tests/testthat/_snaps/tm_t_ancova.md

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

template_ancova generates expressions with multiple endpoints

Code
  res
Output
  $data
  {
      adqs <- adqs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary of Analysis of Variance for Function/Well-Being (GF1,GF3,GF7) and BFI All Questions at WEEK 1 DAY 8 for Absolute Change from Baseline") %>% 
      rtables::split_cols_by(var = "ARMCD", ref_group = "ARM A") %>% 
      rtables::split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", 
          split_label = teal.data::col_labels(adqs["AVISIT"], fill = TRUE)) %>% 
      rtables::split_rows_by("PARAMCD", split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adqs["PARAMCD"], 
              fill = TRUE)) %>% summarize_ancova(vars = "CHG", 
      variables = list(arm = "ARMCD", covariates = c("BASE", "STRATA1")), 
      conf_level = 0.95, var_labels = "Adjusted mean", show_labels = "hidden", 
      .labels = NULL)

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

Code
  res
Output
  $data
  {
      adqs <- adqs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary of Analysis of Variance for Function/Well-Being (GF1,GF3,GF7) and BFI All Questions at WEEK 1 DAY 8 for Absolute Change from Baseline") %>% 
      rtables::split_cols_by(var = "ARMCD", ref_group = "ARM A") %>% 
      rtables::split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", 
          split_label = teal.data::col_labels(adqs["AVISIT"], fill = TRUE)) %>% 
      rtables::split_rows_by("PARAMCD", split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adqs["PARAMCD"], 
              fill = TRUE)) %>% rtables::append_topleft(paste0("    Interaction Variable: ", 
      "SEX")) %>% summarize_ancova(vars = "CHG", variables = list(arm = "ARMCD", 
      covariates = c("BASE", "STRATA1", "ARMCD*SEX")), conf_level = 0.95, 
      var_labels = paste("Interaction Level:", "M"), show_labels = if (FALSE) "hidden" else "visible", 
      interaction_y = "M", interaction_item = "SEX")

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

template_ancova generates expressions with multiple endpoints with combined comparison arms

Code
  res
Output
  $data
  {
      adqs <- adqs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% dplyr::mutate(ARMCD = combine_levels(ARMCD, 
          levels = c("ARM B", "ARM C"))) %>% df_explicit_na(na_level = default_na_str())
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% dplyr::mutate(ARMCD = combine_levels(ARMCD, 
          levels = c("ARM B", "ARM C"))) %>% df_explicit_na(na_level = default_na_str())
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary of Analysis of Variance for A and B at WEEK 1 DAY 8 for Absolute Change from Baseline") %>% 
      rtables::split_cols_by(var = "ARMCD", ref_group = "ARM A") %>% 
      rtables::split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", 
          split_label = teal.data::col_labels(adqs["AVISIT"], fill = TRUE)) %>% 
      rtables::split_rows_by("PARAMCD", split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adqs["PARAMCD"], 
              fill = TRUE)) %>% summarize_ancova(vars = "CHG", 
      variables = list(arm = "ARMCD", covariates = c("BASE", "STRATA1")), 
      conf_level = 0.95, var_labels = "Adjusted mean", show_labels = "hidden", 
      .labels = NULL)

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

template_ancova generates expressions with multiple endpoints with combined reference arms

Code
  res
Output
  $data
  {
      adqs <- adqs %>% dplyr::filter(ARMCD %in% c("ARM B", "ARM C", 
          "ARM A")) %>% dplyr::mutate(ARMCD = combine_levels(ARMCD, 
          levels = c("ARM B", "ARM C"), new_level = "ARM B/ARM C")) %>% 
          dplyr::mutate(ARMCD = stats::relevel(ARMCD, ref = "ARM B/ARM C")) %>% 
          droplevels() %>% df_explicit_na(na_level = default_na_str())
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM B", "ARM C", 
          "ARM A")) %>% dplyr::mutate(ARMCD = combine_levels(ARMCD, 
          levels = c("ARM B", "ARM C"), new_level = "ARM B/ARM C")) %>% 
          dplyr::mutate(ARMCD = stats::relevel(ARMCD, ref = "ARM B/ARM C")) %>% 
          droplevels() %>% df_explicit_na(na_level = default_na_str())
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary of Analysis of Variance for A and B at WEEK 2 DAY 1 for Absolute Change from Baseline") %>% 
      rtables::split_cols_by(var = "ARMCD", ref_group = "ARM B/ARM C") %>% 
      rtables::split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", 
          split_label = teal.data::col_labels(adqs["AVISIT"], fill = TRUE)) %>% 
      rtables::split_rows_by("PARAMCD", split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adqs["PARAMCD"], 
              fill = TRUE)) %>% summarize_ancova(vars = "CHG", 
      variables = list(arm = "ARMCD", covariates = c("BASE", "STRATA1")), 
      conf_level = 0.95, var_labels = "Adjusted mean", show_labels = "hidden", 
      .labels = NULL)

