Count number of patients and sum exposure across all patients in columns — summarize_patients_exposure_in

The analyze function analyze_patients_exposure_in_cols() creates a layout element to count total numbers of patients and sum an analysis value (i.e. exposure) across all patients in columns.

The primary analysis variable ex_var is the exposure variable used to calculate the sum_exposure statistic. The id variable is used to uniquely identify patients in the data such that only unique patients are counted in the n_patients statistic, and the var variable is used to create a row split if needed. The percentage returned as part of the n_patients statistic is the proportion of all records that correspond to a unique patient.

The summarize function summarize_patients_exposure_in_cols() performs the same function as analyze_patients_exposure_in_cols() 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.

If a column split has not yet been performed in the table, col_split must be set to TRUE for the first call of analyze_patients_exposure_in_cols() or summarize_patients_exposure_in_cols().

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.

Options are: 'n_patients', 'sum_exposure'

.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

analyze_patients_exposure_in_cols() 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 data rows, with the statistics from s_count_patients_sum_exposure() arranged in columns, to the table layout.

summarize_patients_exposure_in_cols() 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 content rows, with the statistics from s_count_patients_sum_exposure() arranged in columns, to the table layout.

s_count_patients_sum_exposure() returns a named list with the statistics:
- n_patients: Number of unique patients in df.
- sum_exposure: Sum of ex_var across all patients in df.

a_count_patients_sum_exposure() returns formatted rtables::CellValue().

Functions

analyze_patients_exposure_in_cols(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::split_cols_by_multivar() and rtables::analyze_colvars().
summarize_patients_exposure_in_cols(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::split_cols_by_multivar() and rtables::summarize_row_groups().
s_count_patients_sum_exposure(): Statistics function which counts numbers of patients and the sum of exposure across all patients.
a_count_patients_sum_exposure(): Analysis function which is used as afun in rtables::analyze_colvars() within analyze_patients_exposure_in_cols() and as cfun in rtables::summarize_row_groups() within summarize_patients_exposure_in_cols().

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
#>                                               ARM A                      ARM B                       Total          
#>                                       Patients    Person time    Patients    Person time    Patients     Person time
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total patients numbers/person time   6 (100.0%)       46        6 (100.0%)       68        12 (100.0%)       114    
#>   Female                             6 (100.0%)       46         0 (0.0%)         0         6 (50.0%)        46     
#>   Male                                0 (0.0%)         0        6 (100.0%)       68         6 (50.0%)        68     

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
#>                       ARM A        ARM B         Total   
#>                      Patients     Patients     Patients  
#> —————————————————————————————————————————————————————————
#> some custom label   6 (100.0%)   6 (100.0%)   12 (100.0%)
#>   Female            6 (100.0%)    0 (0.0%)     6 (50.0%) 
#>   Male               0 (0.0%)    6 (100.0%)    6 (50.0%) 

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
#>          Patients    Person time
#> ————————————————————————————————
#> Female   6 (50.0%)       46     
#> Male     6 (50.0%)       68     

# 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
#>          Patients     Person time
#> Sex       (N=12)        (N=12)   
#> —————————————————————————————————
#> ARM A    6 (50.0%)        46     
#> ARM B    6 (50.0%)        68     
#> TOTAL   12 (100.0%)       114    

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

result5 <- build_table(lyt5, df = df, alt_counts_df = adsl)
result5
#>                                       Patients     Person time
#> ——————————————————————————————————————————————————————————————
#> Total patients numbers/person time   12 (100.0%)       114    

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
#>                                      Person time
#> ————————————————————————————————————————————————
#> Total patients numbers/person time       114    

a_count_patients_sum_exposure(
  df = df,
  var = "SEX",
  .N_col = nrow(df),
  .stats = "n_patients"
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name formatted_cell indent_mod row_label
#> 1   Female      6 (50.0%)          0    Female
#> 2     Male      6 (50.0%)          0      Male