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

template_binary_outcome generates standard expressions

Code
  res
Output
  $data
  {
      anl <- adrs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% dplyr::mutate(ARMCD = droplevels(ARMCD)) %>% 
          dplyr::mutate(is_rsp = AVALC %in% c("Complete Response (CR)", 
              "Partial Response (PR)")) %>% dplyr::mutate(AVALC = factor(AVALC, 
          levels = c("Complete Response (CR)", "Partial Response (PR)")))
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% dplyr::mutate(ARMCD = droplevels(ARMCD)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Table of BESRSPI for Complete Response (CR) and Partial Response (PR) Responders", 
      subtitles = "") %>% rtables::split_cols_by(var = "ARMCD", 
      ref_group = "ARM A") %>% estimate_proportion(vars = "is_rsp", 
      conf_level = 0.95, method = "waldcc", table_names = "prop_est") %>% 
      estimate_proportion_diff(vars = "is_rsp", show_labels = "visible", 
          var_labels = "Unstratified Analysis", conf_level = 0.95, 
          method = "waldcc", table_names = "u_prop_diff") %>% test_proportion_diff(vars = "is_rsp", 
      method = "schouten", table_names = "u_test_diff") %>% estimate_odds_ratio(vars = "is_rsp", 
      conf_level = 0.95, table_names = "u_est_or") %>% estimate_multinomial_response(var = "AVALC", 
      conf_level = 0.95, method = "waldcc")

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

template_binary_outcome generates right expressions with non-default

Code
  res
Output
  $data
  {
      anl <- ADRS %>% dplyr::filter(ARM %in% c("B: Placebo", "A: Drug X", 
          "C: Combination")) %>% dplyr::mutate(ARM = stats::relevel(ARM, 
          ref = "B: Placebo")) %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          dplyr::mutate(is_rsp = AVALC %in% c("PR", "SD")) %>% 
          dplyr::mutate(AVALC = factor(AVALC, levels = c("PR", 
              "SD")))
      ADSL <- ADSL %>% dplyr::filter(ARM %in% c("B: Placebo", "A: Drug X", 
          "C: Combination")) %>% dplyr::mutate(ARM = stats::relevel(ARM, 
          ref = "B: Placebo")) %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Table of BESRSPI for PR and SD Responders", 
      subtitles = "") %>% rtables::split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% 
      estimate_proportion(vars = "is_rsp", conf_level = 0.95, method = "waldcc", 
          table_names = "prop_est") %>% estimate_proportion_diff(vars = "is_rsp", 
      show_labels = "visible", var_labels = "Unstratified Analysis", 
      conf_level = 0.95, method = "waldcc", table_names = "u_prop_diff") %>% 
      test_proportion_diff(vars = "is_rsp", method = "schouten", 
          table_names = "u_test_diff") %>% estimate_odds_ratio(vars = "is_rsp", 
      conf_level = 0.95, table_names = "u_est_or")

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = ADSL)
  }

template_binary_outcome generates expression without arm comparison

Code
  res
Output
  $data
  {
      anl <- ADRS %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          dplyr::mutate(is_rsp = AVALC %in% c("Complete Response (CR)", 
              "Partial Response (PR)")) %>% dplyr::mutate(AVALC = factor(AVALC, 
          levels = c("Complete Response (CR)", "Partial Response (PR)")))
      ADSL <- ADSL %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Table of BESRSPI for Complete Response (CR) and Partial Response (PR) Responders", 
      subtitles = "") %>% rtables::split_cols_by(var = "ARM") %>% 
      estimate_proportion(vars = "is_rsp", conf_level = 0.95, method = "waldcc", 
          table_names = "prop_est")

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = ADSL)
  }

template_binary_outcome generates expression with non-default controls and strata.

Code
  res
Output
  $data
  {
      anl <- ADRS %>% dplyr::filter(ARM %in% c("B: Placebo", "A: Drug X", 
          "C: Combination")) %>% dplyr::mutate(ARM = stats::relevel(ARM, 
          ref = "B: Placebo")) %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          dplyr::mutate(is_rsp = AVALC %in% c("Complete Response (CR)", 
              "Partial Response (PR)")) %>% dplyr::mutate(AVALC = factor(AVALC, 
          levels = c("Complete Response (CR)", "Partial Response (PR)")))
      ADSL <- ADSL %>% dplyr::filter(ARM %in% c("B: Placebo", "A: Drug X", 
          "C: Combination")) %>% dplyr::mutate(ARM = stats::relevel(ARM, 
          ref = "B: Placebo")) %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Table of BESRSPI for Complete Response (CR) and Partial Response (PR) Responders", 
      subtitles = "Stratified by SEX") %>% rtables::split_cols_by(var = "ARM", 
      ref_group = "B: Placebo") %>% estimate_proportion(vars = "is_rsp", 
      conf_level = 0.8, method = "jeffreys", table_names = "prop_est") %>% 
      estimate_proportion_diff(vars = "is_rsp", show_labels = "visible", 
          var_labels = "Unstratified Analysis", conf_level = 0.8, 
          method = "ha", table_names = "u_prop_diff") %>% test_proportion_diff(vars = "is_rsp", 
      method = "chisq", table_names = "u_test_diff") %>% estimate_odds_ratio(vars = "is_rsp", 
      conf_level = 0.8, table_names = "u_est_or") %>% estimate_proportion_diff(vars = "is_rsp", 
      show_labels = "visible", var_labels = "Stratified Analysis", 
      variables = list(strata = "SEX"), conf_level = 0.8, method = "cmh", 
      table_names = "s_prop_diff") %>% test_proportion_diff(vars = "is_rsp", 
      method = "cmh", variables = list(strata = "SEX"), table_names = "s_test_diff") %>% 
      estimate_odds_ratio(vars = "is_rsp", variables = list(arm = "ARM", 
          strata = "SEX"), conf_level = 0.8, table_names = "s_est_or") %>% 
      estimate_multinomial_response(var = "AVALC", conf_level = 0.8, 
          method = "jeffreys")

