chevron
is a collection of functions to creates tables,
listings, and graphs following Roche standards for clinical trials
reporting. After loading the R packages and the trial data, the output
is to be created by the main function run(...)
. Two
arguments object=
and adam_db=
are always
expected in the function. object=
specifies which Roche
Standard Template ID to use. adam_db=
specifies the input
dataset. Other mandatory and optional arguments within the
run
function vary depending on which template ID is called.
To access which arguments are required and what functions are used in
each template, simply try ?template
(e.g. ?aet01
) to see more detailed descriptions and
instructions.
The input dataset expected by the argument adam_db=
in
the run(...)
function is a collection of ADaM
datasets as a list object. Each ADaM
dataset is expected to
be an object of data frame. If the ADaM
datasets are read
in individually, user will need to combine them into a list object and
provide the name of the list to adam_db=
. Also, each
element in the list are expected to have corresponding ADaM
dataset names. Conventional ADaM
dataset names, including
adsl
,adex
, adae
,
adlb
,advs
,adeg
,adcm
,admh
,adrs
,
and adtte
, can be picked up by chevron
with
one exception.
By default, chevron
does not pull any subject-level
information from either adsl
or adsub
and
merge into the analysis dataset in the underlying preprocessing steps.
The analysis dataset fed into adam_db=
is expected to have
all variables required for analysis available.
In the output generation, we often need to specify a particular
sorting order of a variable at the time of display. In
chevron
, a character variable needs to be factorized with
pre-specified levels to display in order. When encountering cases, for
instance, "ARM A"
has an Asian group only while
"ARM B"
has both Asian and White groups, it is not able to
produce outputs like the demographic table unless "RACE"
is
factorized to provide access to the same level attribute of the variable
"RACE"
after the arm split. It is noted that the feature
comes from rtables
instead of chevron
.
proc_data <- syn_data
proc_data$adsl <- proc_data$adsl %>%
mutate(RACE = case_when(
ARMCD == "ARM A" ~ "ASIAN",
ARMCD == "ARM B" & !.data$RACE %in% c("WHITE", "ASIAN") ~ "ASIAN",
TRUE ~ RACE
))
Having "RACE"
as a character variable rather than a
factor leads to error message showing up as “Error: Error applying
analysis function (var - RACE): Number of rows generated by analysis
function do not match across all columns,” and it is recommended to
convert analysis variable "RACE"
to a factor.
To resolve this issue, simply try factorizing the variable
"RACE"
:
proc_data$adsl$RACE <- as.factor(proc_data$adsl$RACE)
run(dmt01, proc_data)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=15) (N=15) (N=15) (N=45)
#> ————————————————————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 15 15 15 45
#> Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4) 34.3 (7.3)
#> Median 31.0 35.0 35.0 34.0
#> Min - Max 24 - 40 24 - 57 24 - 49 24 - 57
#> Age Group
#> n 15 15 15 45
#> <65 15 (100%) 15 (100%) 15 (100%) 45 (100%)
#> Sex
#> n 15 15 15 45
#> Male 3 (20.0%) 7 (46.7%) 5 (33.3%) 15 (33.3%)
#> Female 12 (80.0%) 8 (53.3%) 10 (66.7%) 30 (66.7%)
#> Ethnicity
#> n 15 15 15 45
#> HISPANIC OR LATINO 2 (13.3%) 0 0 2 (4.4%)
#> NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%) 41 (91.1%)
#> NOT REPORTED 0 0 2 (13.3%) 2 (4.4%)
#> RACE
#> n 15 15 15 45
#> AMERICAN INDIAN OR ALASKA NATIVE 0 0 1 (6.7%) 1 (2.2%)
#> ASIAN 15 (100%) 13 (86.7%) 8 (53.3%) 36 (80.0%)
#> BLACK OR AFRICAN AMERICAN 0 0 4 (26.7%) 4 (8.9%)
#> WHITE 0 2 (13.3%) 2 (13.3%) 4 (8.9%)
The run
function when calling a Graphics Template ID
returns a gTree
object which will be used in the downstream
workflow for output generation. There are two alternative approaches to
rendering the plot: (1) having draw = TRUE
in the
run
function to enable the generated plot to be
automatically created and viewed via the Plots
tab, and (2)
calling the function grid.draw
from the package
grid
which can be utilized to render the plot for viewing
and testing purpose. See example below:
lbl_overall
: Column of TotalThe generic argument lbl_overall
controls whether the
column of total will be produced or not. lbl_overall = NULL
suppresses the total, lbl_overall = "All Patients"
produces
the total.
Column counts are displayed by default. There is no generic argument
controlling whether the count of unique number of subjects (N=xxx) will
be displayed in the column header or not. Users are allowed to customize
the display of N=xxx by forcing
display_columncounts = FALSE
to wipe column counts away
during the postprocessing (with precautions and it is not
recommended).
tbl <- run(dmt01, syn_data) # table with column counts
tbl@col_info@display_columncounts <- FALSE
tbl # no column counts now
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=15) (N=15) (N=15) (N=45)
#> ————————————————————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 15 15 15 45
#> Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4) 34.3 (7.3)
#> Median 31.0 35.0 35.0 34.0
#> Min - Max 24 - 40 24 - 57 24 - 49 24 - 57
#> Age Group
#> n 15 15 15 45
#> <65 15 (100%) 15 (100%) 15 (100%) 45 (100%)
#> Sex
#> n 15 15 15 45
#> Male 3 (20.0%) 7 (46.7%) 5 (33.3%) 15 (33.3%)
#> Female 12 (80.0%) 8 (53.3%) 10 (66.7%) 30 (66.7%)
#> Ethnicity
#> n 15 15 15 45
#> HISPANIC OR LATINO 2 (13.3%) 0 0 2 (4.4%)
#> NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%) 41 (91.1%)
#> NOT REPORTED 0 0 2 (13.3%) 2 (4.4%)
#> RACE
#> n 15 15 15 45
#> AMERICAN INDIAN OR ALASKA NATIVE 0 2 (13.3%) 1 (6.7%) 3 (6.7%)
#> ASIAN 8 (53.3%) 10 (66.7%) 8 (53.3%) 26 (57.8%)
#> BLACK OR AFRICAN AMERICAN 4 (26.7%) 1 (6.7%) 4 (26.7%) 9 (20.0%)
#> WHITE 3 (20.0%) 2 (13.3%) 2 (13.3%) 7 (15.6%)
AET01
)The aet01
template produces the
standard safety summary.
run(aet01, syn_data, arm_var = "ARM")
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one AE 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of AEs 58 59 99
#> Total number of deaths 2 (13.3%) 4 (26.7%) 3 (20.0%)
#> Total number of patients withdrawn from study due to an AE 0 0 1 (6.7%)
#> Total number of patients with at least one
#> AE with fatal outcome 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> Serious AE 12 (80.0%) 12 (80.0%) 11 (73.3%)
#> Serious AE leading to withdrawal from treatment 0 0 2 (13.3%)
#> Serious AE leading to dose modification/interruption 4 (26.7%) 3 (20.0%) 4 (26.7%)
#> Related Serious AE 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> AE leading to withdrawal from treatment 2 (13.3%) 3 (20.0%) 3 (20.0%)
#> AE leading to dose modification/interruption 6 (40.0%) 9 (60.0%) 11 (73.3%)
#> Related AE 11 (73.3%) 10 (66.7%) 13 (86.7%)
#> Related AE leading to withdrawal from treatment 0 3 (20.0%) 0
#> Related AE leading to dose modification/interruption 1 (6.7%) 4 (26.7%) 9 (60.0%)
#> Severe AE (at greatest intensity) 11 (73.3%) 10 (66.7%) 12 (80.0%)
Analyses under “Total number of patients with at least one” can be
removed, added, or modified by editing the parameter
anl_vars
. An analysis here is an abbreviated name of the
analysis of interest, and supported by a variable in ADAE
derived under the condition of interest. The defined analyses currently
include "FATAL"
, "SER"
, "SERWD"
,
"SERDSM"
, "RELSER"
, "WD"
,
"DSM"
, "REL"
, "RELWD"
,
"RELDSM"
, and "SEV"
. When modification is
made, analyses must all be listed in the argument anl_vars
.
