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

template_tte produces healthy standard output

Code
  res
Output
  $data
  {
      anl <- ANL %>% dplyr::mutate(ARM = droplevels(ARM)) %>% dplyr::mutate(is_event = CNSR == 
          0, is_not_event = CNSR == 1, EVNT1 = factor(dplyr::case_when(is_event == 
          TRUE ~ "Patients with event (%)", is_event == FALSE ~ 
          "Patients without event (%)"), levels = c("Patients with event (%)", 
          "Patients without event (%)")), EVNTDESC = factor(EVNTDESC)) %>% 
          df_explicit_na(na_level = "<Missing>")
      ANL_ADSL <- ANL_ADSL %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Time-To-Event Table for OS", 
      main_footer = "Confidence Level Type for Survfit: plain") %>% 
      rtables::split_cols_by(var = "ARM") %>% analyze_vars("is_event", 
      .stats = "count_fraction", .labels = c(count_fraction = "Patients with event (%)"), 
      na_str = "<Missing>") %>% rtables::split_rows_by("EVNT1", 
      split_label = "Earliest contributing event", split_fun = keep_split_levels("Patients with event (%)"), 
      label_pos = "visible", child_labels = "hidden", indent_mod = 1L, 
      ) %>% rtables::split_rows_by("EVNTDESC", split_fun = drop_split_levels) %>% 
      rtables::summarize_row_groups(format = "xx", na_str = "<Missing>") %>% 
      analyze_vars("is_not_event", .stats = "count_fraction", .labels = c(count_fraction = "Patients without event (%)"), 
          nested = FALSE, show_labels = "hidden", na_str = "<Missing>") %>% 
      surv_time(vars = "AVAL", var_labels = paste0("Time to Event (", 
          as.character(anl$AVALU[1]), ")"), is_event = "is_event", 
          control = list(conf_level = 0.95, conf_type = "plain", 
              quantiles = c(0.25, 0.75)), na_str = "<Missing>", 
          table_names = "time_to_event") %>% surv_timepoint(vars = "AVAL", 
      var_labels = as.character(anl$AVALU[1]), is_event = "is_event", 
      time_point = c(183, 365, 548), method = "surv", control = control_surv_timepoint(conf_level = 0.95, 
          conf_type = "plain"), .indent_mods = NULL, na_str = "<Missing>")

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

template_tte produces correct data expression when not comparing arms

Code
  res
Output
  $data
  {
      anl <- ANL %>% dplyr::mutate(ARM = droplevels(ARM)) %>% dplyr::mutate(is_event = CNSR == 
          0, is_not_event = CNSR == 1, EVNT1 = factor(dplyr::case_when(is_event == 
          TRUE ~ "Patients with event (%)", is_event == FALSE ~ 
          "Patients without event (%)"), levels = c("Patients with event (%)", 
          "Patients without event (%)")), EVNTDESC = factor(EVNTDESC)) %>% 
          df_explicit_na(na_level = "<Missing>")
      ANL_ADSL <- ANL_ADSL %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Time-To-Event Table for OS", 
      main_footer = "Confidence Level Type for Survfit: plain") %>% 
      rtables::split_cols_by(var = "ARM") %>% analyze_vars("is_event", 
      .stats = "count_fraction", .labels = c(count_fraction = "Patients with event (%)"), 
      na_str = "<Missing>") %>% rtables::split_rows_by("EVNT1", 
      split_label = "Earliest contributing event", split_fun = keep_split_levels("Patients with event (%)"), 
      label_pos = "visible", child_labels = "hidden", indent_mod = 1L, 
      ) %>% rtables::split_rows_by("EVNTDESC", split_fun = drop_split_levels) %>% 
      rtables::summarize_row_groups(format = "xx", na_str = "<Missing>") %>% 
      analyze_vars("is_not_event", .stats = "count_fraction", .labels = c(count_fraction = "Patients without event (%)"), 
          nested = FALSE, show_labels = "hidden", na_str = "<Missing>") %>% 
      surv_time(vars = "AVAL", var_labels = paste0("Time to Event (", 
          as.character(anl$AVALU[1]), ")"), is_event = "is_event", 
          control = list(conf_level = 0.95, conf_type = "plain", 
              quantiles = c(0.25, 0.75)), na_str = "<Missing>", 
          table_names = "time_to_event") %>% surv_timepoint(vars = "AVAL", 
      var_labels = as.character(anl$AVALU[1]), is_event = "is_event", 
      time_point = c(183, 365, 548), method = "surv", control = control_surv_timepoint(conf_level = 0.95, 
          conf_type = "plain"), .indent_mods = NULL, na_str = "<Missing>")

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

template_tte produces correct data expression when comparing and combining arms

Code
  res
Output
  $data
  {
      anl <- ANL %>% dplyr::filter(ARM %in% c("A: Drug X", "B: Placebo", 
          "C: Combination")) %>% dplyr::mutate(ARM = stats::relevel(ARM, 
          ref = "")) %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          dplyr::mutate(is_event = CNSR == 0, is_not_event = CNSR == 
              1, EVNT1 = factor(dplyr::case_when(is_event == TRUE ~ 
              "Patients with event (%)", is_event == FALSE ~ "Patients without event (%)"), 
              levels = c("Patients with event (%)", "Patients without event (%)")), 
              EVNTDESC = factor(EVNTDESC)) %>% df_explicit_na(na_level = "<Missing>")
      ANL_ADSL <- ANL_ADSL %>% dplyr::filter(ARM %in% c("A: Drug X", 
          "B: Placebo", "C: Combination")) %>% dplyr::mutate(ARM = stats::relevel(ARM, 
          ref = "")) %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

