Count the Number of Patients with Particular Flags

The primary analysis variable .var denotes the unique patient identifier.

Usage

s_count_patients_with_flags(
  df,
  .var,
  flag_variables,
  .N_col,
  .N_row,
  denom = c("n", "N_row", "N_col")
)

a_count_patients_with_flags(
  df,
  .var,
  flag_variables,
  .N_col,
  .N_row,
  denom = c("n", "N_row", "N_col")
)

count_patients_with_flags(
  lyt,
  var,
  var_labels = var,
  show_labels = "hidden",
  ...,
  table_names = paste0("tbl_flags_", var),
  .stats = "count_fraction",
  .formats = NULL,
  .indent_mods = NULL
)

Arguments

df

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

.var

(character)
name of the column that contains the unique identifier.

flag_variables

(character)
a character vector specifying the names of logical variables from analysis dataset used for counting the number of unique identifiers.

.N_col

(count)
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is passed by rtables.

.N_row

(count)
column-wise N (column count) for the full column that is passed by rtables.

denom

(string)
choice of denominator for proportion. Options are:

n: number of values in this row and column intersection.
N_row: total number of values in this row across columns.
N_col: total number of values in this column across rows.

lyt

(layout)
input layout where analyses will be added to.

var

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

var_labels

(character)
character for label.

show_labels

(string)
label visibility: one of "default", "visible" and "hidden".

...

additional arguments for the lower level functions.

table_names

(character)
this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics.

.indent_mods

(named integer)
indent modifiers for the labels.

Value

s_count_patients_with_flags() returns the count and the fraction of unique identifiers with each particular flag as a list of statistics n, count, count_fraction, and n_blq, with one element per flag.

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

count_patients_with_flags() 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_patients_with_flags() to the table layout.

Functions

s_count_patients_with_flags(): Statistics function which counts the number of patients for which a particular flag variable is TRUE.
a_count_patients_with_flags(): Formatted analysis function which is used as afun in count_patients_with_flags().
count_patients_with_flags(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

Examples

library(dplyr)

# `s_count_patients_with_flags()`

# Add labelled flag variables to analysis dataset.
adae <- tern_ex_adae %>%
  mutate(
    fl1 = TRUE,
    fl2 = TRTEMFL == "Y",
    fl3 = TRTEMFL == "Y" & AEOUT == "FATAL",
    fl4 = TRTEMFL == "Y" & AEOUT == "FATAL" & AEREL == "Y"
  )
labels <- c(
  "fl1" = "Total AEs",
  "fl2" = "Total number of patients with at least one adverse event",
  "fl3" = "Total number of patients with fatal AEs",
  "fl4" = "Total number of patients with related fatal AEs"
)
formatters::var_labels(adae)[names(labels)] <- labels

s_count_patients_with_flags(
  adae,
  "SUBJID",
  flag_variables = c("fl1", "fl2", "fl3", "fl4"),
  denom = "N_col",
  .N_col = 1000
)
#> $n
#> $n$fl1
#> [1] 164
#> 
#> $n$fl2
#> [1] 164
#> 
#> $n$fl3
#> [1] 164
#> 
#> $n$fl4
#> [1] 164
#> 
#> 
#> $count
#> $count$fl1
#> [1] 164
#> 
#> $count$fl2
#> [1] 164
#> 
#> $count$fl3
#> [1] 79
#> 
#> $count$fl4
#> [1] 79
#> 
#> 
#> $count_fraction
#> $count_fraction$fl1
#> [1] 164.000   0.164
#> 
#> $count_fraction$fl2
#> [1] 164.000   0.164
#> 
#> $count_fraction$fl3
#> [1] 79.000  0.079
#> 
#> $count_fraction$fl4
#> [1] 79.000  0.079
#> 
#> 
#> $n_blq
#> $n_blq$fl1
#> [1] 0
#> 
#> $n_blq$fl2
#> [1] 0
#> 
#> $n_blq$fl3
#> [1] 0
#> 
#> $n_blq$fl4
#> [1] 0
#> 
#> 

#  We need to ungroup `count_fraction` first so that the `rtables` formatting
# function `format_count_fraction()` can be applied correctly.

# `a_count_patients_with_flags()`

afun <- make_afun(a_count_patients_with_flags,
  .stats = "count_fraction",
  .ungroup_stats = "count_fraction"
)
afun(
  adae,
  .N_col = 10L,
  .N_row = 10L,
  .var = "USUBJID",
  flag_variables = c("fl1", "fl2", "fl3", "fl4")
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name formatted_cell indent_mod row_label
#> 1      fl1     164 (100%)          0       fl1
#> 2      fl2     164 (100%)          0       fl2
#> 3      fl3     79 (48.2%)          0       fl3
#> 4      fl4     79 (48.2%)          0       fl4

# `count_patients_with_flags()`

lyt2 <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_patients_with_flags(
    "SUBJID",
    flag_variables = formatters::var_labels(adae[, c("fl1", "fl2", "fl3", "fl4")]),
    denom = "N_col"
  )
build_table(lyt2, adae, alt_counts_df = tern_ex_adsl)
#>                                                            A: Drug X    B: Placebo   C: Combination
#>                                                              (N=69)       (N=73)         (N=58)    
#> ———————————————————————————————————————————————————————————————————————————————————————————————————
#> Total AEs                                                  59 (85.5%)   57 (78.1%)     48 (82.8%)  
#> Total number of patients with at least one adverse event   59 (85.5%)   57 (78.1%)     48 (82.8%)  
#> Total number of patients with fatal AEs                    28 (40.6%)   31 (42.5%)     20 (34.5%)  
#> Total number of patients with related fatal AEs            28 (40.6%)   31 (42.5%)     20 (34.5%)

Usage

Arguments

Value

Functions

See also

Examples