The example below shows adding the customized analysis
"RELCTC35"
.
proc_data <- syn_data
proc_data$adae <- proc_data$adae %>%
filter(.data$ANL01FL == "Y") %>%
mutate(
FATAL = with_label(.data$AESDTH == "Y", "AE with fatal outcome"),
SER = with_label(.data$AESER == "Y", "Serious AE"),
SEV = with_label(.data$ASEV == "SEVERE", "Severe AE (at greatest intensity)"),
REL = with_label(.data$AREL == "Y", "Related AE"),
WD = with_label(.data$AEACN == "DRUG WITHDRAWN", "AE leading to withdrawal from treatment"),
DSM = with_label(
.data$AEACN %in% c("DRUG INTERRUPTED", "DOSE INCREASED", "DOSE REDUCED"),
"AE leading to dose modification/interruption"
),
SERWD = with_label(.data$SER & .data$WD, "Serious AE leading to withdrawal from treatment"),
SERDSM = with_label(.data$SER & .data$DSM, "Serious AE leading to dose modification/interruption"),
RELSER = with_label(.data$SER & .data$REL, "Related Serious AE"),
RELWD = with_label(.data$REL & .data$WD, "Related AE leading to withdrawal from treatment"),
RELDSM = with_label(.data$REL & .data$DSM, "Related AE leading to dose modification/interruption"),
CTC35 = with_label(.data$ATOXGR %in% c("3", "4", "5"), "Grade 3-5 AE"),
CTC45 = with_label(.data$ATOXGR %in% c("4", "5"), "Grade 4/5 AE"),
RELCTC35 = with_label(.data$ATOXGR %in% c("3", "4", "5") & .data$AEREL == "Y", "Related Grade 3-5")
)
proc_data$adsl <- proc_data$adsl %>%
mutate(DCSREAS = reformat(.data$DCSREAS, missing_rule))
run(aet01, proc_data, anl_vars = list(safety_var = c("FATAL", "SER", "RELSER", "RELCTC35")), auto_pre = FALSE)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one AE 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of AEs 58 59 99
#> Total number of deaths 2 (13.3%) 4 (26.7%) 3 (20.0%)
#> Total number of patients withdrawn from study due to an AE 0 0 1 (6.7%)
#> Total number of patients with at least one
#> AE with fatal outcome 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> Serious AE 12 (80.0%) 12 (80.0%) 11 (73.3%)
#> Related Serious AE 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> Related Grade 3-5 11 (73.3%) 10 (66.7%) 12 (80.0%)
AET01_AESI
)The aet01_aesi
template produces the
standard safety summary for adverse events of special interest.
run(aet01_aesi, syn_data)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one AE 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of AEs 58 59 99
#> Total number of patients with at least one AE by worst grade
#> Grade 1 0 1 (6.7%) 1 (6.7%)
#> Grade 2 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> Grade 3 1 (6.7%) 2 (13.3%) 1 (6.7%)
#> Grade 4 3 (20.0%) 2 (13.3%) 2 (13.3%)
#> Grade 5 (fatal outcome) 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> Total number of patients with study drug withdrawn due to AE 2 (13.3%) 3 (20.0%) 3 (20.0%)
#> Total number of patients with dose modified/interrupted due to AE 6 (40.0%) 9 (60.0%) 11 (73.3%)
#> Total number of patients with treatment received for AE 10 (66.7%) 10 (66.7%) 14 (93.3%)
#> Total number of patients with all non-fatal AEs resolved 9 (60.0%) 10 (66.7%) 12 (80.0%)
#> Total number of patients with at least one unresolved or ongoing non-fatal AE 10 (66.7%) 9 (60.0%) 14 (93.3%)
#> Total number of patients with at least one serious AE 12 (80.0%) 12 (80.0%) 11 (73.3%)
#> Total number of patients with at least one related AE 11 (73.3%) 10 (66.7%) 13 (86.7%)
Additional analyses can be added with the argument
aesi_vars
, please type ?aet01_aesi
in console
to find out the list of all pre-defined optional analyses in the
HELP.
run(aet01_aesi, syn_data, aesi_vars = c("RESLWD", "RELSER"))
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one AE 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of AEs 58 59 99
#> Total number of patients with at least one AE by worst grade
#> Grade 1 0 1 (6.7%) 1 (6.7%)
#> Grade 2 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> Grade 3 1 (6.7%) 2 (13.3%) 1 (6.7%)
#> Grade 4 3 (20.0%) 2 (13.3%) 2 (13.3%)
#> Grade 5 (fatal outcome) 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> Total number of patients with study drug withdrawn due to AE 2 (13.3%) 3 (20.0%) 3 (20.0%)
#> Total number of patients with dose modified/interrupted due to AE 6 (40.0%) 9 (60.0%) 11 (73.3%)
#> Total number of patients with treatment received for AE 10 (66.7%) 10 (66.7%) 14 (93.3%)
#> Total number of patients with all non-fatal AEs resolved 9 (60.0%) 10 (66.7%) 12 (80.0%)
#> Total number of patients with at least one unresolved or ongoing non-fatal AE 10 (66.7%) 9 (60.0%) 14 (93.3%)
#> Total number of patients with at least one serious AE 12 (80.0%) 12 (80.0%) 11 (73.3%)
#> Total number of patients with at least one related AE 11 (73.3%) 10 (66.7%) 13 (86.7%)
#> No. of patients with serious, related AE 8 (53.3%) 8 (53.3%) 10 (66.7%)
For studies with more than one study drug, users need to define the
analyses in adae
and add to the argument
aesi_vars
following the example above. No pre-defined
analysis is available at this moment.
AET02
)aet02
produces the standard adverse event
summary by MedDRA system organ class and preferred term.lbl_overall = "All Patients"
."AEBODSYS"
, and
"AEDECOD"
are labeled as
No Coding Available
.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 syntax below displays adverse events by MedDRA system organ class, high-level term and preferred term.
run(aet02, syn_data, row_split_var = c("AEBODSYS", "AEHLT"))
#> MedDRA System Organ Class
#> High Level Term 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
#> hlt B.2.2.3
#> Total number of patients with at least one adverse event 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> Total number of events 9 7 13
#> dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> hlt B.2.1.2
#> Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> Total number of events 9 8 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
#> hlt D.1.1.1
#> Total number of patients with at least one adverse event 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> Total number of events 5 7 11
#> dcd D.1.1.1.1 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> hlt D.1.1.4
#> Total number of patients with at least one adverse event 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> Total number of events 8 2 8
#> 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
#> hlt A.1.1.1
#> 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
#> hlt B.1.1.1
#> 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
#> hlt C.2.1.2
#> 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
#> hlt D.2.1.5
#> 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
#> hlt C.1.1.1
#> 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 syntax below displays adverse events by preferred term only.
