Count the number of patients with a particular event

The analyze function count_patients_with_event() creates a layout element to calculate patient counts for a user-specified set of events.

This function analyzes primary analysis variable vars which indicates unique subject identifiers. Events are defined by the user as a named vector via the filters argument, where each name corresponds to a variable and each value is the value(s) that that variable takes for the event.

If there are multiple records with the same event recorded for a patient, only one occurrence is counted.

Usage

count_patients_with_event(
  lyt,
  vars,
  filters,
  riskdiff = FALSE,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = vars,
  .stats = "count_fraction",
  .formats = list(count_fraction = format_count_fraction_fixed_dp),
  .labels = NULL,
  .indent_mods = NULL
)

s_count_patients_with_event(
  df,
  .var,
  filters,
  .N_col,
  .N_row,
  denom = c("n", "N_col", "N_row")
)

a_count_patients_with_event(
  df,
  labelstr = "",
  filters,
  denom = c("n", "N_col", "N_row"),
  .N_col,
  .N_row,
  .df_row,
  .var = NULL,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na_str = default_na_str()
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

filters

(character)
a character vector specifying the column names and flag variables to be used for counting the number of unique identifiers satisfying such conditions. Multiple column names and flags are accepted in this format c("column_name1" = "flag1", "column_name2" = "flag2"). Note that only equality is being accepted as condition.

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.

Options are: 'n', 'count', 'count_fraction', 'count_fraction_fixed_dp', 'n_blq'

.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

(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.

labelstr

(string)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information.

.df_row

(data.frame)
data frame across all of the columns for the given row split.

Value

count_patients_with_event() 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_event() to the table layout.

s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event.

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

Functions

count_patients_with_event(): 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_event(): Statistics function which counts the number of patients for which the defined event has occurred.
a_count_patients_with_event(): Formatted analysis function which is used as afun in count_patients_with_event().

Examples

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_values(
    "STUDYID",
    values = "AB12345",
    .stats = "count",
    .labels = c(count = "Total AEs")
  ) %>%
  count_patients_with_event(
    "SUBJID",
    filters = c("TRTEMFL" = "Y"),
    .labels = c(count_fraction = "Total number of patients with at least one adverse event"),
    table_names = "tbl_all"
  ) %>%
  count_patients_with_event(
    "SUBJID",
    filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL"),
    .labels = c(count_fraction = "Total number of patients with fatal AEs"),
    table_names = "tbl_fatal"
  ) %>%
  count_patients_with_event(
    "SUBJID",
    filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL", "AEREL" = "Y"),
    .labels = c(count_fraction = "Total number of patients with related fatal AEs"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "tbl_rel_fatal"
  )

build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl)
#>                                                            A: Drug X    B: Placebo   C: Combination
#>                                                              (N=69)       (N=73)         (N=58)    
#> ———————————————————————————————————————————————————————————————————————————————————————————————————
#> Total AEs                                                     202          177            162      
#> Total number of patients with at least one adverse event   59 (100%)    57 (100%)      48 (100%)   
#> Total number of patients with fatal AEs                    28 (47.5%)   31 (54.4%)     20 (41.7%)  
#>     Total number of patients with related fatal AEs        28 (47.5%)   31 (54.4%)     20 (41.7%)  

s_count_patients_with_event(
  tern_ex_adae,
  .var = "SUBJID",
  filters = c("TRTEMFL" = "Y")
)
#> $n
#> [1] 164
#> 
#> $count
#> [1] 164
#> 
#> $count_fraction
#> [1] 164   1
#> 
#> $n_blq
#> [1] 0
#> 

s_count_patients_with_event(
  tern_ex_adae,
  .var = "SUBJID",
  filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL")
)
#> $n
#> [1] 164
#> 
#> $count
#> [1] 79
#> 
#> $count_fraction
#> [1] 79.0000000  0.4817073
#> 
#> $n_blq
#> [1] 0
#> 

s_count_patients_with_event(
  tern_ex_adae,
  .var = "SUBJID",
  filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL"),
  denom = "N_col",
  .N_col = 456
)
#> $n
#> [1] 164
#> 
#> $count
#> [1] 79
#> 
#> $count_fraction
#> [1] 79.0000000  0.1732456
#> 
#> $n_blq
#> [1] 0
#> 

a_count_patients_with_event(
  tern_ex_adae,
  .var = "SUBJID",
  filters = c("TRTEMFL" = "Y"),
  .N_col = 100,
  .N_row = 100
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>                  row_name formatted_cell indent_mod      row_label
#> 1                       n            164          0              n
#> 2                   count            164          0          count
#> 3          count_fraction     164 (100%)          0 count_fraction
#> 4 count_fraction_fixed_dp     164 (100%)          0 count_fraction
#> 5                   n_blq              0          0          n_blq

Usage

Arguments

Value

Functions

See also

Examples