tests/testthat/_snaps/tm_t_events_by_grade.md

In teal.modules.clinical: 'teal' Modules for Standard Clinical Outputs

template_events_by_grade generates standard expressions

Code
  res
Output
  $data
  {
      anl <- adae
      anl <- anl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(anl[["ACTARM"]])
      adsl <- adsl %>% dplyr::filter(ACTARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      adae <- df_explicit_na(adae, na_level = "<Missing>")
      anl <- df_explicit_na(anl, na_level = "<Missing>")
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      grade_groups <- list(`- Any Intensity -` = levels(adae$AESEV))
  }

  $layout_prep
  split_fun <- trim_levels_in_group

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Adverse Event summary by Analysis Toxicity Grade: Body System or Organ Class and Dictionary-Derived Term") %>% 
      rtables::split_cols_by("ACTARM") %>% rtables::add_overall_col(label = "All Patients") %>% 
      summarize_occurrences_by_grade(var = "AESEV", grade_groups = grade_groups, 
          na_str = "<Missing>") %>% rtables::split_rows_by("AEBODSYS", 
      child_labels = "visible", nested = TRUE, indent_mod = -1L, 
      split_fun = split_fun("AESEV"), label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_occurrences_by_grade(var = "AESEV", grade_groups = grade_groups, 
          na_str = "<Missing>") %>% rtables::split_rows_by("AEDECOD", 
      child_labels = "visible", nested = TRUE, indent_mod = -1L, 
      split_fun = split_fun("AESEV"), label_pos = "topleft", split_label = teal.data::col_labels(adae["AEDECOD"])) %>% 
      summarize_num_patients(var = "", .stats = "unique", .labels = c("- Any Intensity -"), 
          na_str = "<Missing>") %>% count_occurrences_by_grade(var = "AESEV", 
      .indent_mods = -1L, na_str = "<Missing>") %>% append_varlabels(adae, 
      "AESEV", indent = 2L)

  $table
  result <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

  $prune
  {
      pruned_result <- result
  }

  $sort
  {
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = "AEBODSYS", 
          scorefun = cont_n_onecol(length(levels(adsl$ACTARM)) + 
              1), decreasing = TRUE) %>% sort_at_path(path = c("AEBODSYS", 
          "*", "AEDECOD"), scorefun = cont_n_onecol(length(levels(adsl$ACTARM)) + 
          1), decreasing = TRUE)
  }

template_events_by_grade generates standard expressions with pruning conditions

Code
  res
Output
  $data
  {
      anl <- adae
      anl <- anl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(anl[["ACTARM"]])
      adsl <- adsl %>% dplyr::filter(ACTARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      adae <- df_explicit_na(adae, na_level = "<Missing>")
      anl <- df_explicit_na(anl, na_level = "<Missing>")
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      grade_groups <- list(`- Any Intensity -` = levels(adae$AESEV))
  }

  $layout_prep
  split_fun <- trim_levels_in_group

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Adverse Event summary by Severity/Intensity: Body System or Organ Class and Dictionary-Derived Term") %>% 
      rtables::split_cols_by("ACTARM") %>% rtables::add_overall_col(label = "All Patients") %>% 
      summarize_occurrences_by_grade(var = "AESEV", grade_groups = grade_groups, 
          na_str = "<Missing>") %>% rtables::split_rows_by("AEBODSYS", 
      child_labels = "visible", nested = TRUE, indent_mod = -1L, 
      split_fun = split_fun("AESEV"), label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_occurrences_by_grade(var = "AESEV", grade_groups = grade_groups, 
          na_str = "<Missing>") %>% rtables::split_rows_by("AEDECOD", 
      child_labels = "visible", nested = TRUE, indent_mod = -1L, 
      split_fun = split_fun("AESEV"), label_pos = "topleft", split_label = teal.data::col_labels(adae["AEDECOD"])) %>% 
      summarize_num_patients(var = "", .stats = "unique", .labels = c("- Any Intensity -"), 
          na_str = "<Missing>") %>% count_occurrences_by_grade(var = "AESEV", 
      .indent_mods = -1L, na_str = "<Missing>") %>% append_varlabels(adae, 
      "AESEV", indent = 2L)