run(aet02, syn_data, row_split_var = NULL)
#> 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
#> dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> dcd C.2.1.2.1 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 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%)
#> dcd D.2.1.5.3 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> dcd C.1.1.1.3 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
AET03
)This aet03
template produces the
standard adverse event by greatest intensity summary
run(aet03, syn_data)
#> MedDRA System Organ Class A: Drug X B: Placebo C: Combination
#> MedDRA Preferred Term (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————
#> - Any Intensity - 13 (86.7%) 14 (93.3%) 15 (100%)
#> MILD 0 1 (6.7%) 1 (6.7%)
#> MODERATE 2 (13.3%) 3 (20.0%) 2 (13.3%)
#> SEVERE 11 (73.3%) 10 (66.7%) 12 (80.0%)
#> cl B.2
#> - Any Intensity - 11 (73.3%) 8 (53.3%) 10 (66.7%)
#> MILD 6 (40.0%) 2 (13.3%) 5 (33.3%)
#> MODERATE 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> dcd B.2.2.3.1
#> - Any Intensity - 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> MILD 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> dcd B.2.1.2.1
#> - Any Intensity - 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> MODERATE 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> cl D.1
#> - Any Intensity - 9 (60.0%) 5 (33.3%) 11 (73.3%)
#> MODERATE 5 (33.3%) 1 (6.7%) 4 (26.7%)
#> SEVERE 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.1.1
#> - Any Intensity - 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> SEVERE 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.4.2
#> - Any Intensity - 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> MODERATE 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> cl A.1
#> - Any Intensity - 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> MILD 2 (13.3%) 0 4 (26.7%)
#> MODERATE 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> dcd A.1.1.1.2
#> - Any Intensity - 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> MODERATE 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> dcd A.1.1.1.1
#> - Any Intensity - 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> MILD 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> cl B.1
#> - Any Intensity - 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> SEVERE 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> dcd B.1.1.1.1
#> - Any Intensity - 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> SEVERE 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> cl C.2
#> - Any Intensity - 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> MODERATE 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> dcd C.2.1.2.1
#> - Any Intensity - 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> MODERATE 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> cl D.2
#> - Any Intensity - 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> MILD 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> dcd D.2.1.5.3
#> - Any Intensity - 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> MILD 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> cl C.1
#> - Any Intensity - 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> SEVERE 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> dcd C.1.1.1.3
#> - Any Intensity - 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> SEVERE 4 (26.7%) 4 (26.7%) 5 (33.3%)
NCI CTCAE
Grade
(AET04
)NCI CTCAE
Gradeaet04
template produces the
standard adverse event by highest NCI CTCAE
grade
summary.ADSL
.run(aet04, syn_data)
#> MedDRA System Organ Class
#> MedDRA Preferred Term A: Drug X B: Placebo C: Combination
#> Grade (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————
#> - Any adverse events -
#> - Any Grade - 13 (86.7%) 14 (93.3%) 15 (100%)
#> Grade 1-2 1 (6.7%) 2 (13.3%) 2 (13.3%)
#> 1 0 1 (6.7%) 1 (6.7%)
#> 2 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> Grade 3-4 4 (26.7%) 4 (26.7%) 3 (20.0%)
#> 3 1 (6.7%) 2 (13.3%) 1 (6.7%)
#> 4 3 (20.0%) 2 (13.3%) 2 (13.3%)
#> Grade 5 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> cl B.2
#> - Overall -
#> - Any Grade - 11 (73.3%) 8 (53.3%) 10 (66.7%)
#> Grade 1-2 6 (40.0%) 2 (13.3%) 5 (33.3%)
#> 1 6 (40.0%) 2 (13.3%) 5 (33.3%)
#> Grade 3-4 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 3 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> dcd B.2.2.3.1
#> - Any Grade - 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> Grade 1-2 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> 1 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> dcd B.2.1.2.1
#> - Any Grade - 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> Grade 3-4 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 3 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> cl D.1
#> - Overall -
#> - Any Grade - 9 (60.0%) 5 (33.3%) 11 (73.3%)
#> Grade 3-4 5 (33.3%) 1 (6.7%) 4 (26.7%)
#> 3 5 (33.3%) 1 (6.7%) 4 (26.7%)
#> Grade 5 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.1.1
#> - Any Grade - 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> Grade 5 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.4.2
#> - Any Grade - 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> Grade 3-4 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> 3 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> cl A.1
#> - Overall -
#> - Any Grade - 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> Grade 1-2 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> 1 2 (13.3%) 0 4 (26.7%)
#> 2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> dcd A.1.1.1.2
#> - Any Grade - 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> Grade 1-2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> 2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> dcd A.1.1.1.1
#> - Any Grade - 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> Grade 1-2 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> 1 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> cl B.1
#> - Overall -
#> - Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> dcd B.1.1.1.1
#> - Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> cl C.2
#> - Overall -
#> - Any Grade - 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 1-2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> 2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> dcd C.2.1.2.1
#> - Any Grade - 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 1-2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> 2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> cl D.2
#> - Overall -
#> - Any Grade - 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> Grade 1-2 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 1 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> dcd D.2.1.5.3
#> - Any Grade - 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> Grade 1-2 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 1 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> cl C.1
#> - Overall -
#> - Any Grade - 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Grade 3-4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> 4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> dcd C.1.1.1.3
#> - Any Grade - 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Grade 3-4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> 4 4 (26.7%) 4 (26.7%) 5 (33.3%)
NCI CTCAE
Grade
(Fill in of Grades)If, for some preferred terms, not all grades occur but all grades
should be displayed, this can be achieved by specifying the argument
prune_0 = FALSE
.
run(aet04, syn_data, prune_0 = FALSE)
#> MedDRA System Organ Class
#> MedDRA Preferred Term A: Drug X B: Placebo C: Combination
#> Grade (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————
#> - Any adverse events -
#> - Any Grade - 13 (86.7%) 14 (93.3%) 15 (100%)
#> Grade 1-2 1 (6.7%) 2 (13.3%) 2 (13.3%)
#> 1 0 1 (6.7%) 1 (6.7%)
#> 2 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> Grade 3-4 4 (26.7%) 4 (26.7%) 3 (20.0%)
#> 3 1 (6.7%) 2 (13.3%) 1 (6.7%)
#> 4 3 (20.0%) 2 (13.3%) 2 (13.3%)
#> Grade 5 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> cl B.2
#> - Overall -
#> - Any Grade - 11 (73.3%) 8 (53.3%) 10 (66.7%)
#> Grade 1-2 6 (40.0%) 2 (13.3%) 5 (33.3%)
#> 1 6 (40.0%) 2 (13.3%) 5 (33.3%)
#> 2 0 0 0
#> Grade 3-4 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 3 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 4 0 0 0
#> Grade 5 0 0 0
#> dcd B.2.2.3.1
#> - Any Grade - 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> Grade 1-2 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> 1 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> 2 0 0 0
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 0 0 0
#> dcd B.2.1.2.1
#> - Any Grade - 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-4 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 3 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 4 0 0 0
#> Grade 5 0 0 0
#> cl D.1
#> - Overall -
#> - Any Grade - 9 (60.0%) 5 (33.3%) 11 (73.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-4 5 (33.3%) 1 (6.7%) 4 (26.7%)
#> 3 5 (33.3%) 1 (6.7%) 4 (26.7%)
#> 4 0 0 0
#> Grade 5 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.1.1
#> - Any Grade - 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.4.2
#> - Any Grade - 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-4 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> 3 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> 4 0 0 0
#> Grade 5 0 0 0
#> cl A.1
#> - Overall -
#> - Any Grade - 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> Grade 1-2 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> 1 2 (13.3%) 0 4 (26.7%)
#> 2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 0 0 0
#> dcd A.1.1.1.2
#> - Any Grade - 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> Grade 1-2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> 1 0 0 0
#> 2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 0 0 0
#> dcd A.1.1.1.1
#> - Any Grade - 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> Grade 1-2 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> 1 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> 2 0 0 0
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 0 0 0
#> cl B.1
#> - Overall -
#> - Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> dcd B.1.1.1.1
#> - Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> cl C.2
#> - Overall -
#> - Any Grade - 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 1-2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> 1 0 0 0
#> 2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 0 0 0
#> dcd C.2.1.2.1
#> - Any Grade - 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 1-2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> 1 0 0 0
#> 2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 0 0 0
#> cl D.2
#> - Overall -
#> - Any Grade - 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> Grade 1-2 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 1 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 2 0 0 0
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 0 0 0
#> dcd D.2.1.5.3
#> - Any Grade - 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> Grade 1-2 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 1 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 2 0 0 0
#> Grade 3-4 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Grade 5 0 0 0
#> cl C.1
#> - Overall -
#> - Any Grade - 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> 3 0 0 0
#> 4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Grade 5 0 0 0
#> dcd C.1.1.1.3
#> - Any Grade - 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> 3 0 0 0
#> 4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Grade 5 0 0 0
NCI CTCAE
Grade
with modified grouping of gradeCollapsing grade 3-4 with grade 5, can be achieved by modifying the
definition of grade groups in the argument
grade_groups
.
