tern (version 0.9.4)

abnormal_by_marked: Count patients with marked laboratory abnormalities

Description

Primary analysis variable .var indicates whether single, replicated or last marked laboratory abnormality was observed (factor). Additional analysis variables are id (character or factor) and direction (factor) indicating the direction of the abnormality. Denominator is number of patients with at least one valid measurement during the analysis.

• For Single, not last and Last or replicated: Numerator is number of patients with Single, not last and Last or replicated levels, respectively.

• For Any: Numerator is the number of patients with either single or replicated marked abnormalities.

Usage

count_abnormal_by_marked(
  lyt,
  var,
  category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
  variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir"),
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)
s_count_abnormal_by_marked(
  df,
  .var = "AVALCAT1",
  .spl_context,
  category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
  variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")
)
a_count_abnormal_by_marked(
  df,
  .var = "AVALCAT1",
  .spl_context,
  category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
  variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")
)

Value

• count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout.

• s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results.

• a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue().

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

category

(list)
a list with different marked category names for single and last or replicated.

variables

(named list of string)
list of additional analysis variables.

na_str

(string)
string used to replace all NA or empty values in the output.

nested

(flag)
whether this layout instruction should be applied within the existing layout structure _if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.

...

additional arguments for the lower level functions.

.stats

(character)
statistics to select for the table. Run get_stats("abnormal_by_marked") to see available statistics for this function.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

df

(data.frame)
data set containing all analysis variables.

.var, var

(string)
single variable name that is passed by rtables when requested by a statistics function.

.spl_context

(data.frame)
gives information about ancestor split states that is passed by rtables.

Functions

• count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

• s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities.

• a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked().

Examples

library(dplyr)

df <- data.frame(
  USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))),
  ARMCD = factor(c(rep("ARM A", 5), rep("ARM B", 5), rep("ARM A", 5), rep("ARM B", 5))),
  ANRIND = factor(c(
    "NORMAL", "HIGH", "HIGH", "HIGH HIGH", "HIGH",
    "HIGH", "HIGH", "HIGH HIGH", "NORMAL", "HIGH HIGH", "NORMAL", "LOW", "LOW", "LOW LOW", "LOW",
    "LOW", "LOW", "LOW LOW", "NORMAL", "LOW LOW"
  )),
  ONTRTFL = rep(c("", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"), 2),
  PARAMCD = factor(c(rep("CRP", 10), rep("ALT", 10))),
  AVALCAT1 = factor(rep(c("", "", "", "SINGLE", "REPLICATED", "", "", "LAST", "", "SINGLE"), 2)),
  stringsAsFactors = FALSE
)

df <- df %>%
  mutate(abn_dir = factor(
    case_when(
      ANRIND == "LOW LOW" ~ "Low",
      ANRIND == "HIGH HIGH" ~ "High",
      TRUE ~ ""
    ),
    levels = c("Low", "High")
  ))

# Select only post-baseline records.
df <- df %>% filter(ONTRTFL == "Y")
df_crp <- df %>%
  filter(PARAMCD == "CRP") %>%
  droplevels()
full_parent_df <- list(df_crp, "not_needed")
cur_col_subset <- list(rep(TRUE, nrow(df_crp)), "not_needed")
spl_context <- data.frame(
  split = c("PARAMCD", "GRADE_DIR"),
  full_parent_df = I(full_parent_df),
  cur_col_subset = I(cur_col_subset)
)

map <- unique(
  df[df$abn_dir %in% c("Low", "High") & df$AVALCAT1 != "", c("PARAMCD", "abn_dir")]
) %>%
  lapply(as.character) %>%
  as.data.frame() %>%
  arrange(PARAMCD, abn_dir)

basic_table() %>%
  split_cols_by("ARMCD") %>%
  split_rows_by("PARAMCD") %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique_count"
  ) %>%
  split_rows_by(
    "abn_dir",
    split_fun = trim_levels_to_map(map)
  ) %>%
  count_abnormal_by_marked(
    var = "AVALCAT1",
    variables = list(
      id = "USUBJID",
      param = "PARAMCD",
      direction = "abn_dir"
    )
  ) %>%
  build_table(df = df)

basic_table() %>%
  split_cols_by("ARMCD") %>%
  split_rows_by("PARAMCD") %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique_count"
  ) %>%
  split_rows_by(
    "abn_dir",
    split_fun = trim_levels_in_group("abn_dir")
  ) %>%
  count_abnormal_by_marked(
    var = "AVALCAT1",
    variables = list(
      id = "USUBJID",
      param = "PARAMCD",
      direction = "abn_dir"
    )
  ) %>%
  build_table(df = df)