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

template_events generates correct 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))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      anl <- anl %>% df_explicit_na(omit_columns = setdiff(names(anl), 
          c("AEBODSYS", "AEDECOD")))
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Event Summary by Term : Body System and Adverse Event Code") %>% 
      rtables::split_cols_by(var = "ACTARM") %>% rtables::add_overall_col(label = "All Patients") %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      rtables::split_rows_by("AEBODSYS", child_labels = "visible", 
          nested = FALSE, indent_mod = -1L, split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      count_occurrences(vars = "AEDECOD", .indent_mods = c(count_fraction = 1L)) %>% 
      append_varlabels(adae, "AEDECOD", indent = 1L)

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

  $prune
  {
      pruned_result <- rtables::prune_table(table)
  }

  $sort
  {
      idx_split_col <- which(sapply(col_paths(table), tail, 1) == 
          "All Patients")
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = c("AEBODSYS"), 
          scorefun = cont_n_onecol(idx_split_col)) %>% sort_at_path(path = c("AEBODSYS", 
          "*", "AEDECOD"), scorefun = score_occurrences_cols(col_indices = seq(1, 
          ncol(table))))
      pruned_and_sorted_result
  }

template_events generates correct expressions for nested columns

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))
      anl <- anl %>% dplyr::mutate(ACTARMCD = droplevels(ACTARMCD))
      arm_levels <- levels(anl[["ACTARMCD"]])
      adsl <- adsl %>% dplyr::filter(ACTARMCD %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ACTARMCD = droplevels(ACTARMCD))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      anl <- anl %>% df_explicit_na(omit_columns = setdiff(names(anl), 
          c("AEBODSYS", "AEDECOD")))
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Event Summary by Term : Body System and Adverse Event Code") %>% 
      rtables::split_cols_by(var = "ACTARM") %>% rtables::split_cols_by("ACTARMCD", 
      split_fun = drop_split_levels) %>% rtables::add_overall_col(label = "All Patients") %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      rtables::split_rows_by("AEBODSYS", child_labels = "visible", 
          nested = FALSE, indent_mod = -1L, split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      count_occurrences(vars = "AEDECOD", .indent_mods = c(count_fraction = 1L)) %>% 
      append_varlabels(adae, "AEDECOD", indent = 1L)

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

  $prune
  {
      pruned_result <- rtables::prune_table(table)
  }

  $sort
  {
      idx_split_col <- which(sapply(col_paths(table), tail, 1) == 
          "All Patients")
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = c("AEBODSYS"), 
          scorefun = cont_n_onecol(idx_split_col)) %>% sort_at_path(path = c("AEBODSYS", 
          "*", "AEDECOD"), scorefun = score_occurrences_cols(col_indices = seq(1, 
          ncol(table))))
      pruned_and_sorted_result
  }

template_events can generate customized table

Code
  res
Output
  $data
  {
      anl <- adcm
      adsl <- adsl %>% dplyr::mutate(ACTARM = droplevels(ACTARM))
      arm_levels <- levels(adsl[["ACTARM"]])
      anl <- anl %>% dplyr::mutate(ACTARM = factor(ACTARM, levels = arm_levels))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      anl <- anl %>% df_explicit_na(omit_columns = setdiff(names(anl), 
          "CMDECOD"))
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Event Summary by Term : Con Med Code") %>% 
      rtables::split_cols_by(var = "ACTARM") %>% summarize_num_patients(var = "USUBJID", 
      .stats = c("unique", "nonunique"), .labels = c(unique = "Total number of patients with at least one treatment", 
          nonunique = "Overall total number of treatments"), na_str = "<Missing>") %>% 
      count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>% 
      append_varlabels(adcm, "CMDECOD")

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

  $prune
  {
      pruned_result <- rtables::prune_table(table)
  }

  $sort
  {
      idx_split_col <- which(sapply(col_paths(table), tail, 1) == 
          "All Patients")
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = c("CMDECOD"), 
          scorefun = score_occurrences)
      pruned_and_sorted_result
  }

template_events can generate customized table with alphabetical sorting

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))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      anl[["AEBODSYS"]] <- as.character(anl[["AEBODSYS"]])
      anl[["AEDECOD"]] <- as.character(anl[["AEDECOD"]])
      anl <- anl %>% df_explicit_na(omit_columns = setdiff(names(anl), 
          c("AEBODSYS", "AEDECOD")))
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Event Summary by Term : Body System and Adverse Event Code") %>% 
      rtables::split_cols_by(var = "ACTARM") %>% rtables::add_overall_col(label = "All Patients") %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      rtables::split_rows_by("AEBODSYS", child_labels = "visible", 
          nested = FALSE, indent_mod = -1L, split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      count_occurrences(vars = "AEDECOD", .indent_mods = c(count_fraction = 1L)) %>% 
      append_varlabels(adae, "AEDECOD", indent = 1L)

