The chevron
R package provides functions to produce
standard tables, listings and graphs (TLGs) used to analyze and report
clinical trials data. The ensemble of function used to produce a
particular output are stored in an S4
object of virtual
class chevron_tlg
. Each type of output are associated with
a specific class: chevron_t
for tables,
chevron_l
for listings and chevron_g
for
graphs.
Each standard output is associated with one
chevron_tlg
object. They contain the following
objects in separate slots:
main
function also refereed to as
TLG-function.preprocess
function.postprocess
functionThe TLG-functions in chevron
use other packages
to produce the final outputs, for example rtables
and
tern
are used to create tables, ggplot2
,
lattice
, and grid
are used to create graphs,
rlistings
to create listings.
TLG-functions in chevron
such as
dmt01_main
, aet02_main
,
aet02_main
have the following properties:
GDS
). Note, that the naming convention
<gds template id>_main
indicates that a Roche
GDS
defined standard may have different implementations.
Or, alternatively, a GDS
template id can be regarded as a
guideline and the function name in chevron
as a
standard.adam_db
which is the
collection of ADaM
datasets (ADSL
,
ADAE
, ADRS
, etc.). Please read the The
adam_db
Argument vignette in this package for more
details.The preprocess functions in chevron
use
base
, dplyr
and dunlin
packages
to process input data object and turn them into a suitable input for
TLG-functions.
preprocess in chevron such as dmt01_pre
,
aet02_pre
, aet02_pre
have the following
properties:
list
of data.frame
object
amenable to processing by a TLG-functions. message.adam_db
which is the
collection of ADaM
datasets (ADSL
,
ADAE
, ADRS
, etc.). Please read the The
adam_db
Argument vignette in this package for more
details.Please note that the ultimate responsible person of the preprocessing
functions is the end user. The provided preprocessing function is only a
template and users could modify depending on their need/data. This
preprocessing function will be printed to allow modification in script
generated in citril
.
By default, the Postprocessing function returns its input or a null
report if the input has no rows. postprocessing
function of a chevron_tlg
object must have at least
tlg
as formal arguments.
AET02
For example, the GDS
template aet02
is
implemented in chevron
with the chevropn_tlg
objects that have the name aet02
.
We first load the data as a list
of
data.frame
, where each table represents a domain.
library(chevron)
#> Registered S3 method overwritten by 'tern':
#> method from
#> tidy.glm broom
#>
#> Attaching package: 'chevron'
#> The following object is masked from 'package:rmarkdown':
#>
#> run
data(syn_data, package = "chevron")
A the aet02
output is then created as follows:
run(aet02, syn_data)
#> MedDRA System Organ Class A: Drug X B: Placebo C: Combination
#> MedDRA Preferred Term (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one adverse event 13 (86.7%) 14 (93.3%) 15 (100%)
#> Overall total number of events 58 59 99
#> cl B.2
#> Total number of patients with at least one adverse event 11 (73.3%) 8 (53.3%) 10 (66.7%)
#> Total number of events 18 15 20
#> dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> cl D.1
#> Total number of patients with at least one adverse event 9 (60.0%) 5 (33.3%) 11 (73.3%)
#> Total number of events 13 9 19
#> dcd D.1.1.1.1 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.4.2 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> cl A.1
#> Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> Total number of events 8 11 16
#> dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> cl B.1
#> Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> Total number of events 6 6 12
#> dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> cl C.2
#> Total number of patients with at least one adverse event 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Total number of events 6 4 12
#> dcd C.2.1.2.1 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> cl D.2
#> Total number of patients with at least one adverse event 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> Total number of events 3 5 10
#> dcd D.2.1.5.3 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> cl C.1
#> Total number of patients with at least one adverse event 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Total number of events 4 9 10
#> dcd C.1.1.1.3 4 (26.7%) 4 (26.7%) 5 (33.3%)
The function associated with a particular slot can be retrieved with
the corresponding method: main
, lyt
,
preprocess
postprocess
and
datasets
.
main(aet02)
#> function (adam_db, arm_var = "ACTARM", row_split_var = "AEBODSYS",
#> lbl_overall = NULL, summary_labels = list(all = aet02_label,
#> TOTAL = c(nonunique = "Overall total number of events")),
#> ...)
#> {
#> assert_all_tablenames(adam_db, "adsl", "adae")
#> assert_string(arm_var)
#> assert_character(row_split_var, null.ok = TRUE)
#> assert_string(lbl_overall, null.ok = TRUE)
#> assert_valid_variable(adam_db$adsl, c("USUBJID", arm_var),
#> types = list(c("character", "factor")))
#> assert_valid_variable(adam_db$adae, c(arm_var, row_split_var,
#> "AEDECOD"), types = list(c("character", "factor")))
#> assert_valid_variable(adam_db$adae, "USUBJID", empty_ok = TRUE,
#> types = list(c("character", "factor")))
#> assert_valid_var_pair(adam_db$adsl, adam_db$adae, arm_var)
#> assert_list(summary_labels, null.ok = TRUE)
#> assert_subset(names(summary_labels), c("all", "TOTAL", row_split_var))
#> assert_subset(unique(unlist(lapply(summary_labels, names))),
#> c("unique", "nonunique", "unique_count"))
#> summary_labels <- expand_list(summary_labels, c("TOTAL",
#> row_split_var))
#> lbl_overall <- render_safe(lbl_overall)
#> lbl_row_split <- var_labels_for(adam_db$adae, row_split_var)
#> lbl_aedecod <- var_labels_for(adam_db$adae, "AEDECOD")
#> lyt <- occurrence_lyt(arm_var = arm_var, lbl_overall = lbl_overall,
#> row_split_var = row_split_var, lbl_row_split = lbl_row_split,
#> medname_var = "AEDECOD", lbl_medname_var = lbl_aedecod,
#> summary_labels = summary_labels, count_by = NULL)
#> tbl <- build_table(lyt, adam_db$adae, alt_counts_df = adam_db$adsl)
#> tbl
#> }
#> <bytecode: 0x55bc7e81d480>
#> <environment: namespace:chevron>
These are standard functions that can be used on their own.
chevron_tlg
object customizationIn some instances it is useful to customize the
chevron_tlg
object, for example by changing the pre
processing functions in script generated. Please modify the code
directly inside the pre_fun
, and make sure the function
returns a named list of data frames. Please be careful about the
argument names. The default argument of pre
functions will
be override by the argument in spec.
chevron_tlg
object creationIn some cases, you may want to create a new chevron_tlg
template. To create a chevron_tlg
object from scratch, use
the provided constructors corresponding to the desired output:
chevron_t()
for tables.chevron_l()
for listings.chevron_g()
for graphs.library(rtables)
library(tern)
my_template <- chevron_t(
main = "<your main function to build the table>",
preprocess = "<your pre function to process the data>",
postprocess = "<your post function to add custom sorting>"
)
run(my_template, syn_data)
Note that to ensure the correct execution of the run
function, the name of the first argument of the main
function must be adam_db
; the input list
of
data.frame
object to pre-process. The name of the first
argument of the preprocess
function must be
adam_db
; the input list
object to create
TLG
output and finally, the name of the first argument of
the postprocess
function must be tlg
, the
input TableTree
object to post-process. Validation criteria
enforce these rules upon creation of a chevron_tlg
object.