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

template_ancova generates expressions with single endpoint

Code
  res
Output
  $data
  {
      adqs <- adqs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary of Analysis of Variance for MYFAVORITE at  for Absolute Change from Baseline") %>% 
      rtables::split_cols_by(var = "ARMCD", ref_group = "ARM A") %>% 
      rtables::split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", 
          split_label = teal.data::col_labels(adqs["AVISIT"], fill = TRUE)) %>% 
      rtables::append_topleft(paste0("  ", "MYFAVORITE")) %>% summarize_ancova(vars = "CHG", 
      variables = list(arm = "ARMCD", covariates = NULL), conf_level = 0.95, 
      var_labels = "Unadjusted comparison", .labels = c(lsmean = "Mean", 
          lsmean_diff = "Difference in Means"), table_names = "unadjusted_comparison") %>% 
      summarize_ancova(vars = "CHG", variables = list(arm = "ARMCD", 
          covariates = c("BASE", "STRATA1")), conf_level = 0.95, 
          var_labels = paste0("Adjusted comparison (", paste(c("BASE", 
              "STRATA1"), collapse = " + "), ")"), table_names = "adjusted_comparison")

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

template_ancova generates expressions with discrete interaction variable

Code
  res
Output
  $data
  {
      adqs <- adqs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary of Analysis of Variance for Function/Well-Being (GF1,GF3,GF7) and BFI All Questions at WEEK 1 DAY 8 for Absolute Change from Baseline") %>% 
      rtables::split_cols_by(var = "ARMCD", ref_group = "ARM A") %>% 
      rtables::split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", 
          split_label = teal.data::col_labels(adqs["AVISIT"], fill = TRUE)) %>% 
      rtables::split_rows_by("PARAMCD", split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adqs["PARAMCD"], 
              fill = TRUE)) %>% rtables::append_topleft(paste0("    Interaction Variable: ", 
      "SEX")) %>% summarize_ancova(vars = "CHG", variables = list(arm = "ARMCD", 
      covariates = c("BASE", "STRATA1", "ARMCD*SEX")), conf_level = 0.95, 
      var_labels = paste("Interaction Level:", "M"), show_labels = if (FALSE) "hidden" else "visible", 
      interaction_y = "M", interaction_item = "SEX")

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

template_ancova generates expressions with continuous interaction variable

Code
  res
Output
  $data
  {
      adqs <- adqs %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
      adsl <- adsl %>% dplyr::filter(ARMCD %in% c("ARM A", "ARM B", 
          "ARM C")) %>% dplyr::mutate(ARMCD = stats::relevel(ARMCD, 
          ref = "ARM A")) %>% droplevels() %>% df_explicit_na(na_level = default_na_str())
  }

  $layout_prep
  split_fun <- drop_split_levels

  $layout
  lyt <- rtables::basic_table(show_colcounts = TRUE, title = "Summary of Analysis of Variance for Function/Well-Being (GF1,GF3,GF7) and BFI All Questions at WEEK 1 DAY 8 for Absolute Change from Baseline") %>% 
      rtables::split_cols_by(var = "ARMCD", ref_group = "ARM A") %>% 
      rtables::split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", 
          split_label = teal.data::col_labels(adqs["AVISIT"], fill = TRUE)) %>% 
      rtables::split_rows_by("PARAMCD", split_fun = split_fun, 
          label_pos = "topleft", split_label = teal.data::col_labels(adqs["PARAMCD"], 
              fill = TRUE)) %>% rtables::append_topleft(paste0("    Interaction Variable: ", 
      "BASE")) %>% summarize_ancova(vars = "CHG", variables = list(arm = "ARMCD", 
      covariates = c("BASE", "STRATA1", "ARMCD*BASE")), conf_level = 0.95, 
      var_labels = paste("Interaction Level:", FALSE), show_labels = if (TRUE) "hidden" else "visible", 
      interaction_y = FALSE, interaction_item = "BASE")

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

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