grade_groups <- list(
"Grade 1-2" = c("1", "2"),
"Grade 3-5" = c("3", "4", "5")
)
run(aet04, syn_data, grade_groups = grade_groups, prune_0 = FALSE)
#> MedDRA System Organ Class
#> MedDRA Preferred Term A: Drug X B: Placebo C: Combination
#> Grade (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————
#> - Any adverse events -
#> - Any Grade - 13 (86.7%) 14 (93.3%) 15 (100%)
#> Grade 1-2 1 (6.7%) 2 (13.3%) 2 (13.3%)
#> 1 0 1 (6.7%) 1 (6.7%)
#> 2 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> Grade 3-5 12 (80.0%) 12 (80.0%) 13 (86.7%)
#> 3 1 (6.7%) 2 (13.3%) 1 (6.7%)
#> 4 3 (20.0%) 2 (13.3%) 2 (13.3%)
#> 5 8 (53.3%) 8 (53.3%) 10 (66.7%)
#> cl B.2
#> - Overall -
#> - Any Grade - 11 (73.3%) 8 (53.3%) 10 (66.7%)
#> Grade 1-2 6 (40.0%) 2 (13.3%) 5 (33.3%)
#> 1 6 (40.0%) 2 (13.3%) 5 (33.3%)
#> 2 0 0 0
#> Grade 3-5 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 3 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 4 0 0 0
#> 5 0 0 0
#> dcd B.2.2.3.1
#> - Any Grade - 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> Grade 1-2 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> 1 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> 2 0 0 0
#> Grade 3-5 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> dcd B.2.1.2.1
#> - Any Grade - 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-5 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 3 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 4 0 0 0
#> 5 0 0 0
#> cl D.1
#> - Overall -
#> - Any Grade - 9 (60.0%) 5 (33.3%) 11 (73.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-5 9 (60.0%) 5 (33.3%) 11 (73.3%)
#> 3 5 (33.3%) 1 (6.7%) 4 (26.7%)
#> 4 0 0 0
#> 5 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.1.1
#> - Any Grade - 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-5 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> 3 0 0 0
#> 4 0 0 0
#> 5 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> dcd D.1.1.4.2
#> - Any Grade - 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-5 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> 3 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> 4 0 0 0
#> 5 0 0 0
#> cl A.1
#> - Overall -
#> - Any Grade - 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> Grade 1-2 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> 1 2 (13.3%) 0 4 (26.7%)
#> 2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> Grade 3-5 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> dcd A.1.1.1.2
#> - Any Grade - 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> Grade 1-2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> 1 0 0 0
#> 2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> Grade 3-5 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> dcd A.1.1.1.1
#> - Any Grade - 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> Grade 1-2 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> 1 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> 2 0 0 0
#> Grade 3-5 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> cl B.1
#> - Overall -
#> - Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-5 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> 3 0 0 0
#> 4 0 0 0
#> 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> dcd B.1.1.1.1
#> - Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-5 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> 3 0 0 0
#> 4 0 0 0
#> 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> cl C.2
#> - Overall -
#> - Any Grade - 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 1-2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> 1 0 0 0
#> 2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 3-5 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> dcd C.2.1.2.1
#> - Any Grade - 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 1-2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> 1 0 0 0
#> 2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> Grade 3-5 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> cl D.2
#> - Overall -
#> - Any Grade - 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> Grade 1-2 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 1 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 2 0 0 0
#> Grade 3-5 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> dcd D.2.1.5.3
#> - Any Grade - 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> Grade 1-2 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 1 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> 2 0 0 0
#> Grade 3-5 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> cl C.1
#> - Overall -
#> - Any Grade - 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-5 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> 3 0 0 0
#> 4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> 5 0 0 0
#> dcd C.1.1.1.3
#> - Any Grade - 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> Grade 1-2 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> Grade 3-5 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> 3 0 0 0
#> 4 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> 5 0 0 0
AET05
)aet05
template produces the
standard adverse event rate adjusted for patient-years at risk summary
considering first occurrence only.adsaftte
parameter codes containing the
string "TTE"
are included in the output. Users are expected
to filter the parameter(s) of interest from input safety time-to-event
dataset in pre-processing if needed.CNSR
, 0
indicates the occurrence of an event
of interest and 1
denotes censoring.proc_data <- log_filter(syn_data, PARAMCD == "AETTE1", "adsaftte")
run(aet05, proc_data)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————————————————
#> Time to first occurrence of any adverse event
#> Total patient-years at risk 31.0 9.0 22.0
#> Number of adverse events observed 5 13 8
#> AE rate per 100 patient-years 16.13 143.75 36.30
#> 95% CI (1.99, 30.27) (65.61, 221.89) (11.15, 61.45)
conf_type
. Options include normal
(default), normal_log
and exact
.conf_level
.run(aet05, syn_data, conf_level = 0.90, conf_type = "exact")
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Time to first occurrence of a grade 3-5 adverse event
#> Total patient-years at risk 10.3 6.3 8.3
#> Number of adverse events observed 12 14 13
#> AE rate per 100 patient-years 116.36 223.74 156.98
#> 90% CI (67.14, 188.53) (135.27, 349.78) (92.86, 249.59)
#> Time to first occurrence of any adverse event
#> Total patient-years at risk 31.0 9.0 22.0
#> Number of adverse events observed 5 13 8
#> AE rate per 100 patient-years 16.13 143.75 36.30
#> 90% CI (6.36, 33.91) (85.03, 228.55) (18.06, 65.50)
#> Time to first occurrence of any serious adverse event
#> Total patient-years at risk 32.9 7.6 9.4
#> Number of adverse events observed 4 14 13
#> AE rate per 100 patient-years 12.15 183.83 137.79
#> 90% CI (4.15, 27.80) (111.14, 287.38) (81.50, 219.06)
AET05_ALL
)aet05_all
template produces the
standard adverse event rate adjusted for patient-years at risk summary
considering all occurrences.adsaftte
parameter codes containing the
string "TOT"
and the parameter code "AEREPTTE"
are required. "TOT"
parameters store the number of
occurrences of adverse event of interests. Parameter code
"AEREPTTE"
stores the time to end of adverse event
reporting period in years that contribute to the summary of “total
patient-years at risk” in the output. Users are expected to filter
parameters of interest from input analysis dataset in pre-processing, if
needed.CNSR
, 0
indicates the occurrence of an event
of interest and 1
denotes censoring.proc_data <- log_filter(syn_data, PARAMCD == "AETOT1" | PARAMCD == "AEREPTTE", "adsaftte")
run(aet05_all, proc_data)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————————————————
#> Number of occurrences of any adverse event
#> Total patient-years at risk 44.4 44.2 44.4
#> Number of adverse events observed 29 49 56
#> AE rate per 100 patient-years 65.32 110.76 126.15
#> 95% CI (41.54, 89.09) (79.75, 141.77) (93.11, 159.19)
conf_type
. Options include normal
(default), normal_log
, exact
, and
byar
.conf_level
.run(aet05_all, syn_data, conf_level = 0.90, conf_type = "exact")
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Number of occurrences of a grade 3-5 adverse event
#> Total patient-years at risk 44.4 44.2 44.4
#> Number of adverse events observed 65 54 95
#> AE rate per 100 patient-years 146.40 122.06 214.00
#> 90% CI (117.86, 179.97) (96.08, 153.12) (179.22, 253.80)
#> Number of occurrences of any adverse event
#> Total patient-years at risk 44.4 44.2 44.4
#> Number of adverse events observed 29 49 56
#> AE rate per 100 patient-years 65.32 110.76 126.15
#> 90% CI (46.73, 89.06) (86.08, 140.53) (99.76, 157.60)
#> Number of occurrences of any serious adverse event
#> Total patient-years at risk 44.4 44.2 44.4
#> Number of adverse events observed 9 36 60
#> AE rate per 100 patient-years 20.27 81.37 135.16
#> 90% CI (10.57, 35.37) (60.42, 107.46) (107.80, 167.58)
AET10
)aet10
template produces the
standard most common adverse events occurring with relative frequency
>=5% output.run(aet10, syn_data)
#> A: Drug X B: Placebo C: Combination
#> MedDRA Preferred Term (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————
#> dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> dcd C.2.1.2.1 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 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%)
#> dcd D.2.1.5.3 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> dcd C.1.1.1.3 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
To modify the threshold for displaying preferred terms, this can be
achieved by providing the threshold to the argument
atleast
.