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = ADSL)
  }

template_binary_outcome can combine comparison arms

Code
  res
Output
  $data
  {
      anl <- adrs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% dplyr::mutate(ARMCD = droplevels(ARMCD)) %>% 
          dplyr::mutate(is_rsp = AVALC %in% c("Complete Response (CR)", 
              "Partial Response (PR)")) %>% dplyr::mutate(AVALC = factor(AVALC, 
          levels = c("Complete Response (CR)", "Partial Response (PR)")))
      ADSL <- ADSL %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% dplyr::mutate(ARMCD = droplevels(ARMCD)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $combine_comp_arms
  groups <- combine_groups(fct = ADSL[["ARMCD"]], ref = "ARM A")

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Table of BESRSPI for Complete Response (CR) and Partial Response (PR) Responders", 
      subtitles = "") %>% split_cols_by_groups(var = "ARMCD", groups_list = groups, 
      ref_group = names(groups)[1]) %>% estimate_proportion(vars = "is_rsp", 
      conf_level = 0.95, method = "waldcc", table_names = "prop_est") %>% 
      estimate_proportion_diff(vars = "is_rsp", show_labels = "visible", 
          var_labels = "Unstratified Analysis", conf_level = 0.95, 
          method = "waldcc", table_names = "u_prop_diff") %>% test_proportion_diff(vars = "is_rsp", 
      method = "schouten", table_names = "u_test_diff") %>% estimate_odds_ratio(vars = "is_rsp", 
      conf_level = 0.95, table_names = "u_est_or") %>% estimate_multinomial_response(var = "AVALC", 
      conf_level = 0.95, method = "waldcc")

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = ADSL)
  }

template_binary_outcome can combine comparison arms when compare arms is FALSE

Code
  res
Output
  $data
  {
      anl <- adrs %>% dplyr::mutate(ARMCD = droplevels(ARMCD)) %>% 
          dplyr::mutate(is_rsp = AVALC %in% c("Complete Response (CR)", 
              "Partial Response (PR)")) %>% dplyr::mutate(AVALC = factor(AVALC, 
          levels = c("Complete Response (CR)", "Partial Response (PR)")))
      ADSL <- ADSL %>% dplyr::mutate(ARMCD = droplevels(ARMCD)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Table of BESRSPI for Complete Response (CR) and Partial Response (PR) Responders", 
      subtitles = "") %>% rtables::split_cols_by(var = "ARMCD") %>% 
      estimate_proportion(vars = "is_rsp", conf_level = 0.95, method = "waldcc", 
          table_names = "prop_est") %>% estimate_multinomial_response(var = "AVALC", 
      conf_level = 0.95, method = "waldcc")

  $table
  {
      table <- rtables::build_table(lyt = lyt, df = anl, alt_counts_df = ADSL)
  }

split_col_expr prepare the right four possible expressions

Code
  res
Output
  [[1]]
  split_cols_by_groups(var = "ARMCD", groups_list = groups, ref_group = names(groups)[1])

  [[2]]
  rtables::split_cols_by(var = "ARMCD", ref_group = "ARM C")

  [[3]]
  rtables::split_cols_by(var = "ARMCD")

  [[4]]
  rtables::split_cols_by(var = "ARMCD")

template_binary_outcome can combine refs

Code
  res
Output
  $data
  {
      anl <- adrs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = combine_levels(ARMCD, 
          levels = c("ARM A", "ARM B"), new_level = "ARM A/ARM B")) %>% 
          dplyr::mutate(ARMCD = stats::relevel(ARMCD, ref = "ARM A/ARM B")) %>% 
          dplyr::mutate(ARMCD = droplevels(ARMCD)) %>% dplyr::mutate(is_rsp = AVALC %in% 
          c("Complete Response (CR)", "Partial Response (PR)")) %>% 
          dplyr::mutate(AVALC = factor(AVALC, levels = c("Complete Response (CR)", 
              "Partial Response (PR)")))
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = combine_levels(ARMCD, 
          levels = c("ARM A", "ARM B"), new_level = "ARM A/ARM B")) %>% 
          dplyr::mutate(ARMCD = stats::relevel(ARMCD, ref = "ARM A/ARM B")) %>% 
          dplyr::mutate(ARMCD = droplevels(ARMCD)) %>% df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Table of BESRSPI for Complete Response (CR) and Partial Response (PR) Responders", 
      subtitles = "") %>% rtables::split_cols_by(var = "ARMCD", 
      ref_group = "ARM A/ARM B") %>% estimate_proportion(vars = "is_rsp", 
      conf_level = 0.95, method = "waldcc", table_names = "prop_est") %>% 
      estimate_proportion_diff(vars = "is_rsp", show_labels = "visible", 
          var_labels = "Unstratified Analysis", conf_level = 0.95, 
          method = "waldcc", table_names = "u_prop_diff") %>% test_proportion_diff(vars = "is_rsp", 
      method = "schouten", table_names = "u_test_diff") %>% estimate_odds_ratio(vars = "is_rsp", 
      conf_level = 0.95, table_names = "u_est_or") %>% estimate_multinomial_response(var = "AVALC", 
      conf_level = 0.95, method = "waldcc")

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