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Counting Patients Summing Exposure Across All Patients in Columns

Source: R/summarize_patients_exposure_in_cols.R
summarize_patients_exposure_in_cols.Rd

[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 

s_count_patients_sum_exposure(
  df,
  .var = "AVAL",
  id = "USUBJID",
  labelstr = "",
  .N_col,
  custom_label = NULL
)

summarize_patients_exposure_in_cols(
  lyt,
  var,
  ...,
  .stats = c("n_patients", "sum_exposure"),
  .labels = c(n_patients = "Patients", sum_exposure = "Person time"),
  .indent_mods = NULL,
  col_split = TRUE
)

Arguments

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.

id

(string)
subject variable name.

labelstr

(character)
label of the level of the parent split currently being summarized (must be present as second argument in Content Row Functions).

.N_col

(count)
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is passed by rtables.

custom_label

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

lyt

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

...

additional arguments for the lower level functions.

.stats

(character)
statistics to select for the table.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels.

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.

Value

s_count_patients_sum_exposure() returns a list with the statistics:

  • n_patients: number of unique patients in df.

  • sum_exposure: sum of .var across all patients in df.

Functions

  • s_count_patients_sum_exposure(): Statistics function which counts numbers of patients and the sum of exposure across all patients.

  • summarize_patients_exposure_in_cols(): Layout creating function which adds the count statistics of patients and the sum of analysis value in the column layout as content rows.

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
)

# Internal function - s_count_patients_sum_exposure
if (FALSE) {
s_count_patients_sum_exposure(df = df, .N_col = nrow(adsl))
s_count_patients_sum_exposure(
  df = df,
  .N_col = nrow(adsl),
  custom_label = "some user's custom label"
)
}


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) %>%
  split_rows_by("SEX") %>%
  summarize_patients_exposure_in_cols(var = "AVAL", 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"
  ) %>%
  split_rows_by("SEX") %>%
  summarize_patients_exposure_in_cols(var = "AVAL", col_split = FALSE)
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         6 (50.0%) 
#>   Male               0 (0.0%)        68        6 (50.0%) 

lyt3 <- basic_table() %>%
  summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE)
result3 <- build_table(lyt3, df = df, alt_counts_df = adsl)
result3
#>                                       Patients     Person time
#> ——————————————————————————————————————————————————————————————
#> Total patients numbers/person time   12 (100.0%)       114    

lyt4 <- basic_table() %>%
  summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE, .stats = "sum_exposure")
result4 <- build_table(lyt4, df = df, alt_counts_df = adsl)
result4
#>                                      Person time
#> ————————————————————————————————————————————————
#> Total patients numbers/person time       114