run(aet10, syn_data, atleast = 0.08)
#> A: Drug X B: Placebo C: Combination
#> MedDRA Preferred Term (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————
#> dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> dcd C.2.1.2.1 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> 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%)
#> dcd D.2.1.5.3 2 (13.3%) 5 (33.3%) 7 (46.7%)
#> dcd C.1.1.1.3 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
CMT01A
)cmt01a
template displays
concomitant medications by ATC Level 2
and Preferred Name
by default.run(cmt01a, syn_data)
#> ATC Level 2 Text A: Drug X B: Placebo C: Combination
#> Other Treatment (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one treatment 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of treatments 58 59 99
#> ATCCLAS2 A
#> Total number of patients with at least one treatment 10 (66.7%) 11 (73.3%) 12 (80.0%)
#> Total number of treatments 15 21 28
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> medname A_2/3 5 (33.3%) 6 (40.0%) 7 (46.7%)
#> medname A_1/3 4 (26.7%) 3 (20.0%) 8 (53.3%)
#> ATCCLAS2 A p2
#> Total number of patients with at least one treatment 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> Total number of treatments 6 8 8
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> ATCCLAS2 B
#> Total number of patients with at least one treatment 12 (80.0%) 10 (66.7%) 14 (93.3%)
#> Total number of treatments 30 30 52
#> medname B_3/4 8 (53.3%) 6 (40.0%) 8 (53.3%)
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> medname B_4/4 4 (26.7%) 5 (33.3%) 8 (53.3%)
#> ATCCLAS2 B p2
#> Total number of patients with at least one treatment 10 (66.7%) 8 (53.3%) 12 (80.0%)
#> Total number of treatments 18 17 25
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> ATCCLAS2 B p3
#> Total number of patients with at least one treatment 10 (66.7%) 8 (53.3%) 12 (80.0%)
#> Total number of treatments 18 17 25
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> ATCCLAS2 C
#> Total number of patients with at least one treatment 9 (60.0%) 7 (46.7%) 12 (80.0%)
#> Total number of treatments 13 8 19
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
#> ATCCLAS2 C p2
#> Total number of patients with at least one treatment 9 (60.0%) 7 (46.7%) 12 (80.0%)
#> Total number of treatments 13 8 19
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
#> ATCCLAS2 C p3
#> Total number of patients with at least one treatment 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> Total number of treatments 5 5 12
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
ATC class level
)run(cmt01a, syn_data, row_split_var = "ATC1")
#> ATC Level 1 Text A: Drug X B: Placebo C: Combination
#> Other Treatment (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one treatment 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of treatments 58 59 99
#> ATCCLAS1 A
#> Total number of patients with at least one treatment 10 (66.7%) 11 (73.3%) 12 (80.0%)
#> Total number of treatments 15 21 28
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> medname A_2/3 5 (33.3%) 6 (40.0%) 7 (46.7%)
#> medname A_1/3 4 (26.7%) 3 (20.0%) 8 (53.3%)
#> ATCCLAS1 A p2
#> Total number of patients with at least one treatment 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> Total number of treatments 6 8 8
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> ATCCLAS1 B
#> Total number of patients with at least one treatment 12 (80.0%) 10 (66.7%) 14 (93.3%)
#> Total number of treatments 30 30 52
#> medname B_3/4 8 (53.3%) 6 (40.0%) 8 (53.3%)
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> medname B_4/4 4 (26.7%) 5 (33.3%) 8 (53.3%)
#> ATCCLAS1 B p2
#> Total number of patients with at least one treatment 10 (66.7%) 8 (53.3%) 12 (80.0%)
#> Total number of treatments 18 17 25
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> ATCCLAS1 B p3
#> Total number of patients with at least one treatment 10 (66.7%) 8 (53.3%) 12 (80.0%)
#> Total number of treatments 18 17 25
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> ATCCLAS1 C
#> Total number of patients with at least one treatment 9 (60.0%) 7 (46.7%) 12 (80.0%)
#> Total number of treatments 13 8 19
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
#> ATCCLAS1 C p2
#> Total number of patients with at least one treatment 9 (60.0%) 7 (46.7%) 12 (80.0%)
#> Total number of treatments 13 8 19
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
#> ATCCLAS1 C p3
#> Total number of patients with at least one treatment 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> Total number of treatments 5 5 12
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
The argument sort_by_freq = TRUE
sort medication class
by frequency.
run(cmt01a, syn_data, sort_by_freq = TRUE)
#> ATC Level 2 Text A: Drug X B: Placebo C: Combination
#> Other Treatment (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one treatment 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of treatments 58 59 99
#> ATCCLAS2 B
#> Total number of patients with at least one treatment 12 (80.0%) 10 (66.7%) 14 (93.3%)
#> Total number of treatments 30 30 52
#> medname B_3/4 8 (53.3%) 6 (40.0%) 8 (53.3%)
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> medname B_4/4 4 (26.7%) 5 (33.3%) 8 (53.3%)
#> ATCCLAS2 A
#> Total number of patients with at least one treatment 10 (66.7%) 11 (73.3%) 12 (80.0%)
#> Total number of treatments 15 21 28
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> medname A_2/3 5 (33.3%) 6 (40.0%) 7 (46.7%)
#> medname A_1/3 4 (26.7%) 3 (20.0%) 8 (53.3%)
#> ATCCLAS2 B p2
#> Total number of patients with at least one treatment 10 (66.7%) 8 (53.3%) 12 (80.0%)
#> Total number of treatments 18 17 25
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> ATCCLAS2 B p3
#> Total number of patients with at least one treatment 10 (66.7%) 8 (53.3%) 12 (80.0%)
#> Total number of treatments 18 17 25
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> ATCCLAS2 C
#> Total number of patients with at least one treatment 9 (60.0%) 7 (46.7%) 12 (80.0%)
#> Total number of treatments 13 8 19
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
#> ATCCLAS2 C p2
#> Total number of patients with at least one treatment 9 (60.0%) 7 (46.7%) 12 (80.0%)
#> Total number of treatments 13 8 19
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
#> ATCCLAS2 A p2
#> Total number of patients with at least one treatment 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> Total number of treatments 6 8 8
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> ATCCLAS2 C p3
#> Total number of patients with at least one treatment 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> Total number of treatments 5 5 12
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
The cmt01a
template includes the
analysis of ‘total number of treatments’ by default, modify the argument
summary_labels
to change it.
run(cmt01a, syn_data, summary_labels = list(TOTAL = cmt01_label, ATC2 = cmt01_label[1]))
#> ATC Level 2 Text A: Drug X B: Placebo C: Combination
#> Other Treatment (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one treatment 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of treatments 58 59 99
#> ATCCLAS2 A
#> Total number of patients with at least one treatment 10 (66.7%) 11 (73.3%) 12 (80.0%)
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> medname A_2/3 5 (33.3%) 6 (40.0%) 7 (46.7%)
#> medname A_1/3 4 (26.7%) 3 (20.0%) 8 (53.3%)
#> ATCCLAS2 A p2
#> Total number of patients with at least one treatment 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> ATCCLAS2 B
#> Total number of patients with at least one treatment 12 (80.0%) 10 (66.7%) 14 (93.3%)
#> medname B_3/4 8 (53.3%) 6 (40.0%) 8 (53.3%)
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> medname B_4/4 4 (26.7%) 5 (33.3%) 8 (53.3%)
#> ATCCLAS2 B p2
#> Total number of patients with at least one treatment 10 (66.7%) 8 (53.3%) 12 (80.0%)
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> ATCCLAS2 B p3
#> Total number of patients with at least one treatment 10 (66.7%) 8 (53.3%) 12 (80.0%)
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> ATCCLAS2 C
#> Total number of patients with at least one treatment 9 (60.0%) 7 (46.7%) 12 (80.0%)
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
#> ATCCLAS2 C p2
#> Total number of patients with at least one treatment 9 (60.0%) 7 (46.7%) 12 (80.0%)
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
#> ATCCLAS2 C p3
#> Total number of patients with at least one treatment 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
CMT02_PT
)cmt02_pt
template displays
concomitant medications by Preferred Name by default.sort_by_freq = TRUE
to sort preferred
names by frequency.run(cmt02_pt, syn_data)
#> A: Drug X B: Placebo C: Combination
#> Other Treatment (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one treatment 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of treatments 58 59 99
#> medname B_3/4 8 (53.3%) 6 (40.0%) 8 (53.3%)
#> medname B_2/4 6 (40.0%) 5 (33.3%) 10 (66.7%)
#> medname A_3/3 5 (33.3%) 8 (53.3%) 6 (40.0%)
#> medname B_1/4 7 (46.7%) 6 (40.0%) 6 (40.0%)
#> medname A_2/3 5 (33.3%) 6 (40.0%) 7 (46.7%)
#> medname B_4/4 4 (26.7%) 5 (33.3%) 8 (53.3%)
#> medname C_2/2 4 (26.7%) 5 (33.3%) 7 (46.7%)
#> medname A_1/3 4 (26.7%) 3 (20.0%) 8 (53.3%)
#> medname C_1/2 6 (40.0%) 2 (13.3%) 6 (40.0%)
COXT01
)coxt01
template produces the
standard Cox regression output.time_var
argument. By default, time_var
is set
to "AVAL"
, which comes from ADTTE.AVAL
.event_var
argument. By default, event_var
is
set to "EVENT"
, which is derived based on the censoring
indicator ADTTE.CNSR
in the pre-processing function
coxt01_pre
."Arm A"
and "Arm B"
.proc_data <- log_filter(syn_data, PARAMCD == "OS", "adtte")
proc_data <- log_filter(proc_data, ARMCD != "ARM C", "adsl")
run(coxt01, proc_data, time_var = "AVAL", event_var = "EVENT")
#> Treatment Effect Adjusted for Covariate
#> Effect/Covariate Included in the Model n Hazard Ratio 95% CI p-value
#> —————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> B: Placebo vs control (A: Drug X) 30 2.71 (0.93, 7.88) 0.0666
#> Covariate:
#> Sex 30 2.91 (0.97, 8.73) 0.0567
#> RACE 30 3.09 (1.01, 9.50) 0.0487
#> Age (yr) 30 2.89 (0.97, 8.59) 0.0566
To add the interaction term to the model,
interaction = TRUE
, which is passed to
tern::control_coxreg()
, needs to be specified.
