summarize_patients_exposure_in_cols {tern}R Documentation

Count patients and sum exposure across all patients in columns

Description

[Stable]

Counting the number of patients and summing analysis value (i.e exposure values) across all patients when a column table layout is required.

Usage

analyze_patients_exposure_in_cols(
  lyt,
  var = NULL,
  ex_var = "AVAL",
  id = "USUBJID",
  add_total_level = FALSE,
  custom_label = NULL,
  col_split = TRUE,
  na_str = default_na_str(),
  .stats = c("n_patients", "sum_exposure"),
  .labels = c(n_patients = "Patients", sum_exposure = "Person time"),
  .indent_mods = 0L,
  ...
)

summarize_patients_exposure_in_cols(
  lyt,
  var,
  ex_var = "AVAL",
  id = "USUBJID",
  add_total_level = FALSE,
  custom_label = NULL,
  col_split = TRUE,
  na_str = default_na_str(),
  ...,
  .stats = c("n_patients", "sum_exposure"),
  .labels = c(n_patients = "Patients", sum_exposure = "Person time"),
  .indent_mods = NULL
)

s_count_patients_sum_exposure(
  df,
  ex_var = "AVAL",
  id = "USUBJID",
  labelstr = "",
  .stats = c("n_patients", "sum_exposure"),
  .N_col,
  custom_label = NULL
)

a_count_patients_sum_exposure(
  df,
  var = NULL,
  ex_var = "AVAL",
  id = "USUBJID",
  add_total_level = FALSE,
  custom_label = NULL,
  labelstr = "",
  .N_col,
  .stats,
  .formats = list(n_patients = "xx (xx.x%)", sum_exposure = "xx")
)

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.

ex_var

(string)
name of the variable in df containing exposure values.

id

(string)
subject variable name.

add_total_level

(flag)
adds a "total" level after the others which includes all the levels that constitute the split. A custom label can be set for this level via the custom_label argument.

custom_label

(string or NULL)
if provided and labelstr is empty, this will be used as label.

col_split

(flag)
whether the columns should be split. Set to FALSE when the required column split has been done already earlier in the layout pipe.

na_str

(string)
string used to replace all NA or empty values in the output.

.stats

(character)
statistics to select for the table. Run get_stats("analyze_patients_exposure_in_cols") to see available statistics for this function.

.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.

...

additional arguments for the lower level functions.

df

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

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.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

Value

Functions

Note

As opposed to summarize_patients_exposure_in_cols() which generates content rows, analyze_patients_exposure_in_cols() generates data rows which will not be repeated on multiple pages when pagination is used.

Examples

set.seed(1)
df <- data.frame(
  USUBJID = c(paste("id", seq(1, 12), sep = "")),
  ARMCD = c(rep("ARM A", 6), rep("ARM B", 6)),
  SEX = c(rep("Female", 6), rep("Male", 6)),
  AVAL = as.numeric(sample(seq(1, 20), 12)),
  stringsAsFactors = TRUE
)
adsl <- data.frame(
  USUBJID = c(paste("id", seq(1, 12), sep = "")),
  ARMCD = c(rep("ARM A", 2), rep("ARM B", 2)),
  SEX = c(rep("Female", 2), rep("Male", 2)),
  stringsAsFactors = TRUE
)

lyt <- basic_table() %>%
  split_cols_by("ARMCD", split_fun = add_overall_level("Total", first = FALSE)) %>%
  summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE) %>%
  analyze_patients_exposure_in_cols(var = "SEX", col_split = FALSE)
result <- build_table(lyt, df = df, alt_counts_df = adsl)
result

lyt2 <- basic_table() %>%
  split_cols_by("ARMCD", split_fun = add_overall_level("Total", first = FALSE)) %>%
  summarize_patients_exposure_in_cols(
    var = "AVAL", col_split = TRUE,
    .stats = "n_patients", custom_label = "some custom label"
  ) %>%
  analyze_patients_exposure_in_cols(var = "SEX", col_split = FALSE, ex_var = "AVAL")
result2 <- build_table(lyt2, df = df, alt_counts_df = adsl)
result2

lyt3 <- basic_table() %>%
  analyze_patients_exposure_in_cols(var = "SEX", col_split = TRUE, ex_var = "AVAL")
result3 <- build_table(lyt3, df = df, alt_counts_df = adsl)
result3

# Adding total levels and custom label
lyt4 <- basic_table(
  show_colcounts = TRUE
) %>%
  analyze_patients_exposure_in_cols(
    var = "ARMCD",
    col_split = TRUE,
    add_total_level = TRUE,
    custom_label = "TOTAL"
  ) %>%
  append_topleft(c("", "Sex"))

result4 <- build_table(lyt4, df = df, alt_counts_df = adsl)
result4

lyt5 <- basic_table() %>%
  summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE)

result5 <- build_table(lyt5, df = df, alt_counts_df = adsl)
result5

lyt6 <- basic_table() %>%
  summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE, .stats = "sum_exposure")

result6 <- build_table(lyt6, df = df, alt_counts_df = adsl)
result6

a_count_patients_sum_exposure(
  df = df,
  var = "SEX",
  .N_col = nrow(df),
  .stats = "n_patients"
)


[Package tern version 0.9.5 Index]