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+---
+title: "Building Tables using tbl_ard*()"
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+For most tables built using {gtsummary}, ARDs for quality control or table summaries can be extracted. 
+However, there are instances where an ARD first approach is beneficial as building more complex tables may require analysis of the data structures prior to table construction. 
+For this ARD first approach, {gtsummary} has `tbl_ard*()` functions that will generate a tables.
+
+-   `tbl_ard_continuous()` - for ARDs summarizing continuous variables.
+-   `tbl_ard_summary()` - for ARDs with descriptive statistics for continuous, categorical and dichotomous variables.
+-   `tbl_ard_wide_summary()` - for ARD statistics represented in a wide table format - in separate columns.
+-   `tbl_ard_hierarchical()` - for ARDs containing nested or hierarchical data structures.
+
+### Building a basic demographics table
+
+In this example, we will build a simple demographics and baseline characteristics table as outlined in the FDA Standard Safety Tables Guidelines.
+
+This table has three types of data summaries: a continuous variable summary for AGE, a categorical variable summary for AGEGR1, RACE, and ETHNIC and a dichotomous variable summary for SEX.
+
+There are three additional arguments that can be specified to improve the appearance of the table.
+- `.attributes` the default labels will be the variable labels if attributes are included.
+- `.missing` when missing results are included, users can include missing counts or rates for the variables.
+- `.total_n` the total N is saved internally when available.
+
+```{r}
+library(gtsummary)
+library(cards)
+
+# summarizing both continuous and categorical variables, consolidate using ard_stack call
+ard_stack(
+  data = cards::ADSL,
+  .by = ARM,
+  ard_categorical(variables = c("AGEGR1", "RACE", "SEX", "ETHNIC")),
+  ard_continuous(variables = "AGE"),
+  .attributes = TRUE,
+  .missing = TRUE,
+  .total_n = TRUE,
+  .overall = TRUE # generate ard with overall column included
+  ) |> # pass to tbl_ard_summary to generate a table.
+  tbl_ard_summary(by = ARM, overall = TRUE) # pass ard to table building function with the overall argument.
+
+```
+
+Notice this ARD first approach builds an equivalent table to one built with `tbl_summary()`
+
+```{r}
+
+cards::ADSL |>
+  select(c("AGEGR1", "RACE", "SEX", "ETHNIC", "AGE", "ARM")) |>
+  tbl_summary(by = ARM) |>
+  add_overall() 
+
+```
+The ARD for this table can be extracted using the `gather_ard()` function.
+
+### Building a disposition table
+Here, we use tbl_ard_wide_summary to build a Pooled disposition table. 
+
+```{r}
+# all variables within tbl_ard_wide_summary must be the same summary type
+ard_stack(
+  trial,
+  ard_dichotomous(variables = response),
+  ard_categorical(variables = grade),
+  .missing = TRUE,
+  .attributes = TRUE,
+  .total_n = TRUE
+) |>
+  tbl_ard_wide_summary()
+
+```
+Notice the extracted ARD from `tbl_ard_summary()` (using `gather_ard()`) is equivalent to the ARD built using `tbl_ard_wide_summary()`.
+```{r}
+trial |>
+  select(response, grade) |>
+tbl_summary(missing = 'no') |>
+  gather_ard()
+```
+
+
+### Building an Adverse Events table
+
+For tables that require hierarchical or nested analysis, `ard_stack_hierarchical()` is recommended for building an ARD. The companion function for rendering the table using the ARD is `tbl_ard_hierarchical()`
+
+```{r}
+# Adverse Events Rates Table
+
+ADAE_subset <- cards::ADAE |>
+  dplyr::filter(
+    AESOC %in% unique(cards::ADAE$AESOC)[1:5],
+    AETERM %in% unique(cards::ADAE$AETERM)[1:5])
+
+ard <- cards::ard_stack_hierarchical(
+  data = ADAE_subset,
+  variables = c(AESOC, AETERM),
+  by = TRTA,
+  denominator = cards::ADSL|> mutate(TRT01A = ARM),
+  id = USUBJID
+  )
+
+tbl_ard_hierarchical(
+   cards = ard,
+   variables = c(AESOC, AETERM),
+   by = TRTA
+ )
+
+```
+
+### Complex Analysis Tables
+
+The ARD to Table pipeline is most convenient when trying to consolidate multiple analysis steps into an ARD to feed only the relevant stats to the table building machinery. 
+In the example below, we create ARDs that compute the survival probabilities of both treatment groups, and also conduct a one sample t-test on the survival estimates. 
+The resulting ARD is filtered to only contain the relevant estimate values which can be used generate the table we want. 
+Note that in this pipeline, all the survival analysis stats such as confidence intervals and and standard error are retained in the ARD object - enabling reuse of this object to generate other tables. 
+
+```{r setup}
+
+library(gtsummary)
+library(cardx)
+
+ard <- cards::bind_ard(
+    survival::survfit(survival::Surv(ttdeath, death) ~ trt, trial) |>
+      cardx::ard_survival_survfit(times = c(12, 24)) |>
+      dplyr::filter(stat_name %in% c("estimate")) |> # filtering only the survival probability values.
+      dplyr::mutate( # apply custom formatting to these values - 1 decimal place
+        fmt_fn = list("xx.x%"),
+        group1_level = unlist(group1_level) |> as.character() |> as.list()
+      ),
+    # run a one sample t-test between the means of the survival probability for each treatment group
+    cardx::ard_stats_t_test_onesample(trial, variables = age, by = trt) |> 
+      dplyr::filter(stat_name %in% c("estimate"))
+  ) |>
+    dplyr::select(-cards::all_missing_columns())
+
+tbl_ard_summary(ard, by = trt, statistic = ~ "{estimate}")
+
+```