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Count the number of patients with particular flags

Source: R/count_patients_with_flags.R
count_patients_with_flags.Rd

[Stable]

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

Usage 

count_patients_with_flags(
  lyt,
  var,
  flag_variables,
  flag_labels = NULL,
  var_labels = var,
  show_labels = "hidden",
  riskdiff = FALSE,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = paste0("tbl_flags_", var),
  .stats = "count_fraction",
  .formats = NULL,
  .indent_mods = NULL
)

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

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

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

var

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

flag_variables

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

flag_labels

(character)
vector of labels to use for flag variables.

var_labels

(character)
variable labels.

show_labels

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

riskdiff

(flag)
whether a risk difference column is present. When set to TRUE, add_riskdiff() must be used as split_fun in the prior column split of the table layout, specifying which columns should be compared. See stat_propdiff_ci() for details on risk difference calculation.

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.

table_names

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

.stats

(character)
statistics to select for the table. Run get_stats("count_patients_with_flags") 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.

.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

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

.N_col

(integer(1))
column-wise N (column count) for the full column being analyzed that is typically passed by rtables.

.N_row

(integer(1))
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is typically 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.

Value

  • 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.

  • 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.

Functions

  • 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().

  • 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().

Note

If flag_labels is not specified, variables labels will be extracted from df. If variables are not labeled, variable names will be used instead. Alternatively, a named vector can be supplied to flag_variables such that within each name-value pair the name corresponds to the variable name and the value is the label to use for this variable.

See also

count_patients_with_event

Examples 

library(dplyr)

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

# `count_patients_with_flags()`

lyt2 <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_patients_with_flags(
    "SUBJID",
    flag_variables = 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%)  

# `s_count_patients_with_flags()`

s_count_patients_with_flags(
  adae,
  "SUBJID",
  flag_variables = c("fl1", "fl2", "fl3", "fl4"),
  denom = "N_col",
  .N_col = 1000
)
#> $n
#> $n$`Total AEs`
#> [1] 164
#> 
#> $n$`Total number of patients with at least one adverse event`
#> [1] 164
#> 
#> $n$`Total number of patients with fatal AEs`
#> [1] 164
#> 
#> $n$`Total number of patients with related fatal AEs`
#> [1] 164
#> 
#> 
#> $count
#> $count$`Total AEs`
#> [1] 164
#> 
#> $count$`Total number of patients with at least one adverse event`
#> [1] 164
#> 
#> $count$`Total number of patients with fatal AEs`
#> [1] 79
#> 
#> $count$`Total number of patients with related fatal AEs`
#> [1] 79
#> 
#> 
#> $count_fraction
#> $count_fraction$`Total AEs`
#> [1] 164.000   0.164
#> 
#> $count_fraction$`Total number of patients with at least one adverse event`
#> [1] 164.000   0.164
#> 
#> $count_fraction$`Total number of patients with fatal AEs`
#> [1] 79.000  0.079
#> 
#> $count_fraction$`Total number of patients with related fatal AEs`
#> [1] 79.000  0.079
#> 
#> 
#> $n_blq
#> $n_blq$`Total AEs`
#> [1] 0
#> 
#> $n_blq$`Total number of patients with at least one adverse event`
#> [1] 0
#> 
#> $n_blq$`Total number of patients with fatal AEs`
#> [1] 0
#> 
#> $n_blq$`Total number of patients with related fatal AEs`
#> [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
#> 1                                                Total AEs     164 (100%)
#> 2 Total number of patients with at least one adverse event     164 (100%)
#> 3                  Total number of patients with fatal AEs     79 (48.2%)
#> 4          Total number of patients with related fatal AEs     79 (48.2%)
#>   indent_mod                                                row_label
#> 1          0                                                Total AEs
#> 2          0 Total number of patients with at least one adverse event
#> 3          0                  Total number of patients with fatal AEs
#> 4          0          Total number of patients with related fatal AEs