run(coxt01, proc_data, covariates = "AAGE", interaction = TRUE)
#> Treatment Effect Adjusted for Covariate
#> Effect/Covariate Included in the Model n Hazard Ratio 95% CI p-value Interaction p-value
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> B: Placebo vs control (A: Drug X) 30 2.71 (0.93, 7.88) 0.0666
#> Covariate:
#> Age (yr) 30 0.3666
#> 32 2.87 (0.98, 8.41)
"SEX"
, "RACE"
and
"AAGE"
are used as the covariates for the model.covariates
argument. In the example below,
"RACE"
and "AAGE"
are used as covariates.run(coxt01, proc_data, covariates = c("RACE", "AAGE"))
#> Treatment Effect Adjusted for Covariate
#> Effect/Covariate Included in the Model n Hazard Ratio 95% CI p-value
#> —————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> B: Placebo vs control (A: Drug X) 30 2.71 (0.93, 7.88) 0.0666
#> Covariate:
#> RACE 30 3.09 (1.01, 9.50) 0.0487
#> Age (yr) 30 2.89 (0.97, 8.59) 0.0566
strata = NULL
(no stratification),
ties = "exact"
(equivalent to DISCRETE
in
SAS), and conf_level = 0.95
are applied.strata
argument."efron"
or
"breslow"
, can be specified in the tie
argument, which is passed to tern::control_coxreg()
.conf_level
argument, which is passed
to tern::control_coxreg()
.run(coxt01, proc_data, covariates = c("SEX", "AAGE"), strata = c("RACE"), conf_level = 0.90)
#> Treatment Effect Adjusted for Covariate
#> Effect/Covariate Included in the Model n Hazard Ratio 90% CI p-value
#> —————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> B: Placebo vs control (A: Drug X) 30 2.69 (1.07, 6.76) 0.0785
#> Covariate:
#> Sex 30 2.90 (1.12, 7.54) 0.0668
#> Age (yr) 30 2.72 (1.08, 6.85) 0.0755
COXT02
)coxt02
template produces the
standard multi-variable cox regression output.time_var
argument. By default, time_var
is set
to "AVAL"
, which comes from ADTTE.AVAL
.event_var
argument. By default, event_var
is
set to "EVENT"
, which is derived based on the censoring
indicator ADTTE.CNSR
in the pre-processing function
coxt01_pre
.proc_data <- log_filter(syn_data, PARAMCD == "OS", "adtte")
run(coxt02, proc_data, time_var = "AVAL", event_var = "EVENT")
#> Effect/Covariate Included in the Model Hazard Ratio 95% CI p-value
#> —————————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> Description of Planned Arm (reference = A: Drug X) 0.1630
#> B: Placebo 2.92 (0.93, 9.17) 0.0672
#> C: Combination 1.56 (0.47, 5.10) 0.4659
#> Covariate:
#> Sex (reference = F)
#> M 1.03 (0.41, 2.55) 0.9549
#> RACE (reference = AMERICAN INDIAN OR ALASKA NATIVE) 0.8498
#> ASIAN 1.22 (0.27, 5.55) 0.7967
#> BLACK OR AFRICAN AMERICAN 0.81 (0.12, 5.70) 0.8340
#> WHITE 1.57 (0.26, 9.67) 0.6258
#> Age (yr)
#> All 0.99 (0.93, 1.05) 0.6650
"SEX"
, "RACE"
and
"AAGE"
are used as the covariates for the model.covariates
argument. In the example below,
"RACE"
and "AAGE"
are used as covariates.run(coxt02, proc_data, covariates = c("RACE", "AAGE"))
#> Effect/Covariate Included in the Model Hazard Ratio 95% CI p-value
#> —————————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> Description of Planned Arm (reference = A: Drug X) 0.1390
#> B: Placebo 2.94 (0.97, 8.92) 0.0570
#> C: Combination 1.56 (0.48, 5.09) 0.4605
#> Covariate:
#> RACE (reference = AMERICAN INDIAN OR ALASKA NATIVE) 0.8504
#> ASIAN 1.22 (0.27, 5.54) 0.7972
#> BLACK OR AFRICAN AMERICAN 0.81 (0.12, 5.65) 0.8306
#> WHITE 1.56 (0.26, 9.53) 0.6279
#> Age (yr)
#> All 0.99 (0.93, 1.05) 0.6633
strata = NULL
(no stratification),
ties = "exact"
(equivalent to DISCRETE
in
SAS), and conf_level = 0.95
are applied.strata
argument."efron"
or
"breslow"
, can be specified in the tie
argument, which is passed to tern::control_coxreg()
.conf_level
argument, which is passed
to tern::control_coxreg()
.run(coxt02, proc_data, covariates = c("SEX", "AAGE"), strata = c("RACE"), conf_level = 0.90, ties = "efron")
#> Effect/Covariate Included in the Model Hazard Ratio 90% CI p-value
#> ————————————————————————————————————————————————————————————————————————————————————————————
#> Treatment:
#> Description of Planned Arm (reference = A: Drug X) 0.1680
#> B: Placebo 2.85 (1.09, 7.46) 0.0743
#> C: Combination 1.47 (0.54, 4.02) 0.5254
#> Covariate:
#> Sex (reference = F)
#> M 0.98 (0.45, 2.13) 0.9700
#> Age (yr)
#> All 0.99 (0.94, 1.04) 0.6571
DMT01
)dmt01
template produces the
standard demographics and baseline characteristics summary.run(dmt01, syn_data)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=15) (N=15) (N=15) (N=45)
#> ————————————————————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 15 15 15 45
#> Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4) 34.3 (7.3)
#> Median 31.0 35.0 35.0 34.0
#> Min - Max 24 - 40 24 - 57 24 - 49 24 - 57
#> Age Group
#> n 15 15 15 45
#> <65 15 (100%) 15 (100%) 15 (100%) 45 (100%)
#> Sex
#> n 15 15 15 45
#> Male 3 (20.0%) 7 (46.7%) 5 (33.3%) 15 (33.3%)
#> Female 12 (80.0%) 8 (53.3%) 10 (66.7%) 30 (66.7%)
#> Ethnicity
#> n 15 15 15 45
#> HISPANIC OR LATINO 2 (13.3%) 0 0 2 (4.4%)
#> NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%) 41 (91.1%)
#> NOT REPORTED 0 0 2 (13.3%) 2 (4.4%)
#> RACE
#> n 15 15 15 45
#> AMERICAN INDIAN OR ALASKA NATIVE 0 2 (13.3%) 1 (6.7%) 3 (6.7%)
#> ASIAN 8 (53.3%) 10 (66.7%) 8 (53.3%) 26 (57.8%)
#> BLACK OR AFRICAN AMERICAN 4 (26.7%) 1 (6.7%) 4 (26.7%) 9 (20.0%)
#> WHITE 3 (20.0%) 2 (13.3%) 2 (13.3%) 7 (15.6%)
To remove the column of total, set the argument
lbl_overall
to NULL
.
