These are specific functions to count patients with missed doses. The difference to count_cumulative() is
mainly the special labels.
Usage
s_count_nonmissing(x)
s_count_missed_doses(x, thresholds, .N_col)
a_count_missed_doses(x, thresholds, .N_col)
count_missed_doses(
  lyt,
  vars,
  var_labels = vars,
  show_labels = "visible",
  ...,
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)Arguments
- x
- ( - numeric)
 vector of numbers we want to analyze.
- thresholds
- (vector of - count)
 number of missed doses the patients at least had.
- .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.
- lyt
- ( - layout)
 input layout where analyses will be added to.
- vars
- ( - character)
 variable names for the primary analysis variable to be iterated over.
- var_labels
- ( - character)
 character for label.
- show_labels
- ( - string)
 label visibility: one of "default", "visible" and "hidden".
- ...
- additional arguments for the lower level functions. 
- table_names
- ( - character)
 this can be customized in case that the same- varsare analyzed multiple times, to avoid warnings from- rtables.
- .stats
- ( - character)
 statistics to select for the table.
- .formats
- (named - characteror- 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_nonmissing()returns the statistic- nwhich is the count of non-missing values in- x.
- s_count_missed_doses()returns the statistics- nand- count_fractionwith one element for each threshold.
- a_count_missed_doses()returns the corresponding list with formatted- rtables::CellValue().
- count_missed_doses()returns a layout object suitable for passing to further layouting functions, or to- rtables::build_table(). Adding this function to an- rtablelayout will add formatted rows containing the statistics from- s_count_missed_doses()to the table layout.
Functions
- s_count_nonmissing(): Statistics function to count non-missing values.
- s_count_missed_doses(): Statistics function to count patients with missed doses.
- a_count_missed_doses(): Formatted analysis function which is used as- afunin- count_missed_doses().
- count_missed_doses(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for- rtables::analyze().
See also
Relevant description function d_count_missed_doses().
Examples
set.seed(1)
x <- c(sample(1:10, 10), NA)
library(dplyr)
anl <- tern_ex_adsl %>%
  distinct(STUDYID, USUBJID, ARM) %>%
  mutate(
    PARAMCD = "TNDOSMIS",
    PARAM = "Total number of missed doses during study",
    AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE),
    AVALC = ""
  )
basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_missed_doses("AVAL", thresholds = c(1, 5, 10, 15), var_labels = "Missed Doses") %>%
  build_table(anl, alt_counts_df = tern_ex_adsl)
#>                              A: Drug X    B: Placebo   C: Combination
#>                                (N=69)       (N=73)         (N=58)    
#> —————————————————————————————————————————————————————————————————————
#> Missed Doses                                                         
#>   n                              69           73             58      
#>   At least 1 missed dose     65 (94.2%)   67 (91.8%)     58 (100%)   
#>   At least 5 missed doses    54 (78.3%)   51 (69.9%)     54 (93.1%)  
#>   At least 10 missed doses   31 (44.9%)   40 (54.8%)     31 (53.4%)  
#>   At least 15 missed doses   17 (24.6%)   23 (31.5%)     20 (34.5%)