  $table
  result <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

  $prune
  {
      pruned_result <- result
      col_indices <- 1:(ncol(result) - TRUE)
      row_condition <- has_fraction_in_any_col(atleast = 0.4, col_indices = col_indices) & 
          has_fractions_difference(atleast = 0.1, col_indices = col_indices)
      pruned_result <- pruned_result %>% rtables::prune_table(keep_content_rows(row_condition))
  }

  $sort
  {
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = "AEBODSYS", 
          scorefun = cont_n_onecol(length(levels(adsl$ACTARM)) + 
              1), decreasing = TRUE) %>% sort_at_path(path = c("AEBODSYS", 
          "*", "AEDECOD"), scorefun = cont_n_onecol(length(levels(adsl$ACTARM)) + 
          1), decreasing = TRUE)
  }

template_events_by_grade without adding total column option works as expected

Code
  res
Output
  $data
  {
      anl <- adae
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(adsl[["ACTARM"]])
      anl <- anl %>% dplyr::mutate(ACTARM = factor(ACTARM, levels = arm_levels))
      adae <- df_explicit_na(adae, na_level = "<Missing>")
      anl <- df_explicit_na(anl, na_level = "<Missing>")
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      grade_groups <- list(`- Any Intensity -` = levels(adae$AESEV))
  }

  $layout_prep
  split_fun <- trim_levels_in_group

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Adverse Event summary by Severity/Intensity: Body System or Organ Class and Dictionary-Derived Term") %>% 
      rtables::split_cols_by("ACTARM") %>% summarize_occurrences_by_grade(var = "AESEV", 
      grade_groups = grade_groups, na_str = "<Missing>") %>% rtables::split_rows_by("AEBODSYS", 
      child_labels = "visible", nested = TRUE, indent_mod = -1L, 
      split_fun = split_fun("AESEV"), label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_occurrences_by_grade(var = "AESEV", grade_groups = grade_groups, 
          na_str = "<Missing>") %>% rtables::split_rows_by("AEDECOD", 
      child_labels = "visible", nested = TRUE, indent_mod = -1L, 
      split_fun = split_fun("AESEV"), label_pos = "topleft", split_label = teal.data::col_labels(adae["AEDECOD"])) %>% 
      summarize_num_patients(var = "", .stats = "unique", .labels = c("- Any Intensity -"), 
          na_str = "<Missing>") %>% count_occurrences_by_grade(var = "AESEV", 
      .indent_mods = -1L, na_str = "<Missing>") %>% append_varlabels(adae, 
      "AESEV", indent = 2L)

  $table
  result <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

  $prune
  {
      pruned_result <- result
  }

  $sort
  {
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = "AEBODSYS", 
          scorefun = cont_n_allcols, decreasing = TRUE) %>% sort_at_path(path = c("AEBODSYS", 
          "*", "AEDECOD"), scorefun = cont_n_allcols, decreasing = TRUE)
  }

template_events_by_grade with hlt only works

Code
  res
Output
  $data
  {
      anl <- adae
      anl <- anl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(anl[["ACTARM"]])
      adsl <- adsl %>% dplyr::filter(ACTARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      adae <- df_explicit_na(adae, na_level = "<Missing>")
      anl <- df_explicit_na(anl, na_level = "<Missing>")
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      grade_groups <- list(`- Any Intensity -` = levels(adae$AESEV))
  }