run(dmt01, syn_data, lbl_overall = NULL)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————
#> Age (yr)
#> n 15 15 15
#> Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4)
#> Median 31.0 35.0 35.0
#> Min - Max 24 - 40 24 - 57 24 - 49
#> Age Group
#> n 15 15 15
#> <65 15 (100%) 15 (100%) 15 (100%)
#> Sex
#> n 15 15 15
#> Male 3 (20.0%) 7 (46.7%) 5 (33.3%)
#> Female 12 (80.0%) 8 (53.3%) 10 (66.7%)
#> Ethnicity
#> n 15 15 15
#> HISPANIC OR LATINO 2 (13.3%) 0 0
#> NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%)
#> NOT REPORTED 0 0 2 (13.3%)
#> RACE
#> n 15 15 15
#> AMERICAN INDIAN OR ALASKA NATIVE 0 2 (13.3%) 1 (6.7%)
#> ASIAN 8 (53.3%) 10 (66.7%) 8 (53.3%)
#> BLACK OR AFRICAN AMERICAN 4 (26.7%) 1 (6.7%) 4 (26.7%)
#> WHITE 3 (20.0%) 2 (13.3%) 2 (13.3%)
summaryvars
. To add or remove analyses, you need
to pass all variables you would like to include to the argument.run(dmt01, syn_data, summaryvars = c("AGE", "AGEGR1", "SEX", "ETHNIC", "RACE", "BBMISI"), lbl_overall = NULL)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————
#> Age
#> n 15 15 15
#> Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4)
#> Median 31.0 35.0 35.0
#> Min - Max 24 - 40 24 - 57 24 - 49
#> Age Group
#> n 15 15 15
#> <65 15 (100%) 15 (100%) 15 (100%)
#> Sex
#> n 15 15 15
#> Male 3 (20.0%) 7 (46.7%) 5 (33.3%)
#> Female 12 (80.0%) 8 (53.3%) 10 (66.7%)
#> Ethnicity
#> n 15 15 15
#> HISPANIC OR LATINO 2 (13.3%) 0 0
#> NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%)
#> NOT REPORTED 0 0 2 (13.3%)
#> RACE
#> n 15 15 15
#> AMERICAN INDIAN OR ALASKA NATIVE 0 2 (13.3%) 1 (6.7%)
#> ASIAN 8 (53.3%) 10 (66.7%) 8 (53.3%)
#> BLACK OR AFRICAN AMERICAN 4 (26.7%) 1 (6.7%) 4 (26.7%)
#> WHITE 3 (20.0%) 2 (13.3%) 2 (13.3%)
#> Baseline BMI
#> n 15 15 15
#> Mean (SD) 29.75 (15.10) 41.08 (26.65) 33.90 (15.39)
#> Median 37.00 33.70 37.80
#> Min - Max 6.4 - 47.9 5.3 - 117.9 -3.5 - 59.0
summaryvars
.proc_data <- syn_data
proc_data$adsl <- proc_data$adsl %>%
mutate(
SEX = reformat(.data$SEX, rule(Male = "M", Female = "F")),
BBMIGR1 = factor(case_when(
BBMISI < 15 ~ "Very severely underweight",
BBMISI >= 15 & BBMISI < 16 ~ "Severely underweight",
BBMISI >= 16 & BBMISI < 18.5 ~ "Underweight",
BBMISI >= 18.5 & BBMISI < 25 ~ "Normal (healthy weight)",
BBMISI >= 25 & BBMISI < 30 ~ "Overweight",
BBMISI >= 30 & BBMISI < 35 ~ "Obese Class I (Moderately obese)",
BBMISI >= 35 & BBMISI < 40 ~ "Obese Class II (Severely obese)",
BBMISI >= 40 ~ "Obese Class III (Very severely obese)"
), levels = c(
"Very severely underweight",
"Severely underweight",
"Underweight",
"Normal (healthy weight)",
"Overweight",
"Obese Class I (Moderately obese)",
"Obese Class II (Severely obese)",
"Obese Class III (Very severely obese)"
))
)
run(dmt01, proc_data, summaryvars = c("AGE", "AGEGR1", "SEX", "ETHNIC", "RACE", "BBMIGR1"), auto_pre = FALSE)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=15) (N=15) (N=15) (N=45)
#> —————————————————————————————————————————————————————————————————————————————————————————————————
#> Age
#> n 15 15 15 45
#> Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4) 34.3 (7.3)
#> Median 31.0 35.0 35.0 34.0
#> Min - Max 24 - 40 24 - 57 24 - 49 24 - 57
#> Age Group
#> n 15 15 15 45
#> <65 15 (100%) 15 (100%) 15 (100%) 45 (100%)
#> Sex
#> n 15 15 15 45
#> Male 3 (20.0%) 7 (46.7%) 5 (33.3%) 15 (33.3%)
#> Female 12 (80.0%) 8 (53.3%) 10 (66.7%) 30 (66.7%)
#> Ethnicity
#> n 15 15 15 45
#> HISPANIC OR LATINO 2 (13.3%) 0 0 2 (4.4%)
#> NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%) 41 (91.1%)
#> NOT REPORTED 0 0 2 (13.3%) 2 (4.4%)
#> RACE
#> n 15 15 15 45
#> AMERICAN INDIAN OR ALASKA NATIVE 0 2 (13.3%) 1 (6.7%) 3 (6.7%)
#> ASIAN 8 (53.3%) 10 (66.7%) 8 (53.3%) 26 (57.8%)
#> BLACK OR AFRICAN AMERICAN 4 (26.7%) 1 (6.7%) 4 (26.7%) 9 (20.0%)
#> WHITE 3 (20.0%) 2 (13.3%) 2 (13.3%) 7 (15.6%)
#> BBMIGR1
#> n 15 15 15 45
#> Very severely underweight 4 (26.7%) 1 (6.7%) 1 (6.7%) 6 (13.3%)
#> Underweight 1 (6.7%) 0 0 1 (2.2%)
#> Normal (healthy weight) 1 (6.7%) 3 (20.0%) 4 (26.7%) 8 (17.8%)
#> Overweight 0 1 (6.7%) 1 (6.7%) 2 (4.4%)
#> Obese Class I (Moderately obese) 0 3 (20.0%) 0 3 (6.7%)
#> Obese Class II (Severely obese) 4 (26.7%) 1 (6.7%) 3 (20.0%) 8 (17.8%)
#> Obese Class III (Very severely obese) 5 (33.3%) 6 (40.0%) 6 (40.0%) 17 (37.8%)
ADVS
or
ADSUB
To add baseline vital signs or other baseline characteristics to the
demographics and baseline characteristics summary, manual preprocess of
input adsl
dataset is expected and merge the vital signs
baseline values from advs
(where
ADVS.ABLFL == "Y"
) or adsub
with
adsl
by unique subject identifier.
