Count patients by most extreme post-baseline toxicity grade per direction of abnormality
Source:R/abnormal_by_worst_grade.R
abnormal_by_worst_grade.Rd
The analyze function count_abnormal_by_worst_grade()
creates a layout element to count patients by highest (worst)
analysis toxicity grade post-baseline for each direction, categorized by parameter value.
This function analyzes primary analysis variable var
which indicates toxicity grades. 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, param
(defaults to PARAM
), a variable
to indicate parameter values, and grade_dir
(defaults to GRADE_DIR
), a variable to indicate directions
(e.g. High or Low) for each toxicity grade supplied in var
.
For each combination of param
and grade_dir
levels, patient counts by worst
grade are calculated as follows:
1
to4
: The number of patients with worst grades 1-4, respectively.Any
: The number of patients with at least one abnormality (i.e. grade is not 0).
Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during treatment.
Pre-processing is crucial when using this function and can be done automatically using the
h_adlb_abnormal_by_worst_grade()
helper function. See the description of this function for details on the
necessary pre-processing steps.
Prior to using this function in your table layout you must use rtables::split_rows_by()
to create two row
splits, one on variable param
and one on variable grade_dir
.
Usage
count_abnormal_by_worst_grade(
lyt,
var,
variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR"),
na_str = default_na_str(),
nested = TRUE,
...,
.stats = NULL,
.formats = NULL,
.labels = NULL,
.indent_mods = NULL
)
s_count_abnormal_by_worst_grade(
df,
.var = "GRADE_ANL",
.spl_context,
variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR")
)
a_count_abnormal_by_worst_grade(
df,
.var = "GRADE_ANL",
.spl_context,
variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR")
)
Arguments
- lyt
(
PreDataTableLayouts
)
layout that analyses will be added to.- variables
(named
list
ofstring
)
list of additional analysis variables.- 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.
- .stats
-
(
character
)
statistics to select for the table.Options are:
'count_fraction', 'count_fraction_fixed_dp'
- .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.- .spl_context
(
data.frame
)
gives information about ancestor split states that is passed byrtables
.
Value
count_abnormal_by_worst_grade()
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_by_worst_grade()
to the table layout.
s_count_abnormal_by_worst_grade()
returns the single statisticcount_fraction
with grades 1 to 4 and "Any" results.
a_count_abnormal_by_worst_grade()
returns the corresponding list with formattedrtables::CellValue()
.
Functions
count_abnormal_by_worst_grade()
: Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper forrtables::analyze()
.s_count_abnormal_by_worst_grade()
: Statistics function which counts patients by worst grade.a_count_abnormal_by_worst_grade()
: Formatted analysis function which is used asafun
incount_abnormal_by_worst_grade()
.
See also
h_adlb_abnormal_by_worst_grade()
which pre-processes ADLB data frames to be used in
count_abnormal_by_worst_grade()
.
Examples
library(dplyr)
library(forcats)
adlb <- tern_ex_adlb
# Data is modified in order to have some parameters with grades only in one direction
# and simulate the real data.
adlb$ATOXGR[adlb$PARAMCD == "ALT" & adlb$ATOXGR %in% c("1", "2", "3", "4")] <- "-1"
adlb$ANRIND[adlb$PARAMCD == "ALT" & adlb$ANRIND == "HIGH"] <- "LOW"
adlb$WGRHIFL[adlb$PARAMCD == "ALT"] <- ""
adlb$ATOXGR[adlb$PARAMCD == "IGA" & adlb$ATOXGR %in% c("-1", "-2", "-3", "-4")] <- "1"
adlb$ANRIND[adlb$PARAMCD == "IGA" & adlb$ANRIND == "LOW"] <- "HIGH"
adlb$WGRLOFL[adlb$PARAMCD == "IGA"] <- ""
# Pre-processing
adlb_f <- adlb %>% h_adlb_abnormal_by_worst_grade()
# Map excludes records without abnormal grade since they should not be displayed
# in the table.
map <- unique(adlb_f[adlb_f$GRADE_DIR != "ZERO", c("PARAM", "GRADE_DIR", "GRADE_ANL")]) %>%
lapply(as.character) %>%
as.data.frame() %>%
arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)
basic_table() %>%
split_cols_by("ARMCD") %>%
split_rows_by("PARAM") %>%
split_rows_by("GRADE_DIR", split_fun = trim_levels_to_map(map)) %>%
count_abnormal_by_worst_grade(
var = "GRADE_ANL",
variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR")
) %>%
build_table(df = adlb_f)
#> ARM A ARM B ARM C
#> ———————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement
#> LOW
#> 1 12 (17.4%) 5 (6.8%) 8 (13.8%)
#> 2 9 (13%) 13 (17.8%) 6 (10.3%)
#> 3 6 (8.7%) 4 (5.5%) 6 (10.3%)
#> 4 7 (10.1%) 7 (9.6%) 6 (10.3%)
#> Any 34 (49.3%) 29 (39.7%) 26 (44.8%)
#> C-Reactive Protein Measurement
#> LOW
#> 1 11 (15.9%) 12 (16.4%) 7 (12.1%)
#> 2 8 (11.6%) 2 (2.7%) 6 (10.3%)
#> 3 4 (5.8%) 9 (12.3%) 6 (10.3%)
#> 4 7 (10.1%) 6 (8.2%) 4 (6.9%)
#> Any 30 (43.5%) 29 (39.7%) 23 (39.7%)
#> HIGH
#> 1 8 (11.6%) 11 (15.1%) 2 (3.4%)
#> 2 9 (13%) 11 (15.1%) 11 (19%)
#> 3 14 (20.3%) 10 (13.7%) 5 (8.6%)
#> 4 2 (2.9%) 4 (5.5%) 6 (10.3%)
#> Any 33 (47.8%) 36 (49.3%) 24 (41.4%)
#> Immunoglobulin A Measurement
#> HIGH
#> 1 7 (10.1%) 7 (9.6%) 6 (10.3%)
#> 2 8 (11.6%) 6 (8.2%) 8 (13.8%)
#> 3 7 (10.1%) 5 (6.8%) 9 (15.5%)
#> 4 6 (8.7%) 2 (2.7%) 3 (5.2%)
#> Any 28 (40.6%) 20 (27.4%) 26 (44.8%)