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

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Time-To-Event Table for OS", 
      main_footer = c("p-value method for Coxph (Hazard Ratio): log-rank", 
          "Ties for Coxph (Hazard Ratio): efron", "Confidence Level Type for Survfit: plain")) %>% 
      split_cols_by_groups(var = "ARM", groups_list = groups, ref_group = names(groups)[1]) %>% 
      analyze_vars("is_event", .stats = "count_fraction", .labels = c(count_fraction = "Patients with event (%)"), 
          na_str = "<Missing>") %>% rtables::split_rows_by("EVNT1", 
      split_label = "Earliest contributing event", split_fun = keep_split_levels("Patients with event (%)"), 
      label_pos = "visible", child_labels = "hidden", indent_mod = 1L, 
      ) %>% rtables::split_rows_by("EVNTDESC", split_fun = drop_split_levels) %>% 
      rtables::summarize_row_groups(format = "xx", na_str = "<Missing>") %>% 
      analyze_vars("is_not_event", .stats = "count_fraction", .labels = c(count_fraction = "Patients without event (%)"), 
          nested = FALSE, show_labels = "hidden", na_str = "<Missing>") %>% 
      surv_time(vars = "AVAL", var_labels = paste0("Time to Event (", 
          as.character(anl$AVALU[1]), ")"), is_event = "is_event", 
          control = list(conf_level = 0.95, conf_type = "plain", 
              quantiles = c(0.25, 0.75)), na_str = "<Missing>", 
          table_names = "time_to_event") %>% coxph_pairwise(vars = "AVAL", 
      is_event = "is_event", var_labels = c("Unstratified Analysis"), 
      control = list(pval_method = "log-rank", ties = "efron", 
          conf_level = 0.95), na_str = "<Missing>", table_names = "unstratified") %>% 
      surv_timepoint(vars = "AVAL", var_labels = as.character(anl$AVALU[1]), 
          is_event = "is_event", time_point = c(183, 365, 548), 
          method = "both", control = control_surv_timepoint(conf_level = 0.95, 
              conf_type = "plain"), .indent_mods = c(pt_at_risk = 0L, 
              event_free_rate = 0L, rate_ci = 0L, rate_diff = 1L, 
              rate_diff_ci = 1L, ztest_pval = 1L), na_str = "<Missing>")

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

template_tte produces correct data expression when comparing arms

Code
  res
Output
  $data
  {
      anl <- ANL %>% dplyr::filter(ARM %in% c("A: Drug X", "B: Placebo", 
          "C: Combination")) %>% dplyr::mutate(ARM = stats::relevel(ARM, 
          ref = "")) %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          dplyr::mutate(is_event = CNSR == 0, is_not_event = CNSR == 
              1, EVNT1 = factor(dplyr::case_when(is_event == TRUE ~ 
              "Patients with event (%)", is_event == FALSE ~ "Patients without event (%)"), 
              levels = c("Patients with event (%)", "Patients without event (%)")), 
              EVNTDESC = factor(EVNTDESC)) %>% df_explicit_na(na_level = "<Missing>")
      ANL_ADSL <- ANL_ADSL %>% dplyr::filter(ARM %in% c("A: Drug X", 
          "B: Placebo", "C: Combination")) %>% dplyr::mutate(ARM = stats::relevel(ARM, 
          ref = "")) %>% dplyr::mutate(ARM = droplevels(ARM)) %>% 
          df_explicit_na(na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Time-To-Event Table for OS", 
      main_footer = c("p-value method for Coxph (Hazard Ratio): log-rank", 
          "Ties for Coxph (Hazard Ratio): efron", "Confidence Level Type for Survfit: plain")) %>% 
      rtables::split_cols_by(var = "ARM", ref_group = "") %>% analyze_vars("is_event", 
      .stats = "count_fraction", .labels = c(count_fraction = "Patients with event (%)"), 
      na_str = "<Missing>") %>% rtables::split_rows_by("EVNT1", 
      split_label = "Earliest contributing event", split_fun = keep_split_levels("Patients with event (%)"), 
      label_pos = "visible", child_labels = "hidden", indent_mod = 1L, 
      ) %>% rtables::split_rows_by("EVNTDESC", split_fun = drop_split_levels) %>% 
      rtables::summarize_row_groups(format = "xx", na_str = "<Missing>") %>% 
      analyze_vars("is_not_event", .stats = "count_fraction", .labels = c(count_fraction = "Patients without event (%)"), 
          nested = FALSE, show_labels = "hidden", na_str = "<Missing>") %>% 
      surv_time(vars = "AVAL", var_labels = paste0("Time to Event (", 
          as.character(anl$AVALU[1]), ")"), is_event = "is_event", 
          control = list(conf_level = 0.95, conf_type = "plain", 
              quantiles = c(0.25, 0.75)), na_str = "<Missing>", 
          table_names = "time_to_event") %>% coxph_pairwise(vars = "AVAL", 
      is_event = "is_event", var_labels = c("Unstratified Analysis"), 
      control = list(pval_method = "log-rank", ties = "efron", 
          conf_level = 0.95), na_str = "<Missing>", table_names = "unstratified") %>% 
      surv_timepoint(vars = "AVAL", var_labels = as.character(anl$AVALU[1]), 
          is_event = "is_event", time_point = c(183, 365, 548), 
          method = "both", control = control_surv_timepoint(conf_level = 0.95, 
              conf_type = "plain"), .indent_mods = c(pt_at_risk = 0L, 
              event_free_rate = 0L, rate_ci = 0L, rate_diff = 1L, 
              rate_diff_ci = 1L, ztest_pval = 1L), na_str = "<Missing>")

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