Count patients with marked laboratory abnormalities

The analyze function count_abnormal_by_marked() creates a layout element to count patients with marked laboratory abnormalities for each direction of abnormality, categorized by parameter value.

This function analyzes primary analysis variable var which indicates whether a single, replicated, or last marked laboratory abnormality was observed. Levels of var to include for each marked lab abnormality (single and last_replicated) can be supplied via the category parameter. Additional analysis variables that can be supplied as a list via the variables parameter are id (defaults to USUBJID), a variable to indicate unique subject identifiers, param (defaults to PARAM), a variable to indicate parameter values, and direction (defaults to abn_dir), a variable to indicate abnormality directions.

For each combination of param and direction levels, marked lab abnormality counts are calculated as follows:

Single, not last & Last or replicated: The number of patients with Single, not last and Last or replicated values, respectively.
Any: The number of patients with either single or replicated marked abnormalities.

Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis.

Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction.

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")
)

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.

Options are: 'count_fraction', 'count_fraction_fixed_dp'

.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.

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().

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().

Note

Single, not last and Last or replicated levels are mutually exclusive. If a patient has abnormalities that meet both the Single, not last and Last or replicated criteria, then the patient will be counted only under the Last or replicated category.

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)
#>                           ARM A      ARM B  
#> ————————————————————————————————————————————
#> ALT (n)                     1          1    
#>   Low                                       
#>     Single, not last     1 (100%)      0    
#>     Last or replicated      0       1 (100%)
#>     Any Abnormality      1 (100%)   1 (100%)
#> CRP (n)                     1          1    
#>   High                                      
#>     Single, not last     1 (100%)      0    
#>     Last or replicated      0       1 (100%)
#>     Any Abnormality      1 (100%)   1 (100%)

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)
#>                           ARM A      ARM B  
#> ————————————————————————————————————————————
#> ALT (n)                     1          1    
#>   Low                                       
#>     Single, not last     1 (100%)      0    
#>     Last or replicated      0       1 (100%)
#>     Any Abnormality      1 (100%)   1 (100%)
#> CRP (n)                     1          1    
#>   High                                      
#>     Single, not last     1 (100%)      0    
#>     Last or replicated      0       1 (100%)
#>     Any Abnormality      1 (100%)   1 (100%)