n = Inf)
+    Message
+      {cards} data frame: 20 x 4
+    Output
+         group1 group1_level  group2 group2_level
+      1     sex            1 ph.ecog            0
+      2     sex            1 ph.ecog            0
+      3     sex            1 ph.ecog            0
+      4     sex            1 ph.ecog            0
+      5     sex            1 ph.ecog            0
+      6     sex            1 ph.ecog            0
+      7     sex            1 ph.ecog            1
+      8     sex            1 ph.ecog            1
+      9     sex            1 ph.ecog            1
+      10    sex            1 ph.ecog            1
+      11    sex            1 ph.ecog            1
+      12    sex            1 ph.ecog            1
+      13    sex            1 ph.ecog            2
+      14    sex            1 ph.ecog            2
+      15    sex            1 ph.ecog            2
+      16    sex            1 ph.ecog            2
+      17    sex            1 ph.ecog            2
+      18    sex            1 ph.ecog            2
+      19    sex            1 ph.ecog            3
+      20    sex            1 ph.ecog            3
+
+---
+
+    Code
+      print(head(dplyr::select(dplyr::mutate(ard_survfit(survival::survfit(survival::Surv(
+        time, status) ~ sex + ph.ecog, data = survival::lung), probs = c(0.5, 0.75)),
+      stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))),
+      "group1", "group1_level", "group2", "group2_level"), 20), n = Inf)
+    Message
+      {cards} data frame: 20 x 4
+    Output
+         group1 group1_level  group2 group2_level
+      1     sex            1 ph.ecog            0
+      2     sex            1 ph.ecog            0
+      3     sex            1 ph.ecog            0
+      4     sex            1 ph.ecog            0
+      5     sex            1 ph.ecog            0
+      6     sex            1 ph.ecog            0
+      7     sex            1 ph.ecog            1
+      8     sex            1 ph.ecog            1
+      9     sex            1 ph.ecog            1
+      10    sex            1 ph.ecog            1
+      11    sex            1 ph.ecog            1
+      12    sex            1 ph.ecog            1
+      13    sex            1 ph.ecog            2
+      14    sex            1 ph.ecog            2
+      15    sex            1 ph.ecog            2
+      16    sex            1 ph.ecog            2
+      17    sex            1 ph.ecog            2
+      18    sex            1 ph.ecog            2
+      19    sex            1 ph.ecog            3
+      20    sex            1 ph.ecog            3
+
+# ard_survfit() works with competing risks
+
+    Code
+      print(dplyr::mutate(survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, data = ADTTE_MS) %>%
+        ard_survfit(times = c(60, 180)), stat = lapply(stat, function(x) ifelse(
+        is.numeric(x), cards::round5(x, 3), x))), n = Inf)
+    Message
+      Multi-state model detected. Showing probabilities into state 'death from cancer'.
+      {cards} data frame: 18 x 11
+    Output
+         group1 group1_level variable variable_level stat_name stat_label  stat
+      1    TRTA      Placebo     time             60  estimate  Survival… 0.054
+      2    TRTA      Placebo     time             60 conf.high  CI Upper…  0.14
+      3    TRTA      Placebo     time             60  conf.low  CI Lower… 0.021
+      4    TRTA      Placebo     time            180  estimate  Survival… 0.226
+      5    TRTA      Placebo     time            180 conf.high  CI Upper… 0.361
+      6    TRTA      Placebo     time            180  conf.low  CI Lower… 0.142
+      7    TRTA    Xanomeli…     time             60  estimate  Survival… 0.137
+      8    TRTA    Xanomeli…     time             60 conf.high  CI Upper… 0.311
+      9    TRTA    Xanomeli…     time             60  conf.low  CI Lower…  0.06
+      10   TRTA    Xanomeli…     time            180  estimate  Survival…  0.51
+      11   TRTA    Xanomeli…     time            180 conf.high  CI Upper… 0.892
+      12   TRTA    Xanomeli…     time            180  conf.low  CI Lower… 0.292
+      13   TRTA    Xanomeli…     time             60  estimate  Survival… 0.162
+      14   TRTA    Xanomeli…     time             60 conf.high  CI Upper…  0.33
+      15   TRTA    Xanomeli…     time             60  conf.low  CI Lower…  0.08
+      16   TRTA    Xanomeli…     time            180  estimate  Survival… 0.244
+      17   TRTA    Xanomeli…     time            180 conf.high  CI Upper… 0.516
+      18   TRTA    Xanomeli…     time            180  conf.low  CI Lower… 0.115
+    Message
+      i 4 more variables: context, fmt_fn, warning, error
+
+# ard_survfit() errors are properly handled
+
+    Code
+      ard_survfit("not_survfit")
+    Condition
+      Error in `ard_survfit()`:
+      ! The `x` argument must be class <survfit>, not a string.
+
+---
+
+    Code
+      ard_survfit(survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE),
+      times = 100, type = "notatype")
+    Condition
+      Error in `ard_survfit()`:
+      ! `type` must be one of "survival", "risk", or "cumhaz", not "notatype".
