#' Helper function to produce data frame with results

#' of pool for a single visit

#'

#' `r lifecycle::badge("experimental")`

#'

#' @param x (`pool`) is a list of pooled object from `rbmi` analysis results. This list includes

#' analysis results, confidence level, hypothesis testing type.

#' @return Data frame with results of pool for a single visit.

#' @export

#'

#' @examples

#' data("rbmi_test_data")

#' pool_obj <- rbmi_test_data

#'

#' h_tidy_pool(pool_obj$pars[1:3])

#'

h_tidy_pool <- function(x) {

  )

  )

  )

}

#' Helper method (for [`broom::tidy()`]) to prepare a data frame from an

#'   `pool` `rbmi` object containing the LS means and contrasts and multiple visits

#'

#' `r lifecycle::badge("experimental")`

#'

#' @method tidy pool

#' @param x (`pool`) is a list of pooled object from `rbmi` analysis results. This list includes

#' analysis results, confidence level, hypothesis testing type.

#' @param ... Additional arguments. Not used. Needed to match generic signature only.

#' @export

#' @return A dataframe

#'

tidy.pool <- function(x, ...) { # nolint

}

#' Statistics function which is extracting estimates from a tidied LS means

#'   data frame.

#'

#' `r lifecycle::badge("experimental")`

#'

#' @param df input dataframe

#' @param .in_ref_col boolean variable, if reference column is specified

#' @param show_relative "reduction" if (`control - treatment`, default) or "increase"

#'   (`treatment - control`) of relative change from baseline?

#' @return A list of statistics extracted from a tidied LS means data frame.

#' @export

#'

#' @examples

#' library(rtables)

#' library(dplyr)

#' library(broom)

#'

#' data("rbmi_test_data")

#' pool_obj <- rbmi_test_data

#' df <- tidy(pool_obj)

#'

#' s_rbmi_lsmeans(df[1, ], .in_ref_col = TRUE)

#'

#' s_rbmi_lsmeans(df[2, ], .in_ref_col = FALSE)

#'

s_rbmi_lsmeans <- function(df, .in_ref_col, show_relative = c("reduction", "increase")) {

    ),

    ),

    ),

  )

}

#' Formatted Analysis function which can be further customized by calling

#'   [`rtables::make_afun()`] on it. It is used as `afun` in [`rtables::analyze()`].

#'

#' `r lifecycle::badge("experimental")`

#'

#' @param df input dataframe

#' @param .in_ref_col boolean variable, if reference column is specified

#' @param show_relative "reduction" if (`control - treatment`, default) or "increase"

#'   (`treatment - control`) of relative change from baseline?

#' @return Formatted Analysis function

#' @export

#'

a_rbmi_lsmeans <- make_afun(

  s_rbmi_lsmeans,

  .labels = c(

    adj_mean_se = "Adjusted Mean (SE)",

    diff_mean_se = "Difference in Adjusted Means (SE)",

    p_value = "p-value (RBMI)"

  ),

  .formats = c(

    # n = "xx.", # note we don't have N from `rbmi` result

    adj_mean_se = sprintf_format("%.3f (%.3f)"),

    adj_mean_ci = "(xx.xxx, xx.xxx)",

    diff_mean_se = sprintf_format("%.3f (%.3f)"),

    diff_mean_ci = "(xx.xxx, xx.xxx)",

    change = "xx.x%",

    p_value = "x.xxxx | (<0.0001)"

  ),

  .indent_mods = c(

    adj_mean_ci = 1L,

    diff_mean_ci = 1L,

    change = 1L,

    p_value = 1L

  ),

  .null_ref_cells = FALSE

)

#' Analyze function for tabulating LS means estimates from tidied

#'   `rbmi` `pool` results.

#'

#' `r lifecycle::badge("experimental")`

#'

#' @param lyt (`layout`)\cr input layout where analyses will be added to.

#' @param table_names (`character`)\cr this can be customized in case that the same `vars` are analyzed multiple times,

#'   to avoid warnings from `rtables`.

#' @param .stats (`character`)\cr statistics to select for the table.

#' @param .formats (named `character` or `list`)\cr formats for the statistics.

#' @param .indent_mods (named `integer`)\cr indent modifiers for the labels.

#' @param .labels (named `character`)\cr labels for the statistics (without indent).

#' @param ... additional argument.

#' @return `rtables` layout for tabulating LS means estimates from tidied

#'   `rbmi` `pool` results.

#' @export

#'

#' @examples

#' library(rtables)

#' library(dplyr)

#' library(broom)

#'

#' data("rbmi_test_data")

#' pool_obj <- rbmi_test_data

#'

#' df <- tidy(pool_obj)

#'

#' basic_table() %>%

#'   split_cols_by("group", ref_group = levels(df$group)[1]) %>%

#'   split_rows_by("visit", split_label = "Visit", label_pos = "topleft") %>%

#'   summarize_rbmi() %>%

#'   build_table(df)

#'

summarize_rbmi <- function(lyt,

                           ...,

                           table_names = "rbmi_summary",

                           .stats = NULL,

                           .formats = NULL,

                           .indent_mods = NULL,

                           .labels = NULL) {

  )

  )

}