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 byrtables
.- 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 samevars
are analyzed multiple times, to avoid warnings fromrtables
.- .stats
(
character
)
statistics to select for the table.- .formats
(named
character
orlist
)
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 statisticn
which is the count of non-missing values inx
.
s_count_missed_doses()
returns the statisticsn
andcount_fraction
with one element for each threshold.
a_count_missed_doses()
returns the corresponding list with formattedrtables::CellValue()
.
count_missed_doses()
returns a layout object suitable for passing to further layouting functions, or tortables::build_table()
. Adding this function to anrtable
layout will add formatted rows containing the statistics froms_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 asafun
incount_missed_doses()
.count_missed_doses()
: Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper forrtables::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%)