  $layout_prep
  split_fun <- trim_levels_in_group

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Adverse Event summary by Severity/Intensity: Body System or Organ Class") %>% 
      rtables::split_cols_by("ACTARM") %>% rtables::add_overall_col(label = "All Patients") %>% 
      summarize_occurrences_by_grade(var = "AESEV", grade_groups = grade_groups, 
          na_str = "<Missing>") %>% rtables::split_rows_by("AEBODSYS", 
      child_labels = "visible", nested = TRUE, indent_mod = -1L, 
      split_fun = split_fun("AESEV"), label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_num_patients(var = "", .stats = "unique", .labels = c("- Any Intensity -"), 
          na_str = "<Missing>") %>% count_occurrences_by_grade(var = "AESEV", 
      .indent_mods = -1L, na_str = "<Missing>") %>% append_varlabels(adae, 
      "AESEV", indent = 1L)

  $table
  result <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

  $prune
  {
      pruned_result <- result
  }

  $sort
  {
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = "AEBODSYS", 
          scorefun = cont_n_onecol(length(levels(adsl$ACTARM)) + 
              1), decreasing = TRUE)
  }

template_events_col_by_grade generates standard expressions

Code
  res
Output
  $data
  {
      anl <- adae
      anl <- anl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(anl[["ACTARM"]])
      adsl <- adsl %>% dplyr::filter(ACTARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      col_counts <- rep(c(table(adsl[["ACTARM"]]), nrow(adsl)), 
          each = length(list(`Any Grade (%)` = c("1", "2", "3", 
              "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
              "4"), `Grade 5 (%)` = "5")))
      anl <- anl %>% dplyr::group_by(USUBJID, ACTARM, AEBODSYS, 
          AEDECOD) %>% dplyr::summarize(MAXAETOXGR = factor(max(as.numeric(AETOXGR)))) %>% 
          dplyr::ungroup() %>% df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(title = "Adverse Event summary by Analysis Toxicity Grade: Body System or Organ Class and Dictionary-Derived Term") %>% 
      rtables::split_cols_by(var = "ACTARM", split_fun = add_overall_level("All Patients", 
          first = FALSE)) %>% split_cols_by_groups("MAXAETOXGR", 
      groups = list(`Any Grade (%)` = c("1", "2", "3", "4", "5"), 
          `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
              "4"), `Grade 5 (%)` = "5")) %>% rtables::split_rows_by("AEBODSYS", 
      child_labels = "visible", nested = FALSE, split_fun = trim_levels_in_group("AEDECOD")) %>% 
      append_varlabels(df = anl, vars = "AEBODSYS") %>% summarize_num_patients(var = "USUBJID", 
      .stats = "unique", .labels = "Total number of patients with at least one adverse event", 
      ) %>% analyze_vars("AEDECOD", na.rm = FALSE, denom = "N_col", 
      .stats = "count_fraction", .formats = c(count_fraction = format_fraction_threshold(0.01))) %>% 
      append_varlabels(df = anl, vars = "AEDECOD", indent = 1L)

  $table
  result <- rtables::build_table(lyt = lyt, df = anl, col_counts = col_counts)

  $sort
  {
      lengths <- lapply(list(`Any Grade (%)` = c("1", "2", "3", 
          "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5"), length)
      start_index <- unname(which.max(lengths))
      col_indices <- seq(start_index, ncol(result), by = length(list(`Any Grade (%)` = c("1", 
          "2", "3", "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5")))
      scorefun_soc <- score_occurrences_cont_cols(col_indices = col_indices)
      scorefun_term <- score_occurrences_cols(col_indices = col_indices)
      sorted_result <- result %>% sort_at_path(path = c("AEBODSYS"), 
          scorefun = scorefun_soc, decreasing = TRUE) %>% sort_at_path(path = c("AEBODSYS", 
          "*", "AEDECOD"), scorefun = scorefun_term, decreasing = TRUE)
  }

