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Count number of patients with missed doses by thresholds

Source: R/count_missed_doses.R
count_missed_doses.Rd

[Stable]

The analyze function creates a layout element to calculate cumulative counts of patients with number of missed doses at least equal to user-specified threshold values.

This function analyzes numeric variable vars, a variable with numbers of missed doses, against the threshold values supplied to the thresholds argument as a numeric vector. This function assumes that every row of the given data frame corresponds to a unique patient.

Usage 

count_missed_doses(
  lyt,
  vars,
  thresholds,
  var_labels = vars,
  show_labels = "visible",
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_count_nonmissing(x)

s_count_missed_doses(x, thresholds, .N_col)

a_count_missed_doses(x, thresholds, .N_col)

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

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

thresholds

(numeric)
minimum number of missed doses the patients had.

var_labels

(character)
variable labels.

show_labels

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

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.

...

additional arguments for the lower level functions.

table_names

(character)
this can be customized in the case that the same vars are analyzed multiple times, to avoid warnings from rtables.

.stats

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

.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).

.indent_mods

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

x

(numeric)
vector of numbers we want to analyze.

.N_col

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

Value

  • count_missed_doses() 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_count_missed_doses() to the table layout.

  • s_count_nonmissing() returns the statistic n which is the count of non-missing values in x.

  • s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold.

Functions

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

  • 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 afun in count_missed_doses().

See also

  • Relevant description function d_count_missed_doses() which generates labels for count_missed_doses().

  • Similar analyze function count_cumulative() which more generally counts cumulative values and has more options for threshold handling, but uses different labels.

Examples 

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     69 (100%)    69 (94.5%)     55 (94.8%)  
#>   At least 5 missed doses    57 (82.6%)   55 (75.3%)     50 (86.2%)  
#>   At least 10 missed doses    40 (58%)    40 (54.8%)     32 (55.2%)  
#>   At least 15 missed doses   26 (37.7%)   25 (34.2%)     13 (22.4%)