tests/testthat/_snaps/tm_t_summary.md

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

template_summary generates correct expressions

Code
  res
Output
  $data
  {
      anl <- adrs %>% df_explicit_na(omit_columns = setdiff(names(adrs), 
          c(c("RACE", "COUNTRY", "AGE"))), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, main_footer = "n represents the number of unique subject IDs such that the variable has non-NA values.") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      analyze_vars(vars = c("RACE", "COUNTRY", "AGE"), show_labels = "visible", 
          na.rm = FALSE, na_str = "<Missing>", denom = "N_col", 
          .stats = c("n", "mean_sd", "mean_ci", "median", "median_ci", 
              "quantiles", "range", "geom_mean", "count_fraction"))

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

template_summary can generate customized table

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

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, main_footer = "n represents the number of unique subject IDs such that the variable has non-NA values.") %>% 
      rtables::split_cols_by("ARMCD") %>% rtables::add_overall_col("All Patients") %>% 
      analyze_vars(vars = "RACE", var_labels = c(RACE = "Race"), 
          show_labels = "visible", na.rm = TRUE, na_str = "<Missing>", 
          denom = "N_col", .stats = c("n", "mean_sd", "mean_ci", 
              "median", "median_ci", "quantiles", "range", "geom_mean", 
              "count"))

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

template_summary generates correct expressions for multiple grouping variables

Code
  res
Output
  $data
  {
      anl <- adrs %>% df_explicit_na(omit_columns = setdiff(names(adrs), 
          c(c("RACE", "COUNTRY", "AGE"))), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      anl <- anl %>% dplyr::mutate(STRATA1 = droplevels(STRATA1))
      arm_levels <- levels(anl[["STRATA1"]])
      adsl <- adsl %>% dplyr::filter(STRATA1 %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(STRATA1 = droplevels(STRATA1))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, main_footer = "n represents the number of unique subject IDs such that the variable has non-NA values.") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      rtables::split_cols_by("STRATA1", split_fun = drop_split_levels) %>% 
      analyze_vars(vars = c("RACE", "COUNTRY", "AGE"), show_labels = "visible", 
          na.rm = FALSE, na_str = "<Missing>", denom = "N_col", 
          .stats = c("n", "mean_sd", "mean_ci", "median", "median_ci", 
              "quantiles", "range", "geom_mean", "count_fraction"))

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

template_summary generates correct expressions for multiple grouping variables and all patients

Code
  res
Output
  $data
  {
      anl <- adrs %>% df_explicit_na(omit_columns = setdiff(names(adrs), 
          c(c("RACE", "COUNTRY", "AGE"))), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      anl <- anl %>% dplyr::mutate(STRATA1 = droplevels(STRATA1))
      arm_levels <- levels(anl[["STRATA1"]])
      adsl <- adsl %>% dplyr::filter(STRATA1 %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(STRATA1 = droplevels(STRATA1))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, main_footer = "n represents the number of unique subject IDs such that the variable has non-NA values.") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      rtables::split_cols_by("STRATA1", split_fun = drop_split_levels) %>% 
      rtables::add_overall_col("All Patients") %>% analyze_vars(vars = c("RACE", 
      "COUNTRY", "AGE"), show_labels = "visible", na.rm = FALSE, 
      na_str = "<Missing>", denom = "N_col", .stats = c("n", "mean_sd", 
          "mean_ci", "median", "median_ci", "quantiles", "range", 
          "geom_mean", "count_fraction"))

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

template_summary generates correct expressions for customized numeric statistics

Code
  res
Output
  $data
  {
      anl <- adrs %>% df_explicit_na(omit_columns = setdiff(names(adrs), 
          c(c("RACE", "COUNTRY", "AGE"))), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      anl <- anl %>% dplyr::mutate(STRATA1 = droplevels(STRATA1))
      arm_levels <- levels(anl[["STRATA1"]])
      adsl <- adsl %>% dplyr::filter(STRATA1 %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(STRATA1 = droplevels(STRATA1))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, main_footer = "n represents the number of unique subject IDs such that the variable has non-NA values.") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      rtables::split_cols_by("STRATA1", split_fun = drop_split_levels) %>% 
      analyze_vars(vars = c("RACE", "COUNTRY", "AGE"), show_labels = "visible", 
          na.rm = FALSE, na_str = "<Missing>", denom = "N_col", 
          .stats = c("n", "count_fraction"))

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

template_summary generates correct expressions when arm variable labels are added

Code
  res
Output
  $data
  {
      anl <- adrs %>% df_explicit_na(omit_columns = setdiff(names(adrs), 
          c(c("RACE", "COUNTRY", "AGE"))), na_level = "<Missing>")
      anl <- anl %>% dplyr::mutate(ARM = droplevels(ARM))
      arm_levels <- levels(anl[["ARM"]])
      adsl <- adsl %>% dplyr::filter(ARM %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(ARM = droplevels(ARM))
      anl <- anl %>% dplyr::mutate(SEX = droplevels(SEX))
      arm_levels <- levels(anl[["SEX"]])
      adsl <- adsl %>% dplyr::filter(SEX %in% arm_levels)
      adsl <- adsl %>% dplyr::mutate(SEX = droplevels(SEX))
      adsl <- df_explicit_na(adsl, na_level = "<Missing>")
  }

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, main_footer = "n represents the number of unique subject IDs such that the variable has non-NA values.") %>% 
      rtables::split_cols_by("ARM", split_fun = drop_split_levels) %>% 
      rtables::split_cols_by("SEX", split_fun = drop_split_levels) %>% 
      rtables::add_overall_col("All Patients") %>% analyze_vars(vars = c("RACE", 
      "COUNTRY", "AGE"), show_labels = "visible", na.rm = FALSE, 
      na_str = "<Missing>", denom = "N_col", .stats = c("n", "mean_sd", 
          "mean_ci", "median", "median_ci", "quantiles", "range", 
          "geom_mean", "count_fraction")) %>% append_topleft(c("Arm", 
      "Sex", ""))

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

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