abnormal {tern} | R Documentation |
Count patients with abnormal range values
Description
The analyze function count_abnormal()
creates a layout element to count patients with abnormal analysis range
values in each direction.
This function analyzes primary analysis variable var
which indicates abnormal range results.
Additional analysis variables that can be supplied as a list via the variables
parameter are
id
(defaults to USUBJID
), a variable to indicate unique subject identifiers, and baseline
(defaults to BNRIND
), a variable to indicate baseline reference ranges.
For each direction specified via the abnormal
parameter (e.g. High or Low), a fraction of
patient counts is returned, with numerator and denominator calculated as follows:
-
num
: The number of patients with this abnormality recorded while on treatment. -
denom
: The total number of patients with at least one post-baseline assessment.
This function assumes that df
has been filtered to only include post-baseline records.
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 = "fraction",
.stat_names = NULL,
.formats = list(fraction = format_fraction),
.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,
...,
.stats = NULL,
.stat_names = NULL,
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)
Arguments
lyt |
( |
abnormal |
(named |
variables |
(named |
exclude_base_abn |
( |
na_str |
( |
nested |
( |
... |
additional arguments for the lower level functions. |
table_names |
( |
.stats |
( Options are: |
.stat_names |
( |
.formats |
(named |
.labels |
(named |
.indent_mods |
(named |
df |
( |
.var , var |
( |
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 considers a single variable that contains multiple abnormal levels. -
df
should be filtered to only include post-baseline records. The denominator includes patients that may have other abnormal levels at baseline, and patients missing baseline records. Patients with these abnormalities at baseline can be optionally excluded from numerator and denominator via the
exclude_base_abn
parameter.
Examples
library(dplyr)
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)
# 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)