+
+---
+
+    Code
+      ard_survfit(survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE),
+      times = 100, probs = c(0.25, 0.75))
+    Condition
+      Error in `ard_survfit()`:
+      ! One and only one of `times` and `probs` must be specified.
+
+# ard_survfit() errors with stratified Cox model
+
+    Code
+      ard_survfit(survfit(coxph(Surv(time, status) ~ age + strata(sex), survival::lung)))
+    Condition
+      Error in `ard_survfit()`:
+      ! Argument `x` cannot be class <survfitcox>.
+
diff --git a/tests/testthat/test-ard_survfit.R b/tests/testthat/test-ard_survfit.R
new file mode 100644
index 000000000..80681fab5
--- /dev/null
+++ b/tests/testthat/test-ard_survfit.R
@@ -0,0 +1,143 @@
+skip_if_not(cards::is_pkg_installed(c("survival", "broom"), reference_pkg = "cardx"))
+
+test_that("ard_survfit() works with times provided", {
+  expect_snapshot(
+    survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+      ard_survfit(times = c(60, 180)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ) |>
+      print(n = Inf)
+  )
+})
+
+test_that("ard_survfit() works with different type", {
+  expect_snapshot(
+    survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+      ard_survfit(times = c(60, 180), type = "risk") |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ) |>
+      print(n = Inf)
+  )
+})
+
+test_that("ard_survfit() works with probs provided", {
+  expect_snapshot(
+    survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+      ard_survfit(probs = c(0.25, 0.75)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ) |>
+      print(n = Inf)
+  )
+})
+
+test_that("ard_survfit() works with unstratified model", {
+  expect_snapshot(
+    survival::survfit(survival::Surv(time, status) ~ 1, data = survival::lung) |>
+      ard_survfit(times = c(60, 180)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ) |>
+      print(n = Inf)
+  )
+
+  expect_snapshot(
+    survival::survfit(survival::Surv(time, status) ~ 1, data = survival::lung) |>
+      ard_survfit(probs = c(0.5, 0.75)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ) |>
+      print(n = Inf)
+  )
+})
+
+test_that("ard_survfit() works with multiple stratification variables", {
+  expect_snapshot(
+    survival::survfit(survival::Surv(time, status) ~ sex + ph.ecog, data = survival::lung) |>
+      ard_survfit(times = c(60, 180)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ) |>
+      dplyr::select("group1", "group1_level", "group2", "group2_level") |>
+      head(20) |>
+      print(n = Inf)
+  )
+
+  expect_snapshot(
+    survival::survfit(survival::Surv(time, status) ~ sex + ph.ecog, data = survival::lung) |>
+      ard_survfit(probs = c(0.5, 0.75)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ) |>
+      dplyr::select("group1", "group1_level", "group2", "group2_level") |>
+      head(20) |>
+      print(n = Inf)
+  )
+})
+
+test_that("ard_survfit() works with competing risks", {
+  set.seed(1)
+  ADTTE_MS <- cards::ADTTE %>%
+    dplyr::mutate(
+      CNSR = dplyr::case_when(
+        CNSR == 0 ~ "censor",
+        runif(dplyr::n()) < 0.5 ~ "death from cancer",
+        TRUE ~ "death other causes"
+      ) %>% factor()
+    )
+
+  expect_snapshot(
+    survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, data = ADTTE_MS) %>%
+      ard_survfit(times = c(60, 180)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ) |>
+      print(n = Inf)
+  )
+})
+
+test_that("ard_survfit() errors are properly handled", {
+  expect_snapshot(
+    ard_survfit("not_survfit"),
+    error = TRUE
+  )
+
+  expect_snapshot(
+    survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+      ard_survfit(times = 100, type = "notatype"),
+    error = TRUE
+  )
+
+  expect_snapshot(
+    survival::survfit(survival::Surv(AVAL, CNSR) ~ TRTA, cards::ADTTE) |>
+      ard_survfit(times = 100, probs = c(0.25, 0.75)),
+    error = TRUE
+  )
+})
+
+test_that("ard_survfit() works with non-syntactic names", {
+  expect_equal(
+    survival::survfit(survival::Surv(time, status) ~ factor(sex) + `ph.ecog`, data = survival::lung) |>
+      ard_survfit(times = c(60, 180)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      ),
+    survival::survfit(survival::Surv(time, status) ~ sex + ph.ecog, data = survival::lung) |>
+      ard_survfit(times = c(60, 180)) |>
+      dplyr::mutate(
+        stat = lapply(stat, function(x) ifelse(is.numeric(x), cards::round5(x, 3), x))
+      )
+  )
+})
+
+test_that("ard_survfit() errors with stratified Cox model", {
+  withr::local_namespace("survival")
+  expect_snapshot(
+    error = TRUE,
+    coxph(Surv(time, status) ~ age + strata(sex), survival::lung) |>
+      survfit() |>
+      ard_survfit()
+  )
+})