  $prune
  {
      criteria_fun <- function(tr) {
          inherits(tr, "ContentRow")
      }
      at_least_percent_any <- has_fraction_in_any_col(atleast = 0.1, 
          col_indices = col_indices)
      pruned_and_sorted_result <- sorted_result %>% rtables::trim_rows(criteria = criteria_fun) %>% 
          rtables::prune_table(keep_rows(at_least_percent_any))
  }

template_events_col_by_grade works with custom values

Code
  res
Output
  $data
  {
      anl <- adae
      anl <- anl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(anl[["ACTARM"]])
      adsl <- adsl %>% dplyr::filter(ACTARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      col_counts <- rep(c(table(adsl[["ACTARM"]]), nrow(adsl)), 
          each = length(list(`Any Grade (%)` = c("1", "2", "3", 
              "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
              "4"), `Grade 5 (%)` = "5")))
      anl <- anl %>% dplyr::group_by(USUBJID, ACTARM, AEDECOD) %>% 
          dplyr::summarize(MAXAETOXGR = factor(max(as.numeric(AETOXGR)))) %>% 
          dplyr::ungroup() %>% df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(title = "Adverse Event summary by Analysis Toxicity Grade: Dictionary-Derived Term") %>% 
      rtables::split_cols_by(var = "ACTARM", split_fun = add_overall_level("All Patients", 
          first = FALSE)) %>% split_cols_by_groups("MAXAETOXGR", 
      groups = list(`Any Grade (%)` = c("1", "2", "3", "4", "5"), 
          `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
              "4"), `Grade 5 (%)` = "5")) %>% analyze_vars("AEDECOD", 
      na.rm = FALSE, denom = "N_col", .stats = "count_fraction", 
      .formats = c(count_fraction = format_fraction_threshold(0.01))) %>% 
      append_varlabels(df = anl, vars = "AEDECOD")

  $table
  result <- rtables::build_table(lyt = lyt, df = anl, col_counts = col_counts)

  $sort
  {
      lengths <- lapply(list(`Any Grade (%)` = c("1", "2", "3", 
          "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5"), length)
      start_index <- unname(which.max(lengths))
      col_indices <- seq(start_index, ncol(result), by = length(list(`Any Grade (%)` = c("1", 
          "2", "3", "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5")))
      scorefun_term <- score_occurrences_cols(col_indices = col_indices)
      sorted_result <- result %>% sort_at_path(path = c("AEDECOD"), 
          scorefun = scorefun_term, decreasing = TRUE)
  }

  $prune
  {
      criteria_fun <- function(tr) {
          inherits(tr, "ContentRow")
      }
      at_least_percent_any <- has_fraction_in_any_col(atleast = 0.1, 
          col_indices = col_indices)
      pruned_and_sorted_result <- sorted_result %>% rtables::trim_rows(criteria = criteria_fun) %>% 
          rtables::prune_table(keep_rows(at_least_percent_any))
  }

template_events_col_by_grade without adding total column option works as expected

Code
  res
Output
  $data
  {
      anl <- adae
      anl <- anl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(anl[["ACTARM"]])
      adsl <- adsl %>% dplyr::filter(ACTARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      col_counts <- rep(table(adsl[["ACTARM"]]), each = length(list(`Any Grade (%)` = c("1", 
          "2", "3", "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5")))
      anl <- anl %>% dplyr::group_by(USUBJID, ACTARM, AEDECOD) %>% 
          dplyr::summarize(MAXAETOXGR = factor(max(as.numeric(AETOXGR)))) %>% 
          dplyr::ungroup() %>% df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(title = "Adverse Event summary by Analysis Toxicity Grade: Dictionary-Derived Term") %>% 
      rtables::split_cols_by(var = "ACTARM") %>% split_cols_by_groups("MAXAETOXGR", 
      groups = list(`Any Grade (%)` = c("1", "2", "3", "4", "5"), 
          `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
              "4"), `Grade 5 (%)` = "5")) %>% analyze_vars("AEDECOD", 
      na.rm = FALSE, denom = "N_col", .stats = "count_fraction", 
      .formats = c(count_fraction = format_fraction_threshold(0.01))) %>% 
      append_varlabels(df = anl, vars = "AEDECOD")

