Primary analysis variable .var
indicates the abnormal range result (character
or factor
)
and additional analysis variables are id
(character
or factor
) and baseline
(character
or
factor
). For each direction specified in abnormal
(e.g. high or low) count patients in the
numerator and denominator as follows:
num
: The number of patients with this abnormality recorded while on treatment.denom
: The number of patients with at least one post-baseline assessment.
Usage
count_abnormal(
lyt,
var,
abnormal = list(Low = "LOW", High = "HIGH"),
variables = list(id = "USUBJID", baseline = "BNRIND"),
exclude_base_abn = FALSE,
na_str = default_na_str(),
nested = TRUE,
...,
table_names = var,
.stats = NULL,
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)
s_count_abnormal(
df,
.var,
abnormal = list(Low = "LOW", High = "HIGH"),
variables = list(id = "USUBJID", baseline = "BNRIND"),
exclude_base_abn = FALSE
)
a_count_abnormal(
df,
.var,
abnormal = list(Low = "LOW", High = "HIGH"),
variables = list(id = "USUBJID", baseline = "BNRIND"),
exclude_base_abn = FALSE
)
Arguments
- lyt
(
layout
)
input layout where analyses will be added to.- abnormal
(named
list
)
list identifying the abnormal range level(s) invar
. Defaults tolist(Low = "LOW", High = "HIGH")
but you can also group different levels into the named list, for example,abnormal = list(Low = c("LOW", "LOW LOW"), High = c("HIGH", "HIGH HIGH"))
.- variables
(named
list
ofstring
)
list of additional analysis variables.- exclude_base_abn
(
flag
)
whether to exclude subjects with baseline abnormality from numerator and denominator.- na_str
(
string
)
string used to replace allNA
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 case that the samevars
are analyzed multiple times, to avoid warnings fromrtables
.- .stats
(
character
)
statistics to select for the table. Runget_stats("abnormal")
to see available statistics for this function.- .formats
(named
character
orlist
)
formats for the statistics. See Details inanalyze_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.- df
(
data.frame
)
data set containing all analysis variables.- .var, var
(
string
)
single variable name that is passed byrtables
when requested by a statistics function.
Value
count_abnormal()
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_abnormal()
to the table layout.
s_count_abnormal()
returns the statisticfraction
which is a vector withnum
anddenom
counts of patients.
a_count_abnormal()
returns the corresponding list with formattedrtables::CellValue()
.
Functions
count_abnormal()
: Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper forrtables::analyze()
.s_count_abnormal()
: Statistics function which counts patients with abnormal range values for a singleabnormal
level.a_count_abnormal()
: Formatted analysis function which is used asafun
incount_abnormal()
.
Note
count_abnormal()
only works with a single variable containing multiple abnormal levels.df
should be filtered to include only post-baseline records.the denominator includes patients that might have other abnormal levels at baseline, and patients with missing baseline. Patients with these abnormalities at baseline can be optionally excluded from numerator and denominator.
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:testthat’:
#>
#> matches
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
df <- data.frame(
USUBJID = as.character(c(1, 1, 2, 2)),
ANRIND = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
BNRIND = factor(c("NORMAL", "NORMAL", "HIGH", "HIGH")),
ONTRTFL = c("", "Y", "", "Y"),
stringsAsFactors = FALSE
)
# Select only post-baseline records.
df <- df %>%
filter(ONTRTFL == "Y")
# Layout creating function.
basic_table() %>%
count_abnormal(var = "ANRIND", abnormal = list(high = "HIGH", low = "LOW")) %>%
build_table(df)
#> all obs
#> ————————————————
#> high 1/2 (50%)
#> low 1/2 (50%)
# Passing of statistics function and formatting arguments.
df2 <- data.frame(
ID = as.character(c(1, 1, 2, 2)),
RANGE = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
BL_RANGE = factor(c("NORMAL", "NORMAL", "HIGH", "HIGH")),
ONTRTFL = c("", "Y", "", "Y"),
stringsAsFactors = FALSE
)
# Select only post-baseline records.
df2 <- df2 %>%
filter(ONTRTFL == "Y")
basic_table() %>%
count_abnormal(
var = "RANGE",
abnormal = list(low = "LOW", high = "HIGH"),
variables = list(id = "ID", baseline = "BL_RANGE")
) %>%
build_table(df2)
#> all obs
#> ————————————————
#> low 1/2 (50%)
#> high 1/2 (50%)