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Count the Number of Patients with a Particular Event

Source: R/count_patients_with_event.R
count_patients_with_event.Rd

[Stable]

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

Usage 

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

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

count_patients_with_event(
  lyt,
  vars,
  riskdiff = FALSE,
  nested = TRUE,
  ...,
  table_names = vars,
  .stats = "count_fraction",
  .formats = NULL,
  .labels = 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.

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.

.N_col

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

.N_row

(integer)
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.

lyt

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

vars

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

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.

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 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.

.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.

Value

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

  • 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.

Functions

  • 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().

  • 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().

See also

count_patients_with_flags

Examples 

library(dplyr)

# `s_count_patients_with_event()`

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()`

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          n_blq              0          0          n_blq

# `count_patients_with_event()`

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%)