Count number of patients

Source: R/summarize_num_patients.R

[Stable]

The analyze function analyze_num_patients() creates a layout element to count total numbers of unique or non-unique patients. The primary analysis variable vars is used to uniquely identify patients.

The count_by variable can be used to identify non-unique patients such that the number of patients with a unique combination of values in vars and count_by will be returned instead as the nonunique statistic. The required variable can be used to specify a variable required to be non-missing for the record to be included in the counts.

The summarize function summarize_num_patients() performs the same function as analyze_num_patients() except it creates content rows, not data rows, to summarize the current table row/column context and operates on the level of the latest row split or the root of the table if no row splits have occurred.

analyze_num_patients(
  lyt,
  vars,
  required = NULL,
  count_by = NULL,
  unique_count_suffix = TRUE,
  na_str = default_na_str(),
  nested = TRUE,
  .stats = NULL,
  .formats = NULL,
  .labels = c(unique = "Number of patients with at least one event", nonunique =
    "Number of events"),
  show_labels = c("default", "visible", "hidden"),
  .indent_mods = 0L,
  riskdiff = FALSE,
  ...
)

summarize_num_patients(
  lyt,
  var,
  required = NULL,
  count_by = NULL,
  unique_count_suffix = TRUE,
  na_str = default_na_str(),
  .stats = NULL,
  .formats = NULL,
  .labels = c(unique = "Number of patients with at least one event", nonunique =
    "Number of events"),
  .indent_mods = 0L,
  riskdiff = FALSE,
  ...
)

s_num_patients(
  x,
  labelstr,
  .N_col,
  count_by = NULL,
  unique_count_suffix = TRUE
)

s_num_patients_content(
  df,
  labelstr = "",
  .N_col,
  .var,
  required = NULL,
  count_by = NULL,
  unique_count_suffix = TRUE
)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

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

required

(character or NULL)
name of a variable that is required to be non-missing.

count_by

(character or NULL)
name of a variable to be combined with vars when counting nonunique records.

unique_count_suffix

(flag)
whether the "(n)" suffix should be added to unique_count labels. Defaults to TRUE.

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.

.stats

(character)
statistics to select for the table.

Options are: 'unique', 'nonunique', 'unique_count'

.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).

show_labels

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

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

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.

...

additional arguments for the lower level functions.

x

(character or factor)
vector of patient IDs.

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.

.N_col

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

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.

Value

  • analyze_num_patients() 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_num_patients_content() to the table layout.

  • summarize_num_patients() 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_num_patients_content() to the table layout.

  • s_num_patients() returns a named list of 3 statistics:

    • unique: Vector of counts and percentages.

    • nonunique: Vector of counts.

    • unique_count: Counts.

  • s_num_patients_content() returns the same values as s_num_patients().

Details

In general, functions that starts with analyze* are expected to work like rtables::analyze(), while functions that starts with summarize* are based upon rtables::summarize_row_groups(). The latter provides a value for each dividing split in the row and column space, but, being it bound to the fundamental splits, it is repeated by design in every page when pagination is involved.

Functions

  • analyze_num_patients(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

  • summarize_num_patients(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups().

  • s_num_patients(): Statistics function which counts the number of unique patients, the corresponding percentage taken with respect to the total number of patients, and the number of non-unique patients.

  • s_num_patients_content(): Statistics function which counts the number of unique patients in a column (variable), the corresponding percentage taken with respect to the total number of patients, and the number of non-unique patients in the column.

Note

As opposed to summarize_num_patients(), this function does not repeat the produced rows.

Examples

df <- data.frame(
  USUBJID = as.character(c(1, 2, 1, 4, NA, 6, 6, 8, 9)),
  ARM = c("A", "A", "A", "A", "A", "B", "B", "B", "B"),
  AGE = c(10, 15, 10, 17, 8, 11, 11, 19, 17),
  SEX = c("M", "M", "M", "F", "F", "F", "M", "F", "M")
)

# analyze_num_patients
tbl <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  analyze_num_patients("USUBJID", .stats = c("unique")) %>%
  build_table(df)

tbl
#>                                                  A           B    
#>                                                (N=5)       (N=4)  
#> ——————————————————————————————————————————————————————————————————
#> Number of patients with at least one event   3 (60.0%)   3 (75.0%)

# summarize_num_patients
tbl <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  summarize_num_patients("USUBJID", .stats = "unique_count") %>%
  build_table(df)

tbl
#>         A   B
#> —————————————
#> M (n)   2   2
#> F (n)   1   2

# Use the statistics function to count number of unique and nonunique patients.
s_num_patients(x = as.character(c(1, 1, 1, 2, 4, NA)), labelstr = "", .N_col = 6L)
#> $unique
#> [1] 3.0 0.5
#> attr(,"label")
#> [1] ""
#> 
#> $nonunique
#> [1] 5
#> attr(,"label")
#> [1] ""
#> 
#> $unique_count
#> [1] 3
#> attr(,"label")
#> [1] "(n)"
#> 
s_num_patients(
  x = as.character(c(1, 1, 1, 2, 4, NA)),
  labelstr = "",
  .N_col = 6L,
  count_by = c(1, 1, 2, 1, 1, 1)
)
#> $unique
#> [1] 3.0 0.5
#> attr(,"label")
#> [1] ""
#> 
#> $nonunique
#> [1] 4
#> attr(,"label")
#> [1] ""
#> 
#> $unique_count
#> [1] 3
#> attr(,"label")
#> [1] "(n)"
#> 

# Count number of unique and non-unique patients.

df <- data.frame(
  USUBJID = as.character(c(1, 2, 1, 4, NA)),
  EVENT = as.character(c(10, 15, 10, 17, 8))
)
s_num_patients_content(df, .N_col = 5, .var = "USUBJID")
#> $unique
#> [1] 3.0 0.6
#> attr(,"label")
#> [1] ""
#> 
#> $nonunique
#> [1] 4
#> attr(,"label")
#> [1] ""
#> 
#> $unique_count
#> [1] 3
#> attr(,"label")
#> [1] "(n)"
#> 

df_by_event <- data.frame(
  USUBJID = as.character(c(1, 2, 1, 4, NA)),
  EVENT = c(10, 15, 10, 17, 8)
)
s_num_patients_content(df_by_event, .N_col = 5, .var = "USUBJID", count_by = "EVENT")
#> $unique
#> [1] 3.0 0.6
#> attr(,"label")
#> [1] ""
#> 
#> $nonunique
#> [1] 3
#> attr(,"label")
#> [1] ""
#> 
#> $unique_count
#> [1] 3
#> attr(,"label")
#> [1] "(n)"
#>