  $table
  result <- rtables::build_table(lyt = lyt, df = anl, col_counts = col_counts)

  $sort
  {
      lengths <- lapply(list(`Any Grade (%)` = c("1", "2", "3", 
          "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5"), length)
      start_index <- unname(which.max(lengths))
      col_indices <- seq(start_index, ncol(result), by = length(list(`Any Grade (%)` = c("1", 
          "2", "3", "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5")))
      scorefun_term <- score_occurrences_cols(col_indices = col_indices)
      sorted_result <- result %>% sort_at_path(path = c("AEDECOD"), 
          scorefun = scorefun_term, decreasing = TRUE)
  }

  $prune
  {
      criteria_fun <- function(tr) {
          inherits(tr, "ContentRow")
      }
      at_least_percent_any <- has_fraction_in_any_col(atleast = 0.1, 
          col_indices = col_indices)
      pruned_and_sorted_result <- sorted_result %>% rtables::trim_rows(criteria = criteria_fun) %>% 
          rtables::prune_table(keep_rows(at_least_percent_any))
  }

template_events_col_by_grade without dropping arm levels option works as expected

Code
  res
Output
  $data
  {
      anl <- adae
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(adsl[["ACTARM"]])
      anl <- anl %>% dplyr::mutate(ACTARM = factor(ACTARM, levels = arm_levels))
      col_counts <- rep(table(adsl[["ACTARM"]]), each = length(list(`Any Grade (%)` = c("1", 
          "2", "3", "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5")))
      anl <- anl %>% dplyr::group_by(USUBJID, ACTARM, AEDECOD) %>% 
          dplyr::summarize(MAXAETOXGR = factor(max(as.numeric(AETOXGR)))) %>% 
          dplyr::ungroup() %>% df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(title = "Adverse Event summary by Analysis Toxicity Grade: Dictionary-Derived Term") %>% 
      rtables::split_cols_by(var = "ACTARM") %>% split_cols_by_groups("MAXAETOXGR", 
      groups = list(`Any Grade (%)` = c("1", "2", "3", "4", "5"), 
          `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
              "4"), `Grade 5 (%)` = "5")) %>% analyze_vars("AEDECOD", 
      na.rm = FALSE, denom = "N_col", .stats = "count_fraction", 
      .formats = c(count_fraction = format_fraction_threshold(0.01))) %>% 
      append_varlabels(df = anl, vars = "AEDECOD")

  $table
  result <- rtables::build_table(lyt = lyt, df = anl, col_counts = col_counts)

  $sort
  {
      lengths <- lapply(list(`Any Grade (%)` = c("1", "2", "3", 
          "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5"), length)
      start_index <- unname(which.max(lengths))
      col_indices <- seq(start_index, ncol(result), by = length(list(`Any Grade (%)` = c("1", 
          "2", "3", "4", "5"), `Grade 1-2 (%)` = c("1", "2"), `Grade 3-4 (%)` = c("3", 
          "4"), `Grade 5 (%)` = "5")))
      scorefun_term <- score_occurrences_cols(col_indices = col_indices)
      sorted_result <- result %>% sort_at_path(path = c("AEDECOD"), 
          scorefun = scorefun_term, decreasing = TRUE)
  }

  $prune
  {
      criteria_fun <- function(tr) {
          inherits(tr, "ContentRow")
      }
      at_least_percent_any <- has_fraction_in_any_col(atleast = 0.1, 
          col_indices = col_indices)
      pruned_and_sorted_result <- sorted_result %>% rtables::trim_rows(criteria = criteria_fun) %>% 
          rtables::prune_table(keep_rows(at_least_percent_any))
  }

teal.modules.clinical documentation built on April 4, 2025, 12:35 a.m.