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

  $prune
  {
      pruned_result <- rtables::prune_table(table)
  }

  $sort
  {
      pruned_and_sorted_result <- pruned_result
      pruned_and_sorted_result
  }

template_events can generate customized table with pruning

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))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      anl <- anl %>% df_explicit_na(omit_columns = setdiff(names(anl), 
          c("AEBODSYS", "AEDECOD")))
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Event Summary by Term : Body System and Adverse Event Code") %>% 
      rtables::split_cols_by(var = "ACTARM") %>% rtables::add_overall_col(label = "All Patients") %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      rtables::split_rows_by("AEBODSYS", child_labels = "visible", 
          nested = FALSE, indent_mod = -1L, split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      count_occurrences(vars = "AEDECOD", .indent_mods = c(count_fraction = 1L)) %>% 
      append_varlabels(adae, "AEDECOD", indent = 1L)

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

  $prune
  {
      pruned_result <- rtables::prune_table(table)
      col_indices <- 1:(ncol(table) - 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_rows(row_condition))
  }

  $sort
  {
      idx_split_col <- which(sapply(col_paths(table), tail, 1) == 
          "All Patients")
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = c("AEBODSYS"), 
          scorefun = cont_n_onecol(idx_split_col)) %>% sort_at_path(path = c("AEBODSYS", 
          "*", "AEDECOD"), scorefun = score_occurrences_cols(col_indices = seq(1, 
          ncol(table))))
      criteria_fun <- function(tr) {
          inherits(tr, "ContentRow")
      }
      pruned_and_sorted_result <- rtables::trim_rows(pruned_and_sorted_result, 
          criteria = criteria_fun)
      pruned_and_sorted_result
  }

template_events can generate customized table with pruning for nested column

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))
      anl <- anl %>% dplyr::mutate(ACTARMCD = droplevels(ACTARMCD))
      arm_levels <- levels(anl[["ACTARMCD"]])
      adsl <- adsl %>% dplyr::filter(ACTARMCD %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ACTARMCD = droplevels(ACTARMCD))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
      anl <- anl %>% df_explicit_na(omit_columns = setdiff(names(anl), 
          c("AEBODSYS", "AEDECOD")))
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Event Summary by Term : Body System and Adverse Event Code") %>% 
      rtables::split_cols_by(var = "ACTARM") %>% rtables::split_cols_by("ACTARMCD", 
      split_fun = drop_split_levels) %>% rtables::add_overall_col(label = "All Patients") %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      rtables::split_rows_by("AEBODSYS", child_labels = "visible", 
          nested = FALSE, indent_mod = -1L, split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adae["AEBODSYS"])) %>% 
      summarize_num_patients(var = "USUBJID", .stats = c("unique", 
          "nonunique"), .labels = c(unique = "Total number of patients with at least one event", 
          nonunique = "Overall total number of events"), na_str = "<Missing>") %>% 
      count_occurrences(vars = "AEDECOD", .indent_mods = c(count_fraction = 1L)) %>% 
      append_varlabels(adae, "AEDECOD", indent = 1L)

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

  $prune
  {
      pruned_result <- rtables::prune_table(table)
      col_indices <- 1:(ncol(table) - 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_rows(row_condition))
  }

  $sort
  {
      idx_split_col <- which(sapply(col_paths(table), tail, 1) == 
          "All Patients")
      pruned_and_sorted_result <- pruned_result %>% sort_at_path(path = c("AEBODSYS"), 
          scorefun = cont_n_onecol(idx_split_col)) %>% sort_at_path(path = c("AEBODSYS", 
          "*", "AEDECOD"), scorefun = score_occurrences_cols(col_indices = seq(1, 
          ncol(table))))
      criteria_fun <- function(tr) {
          inherits(tr, "ContentRow")
      }
      pruned_and_sorted_result <- rtables::trim_rows(pruned_and_sorted_result, 
          criteria = criteria_fun)
      pruned_and_sorted_result
  }