proc_data <- syn_data
diabpbl <- proc_data$advs %>%
filter(ABLFL == "Y" & PARAMCD == "DIABP") %>%
mutate(DIABPBL = AVAL) %>%
select("STUDYID", "USUBJID", "DIABPBL")
proc_data$adsl <- proc_data$adsl %>%
mutate(SEX = reformat(.data$SEX, rule(Male = "M", Female = "F"))) %>%
left_join(diabpbl, by = c("STUDYID", "USUBJID"))
run(dmt01, proc_data, summaryvars = c("AGE", "AGEGR1", "SEX", "ETHNIC", "RACE", "DIABPBL"), auto_pre = FALSE)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=15) (N=15) (N=15) (N=45)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Age
#> n 15 15 15 45
#> Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4) 34.3 (7.3)
#> Median 31.0 35.0 35.0 34.0
#> Min - Max 24 - 40 24 - 57 24 - 49 24 - 57
#> Age Group
#> n 15 15 15 45
#> <65 15 (100%) 15 (100%) 15 (100%) 45 (100%)
#> Sex
#> n 15 15 15 45
#> Male 3 (20.0%) 7 (46.7%) 5 (33.3%) 15 (33.3%)
#> Female 12 (80.0%) 8 (53.3%) 10 (66.7%) 30 (66.7%)
#> Ethnicity
#> n 15 15 15 45
#> HISPANIC OR LATINO 2 (13.3%) 0 0 2 (4.4%)
#> NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%) 41 (91.1%)
#> NOT REPORTED 0 0 2 (13.3%) 2 (4.4%)
#> RACE
#> n 15 15 15 45
#> AMERICAN INDIAN OR ALASKA NATIVE 0 2 (13.3%) 1 (6.7%) 3 (6.7%)
#> ASIAN 8 (53.3%) 10 (66.7%) 8 (53.3%) 26 (57.8%)
#> BLACK OR AFRICAN AMERICAN 4 (26.7%) 1 (6.7%) 4 (26.7%) 9 (20.0%)
#> WHITE 3 (20.0%) 2 (13.3%) 2 (13.3%) 7 (15.6%)
#> Analysis Value
#> n 15 15 15 45
#> Mean (SD) 96.132511 (22.458204) 108.110944 (15.074451) 103.148818 (19.751687) 102.464091 (19.534945)
#> Median 93.328321 108.951358 102.849019 102.396129
#> Min - Max 60.58490 - 136.59343 83.44277 - 131.61501 66.05223 - 136.55256 60.58490 - 136.59343
DST01
)dst01
template produces the
standard patient disposition summary.lbl_overall = NULL
to suppress the default.run(dst01, syn_data, lbl_overall = NULL)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ——————————————————————————————————————————————————————————————————————————
#> Completed 10 (66.7%) 10 (66.7%) 10 (66.7%)
#> Discontinued 5 (33.3%) 5 (33.3%) 5 (33.3%)
#> ADVERSE EVENT 0 0 1 (6.7%)
#> DEATH 2 (13.3%) 4 (26.7%) 3 (20.0%)
#> LACK OF EFFICACY 2 (13.3%) 0 0
#> PHYSICIAN DECISION 0 0 1 (6.7%)
#> PROTOCOL VIOLATION 0 1 (6.7%) 0
#> WITHDRAWAL BY PARENT/GUARDIAN 1 (6.7%) 0 0
ADSL.DCSREASGP
] that groups the
discontinuation reasons needs to be derived manually and provided in the
input adsl
dataset.run(dst01, syn_data, detail_vars = list(Discontinued = c("DCSREASGP", "DCSREAS")), lbl_overall = NULL)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————
#> Completed 10 (66.7%) 10 (66.7%) 10 (66.7%)
#> Discontinued 5 (33.3%) 5 (33.3%) 5 (33.3%)
#> Safety
#> ADVERSE EVENT 0 0 1 (6.7%)
#> DEATH 2 (13.3%) 4 (26.7%) 3 (20.0%)
#> Non-Safety
#> LACK OF EFFICACY 2 (13.3%) 0 0
#> PHYSICIAN DECISION 0 0 1 (6.7%)
#> PROTOCOL VIOLATION 0 1 (6.7%) 0
#> WITHDRAWAL BY PARENT/GUARDIAN 1 (6.7%) 0 0
The syntax below adds the end of treatment status to the standard
patient disposition summary by providing the end of treatment status
variable to the argument trt_status_var
.
run(dst01, syn_data, trt_status_var = "EOTSTT", lbl_overall = NULL)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ——————————————————————————————————————————————————————————————————————————
#> Completed 10 (66.7%) 10 (66.7%) 10 (66.7%)
#> Discontinued 5 (33.3%) 5 (33.3%) 5 (33.3%)
#> ADVERSE EVENT 0 0 1 (6.7%)
#> DEATH 2 (13.3%) 4 (26.7%) 3 (20.0%)
#> LACK OF EFFICACY 2 (13.3%) 0 0
#> PHYSICIAN DECISION 0 0 1 (6.7%)
#> PROTOCOL VIOLATION 0 1 (6.7%) 0
#> WITHDRAWAL BY PARENT/GUARDIAN 1 (6.7%) 0 0
#> Completed Treatment 8 (53.3%) 4 (26.7%) 5 (33.3%)
#> Ongoing Treatment 4 (26.7%) 6 (40.0%) 4 (26.7%)
#> Discontinued Treatment 3 (20.0%) 5 (33.3%) 6 (40.0%)
The syntax adds the details of study ongoing/alive status to the
standard patient disposition summary by modifying the argument
detail_vars
.
run(dst01, syn_data, detail_vars = list(Discontinued = "DCSREAS", Ongoing = "STDONS"))
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=15) (N=15) (N=15) (N=45)
#> —————————————————————————————————————————————————————————————————————————————————————————
#> Completed 10 (66.7%) 10 (66.7%) 10 (66.7%) 30 (66.7%)
#> Discontinued 5 (33.3%) 5 (33.3%) 5 (33.3%) 15 (33.3%)
#> ADVERSE EVENT 0 0 1 (6.7%) 1 (2.2%)
#> DEATH 2 (13.3%) 4 (26.7%) 3 (20.0%) 9 (20.0%)
#> LACK OF EFFICACY 2 (13.3%) 0 0 2 (4.4%)
#> PHYSICIAN DECISION 0 0 1 (6.7%) 1 (2.2%)
#> PROTOCOL VIOLATION 0 1 (6.7%) 0 1 (2.2%)
#> WITHDRAWAL BY PARENT/GUARDIAN 1 (6.7%) 0 0 1 (2.2%)
DTHT01
)The dtht01
template produces the
standard deaths output.
run(dst01, syn_data)
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=15) (N=15) (N=15) (N=45)
#> —————————————————————————————————————————————————————————————————————————————————————————
#> Completed 10 (66.7%) 10 (66.7%) 10 (66.7%) 30 (66.7%)
#> Discontinued 5 (33.3%) 5 (33.3%) 5 (33.3%) 15 (33.3%)
#> ADVERSE EVENT 0 0 1 (6.7%) 1 (2.2%)
#> DEATH 2 (13.3%) 4 (26.7%) 3 (20.0%) 9 (20.0%)
#> LACK OF EFFICACY 2 (13.3%) 0 0 2 (4.4%)
#> PHYSICIAN DECISION 0 0 1 (6.7%) 1 (2.2%)
#> PROTOCOL VIOLATION 0 1 (6.7%) 0 1 (2.2%)
#> WITHDRAWAL BY PARENT/GUARDIAN 1 (6.7%) 0 0 1 (2.2%)
run(dtht01, syn_data, other_category = TRUE)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————
#> Total number of deaths 2 (13.3%) 4 (26.7%) 3 (20.0%)
#> Primary Cause of Death
#> n 2 4 3
#> Adverse Event 1 (50.0%) 2 (50.0%) 1 (33.3%)
#> Progressive Disease 1 (50.0%) 0 2 (66.7%)
#> Other 0 2 (50.0%) 0
#> LOST TO FOLLOW UP 0 1 (50%) 0
#> SUICIDE 0 1 (50%) 0
NOTE: In order to avoid the warning above and display ‘Other’ as the
last category under “Primary Cause of Death” right above the detailed
reasons for “Other”, the user is expected to manually provide levels to
ADSL.DTHCAT
based on categories available in the
dataset.
Setting time_since_last_dose
to TRUE
, the
syntax produces the count of deaths by days from last study drug
administration as well as the count of deaths by primary cause and days
from last study drug administration.
run(dtht01, syn_data, time_since_last_dose = TRUE)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of deaths 2 (13.3%) 4 (26.7%) 3 (20.0%)
#> Days from last drug administration
#> n 2 4 3
#> <=30 2 (100%) 1 (25.0%) 2 (66.7%)
#> >30 0 3 (75.0%) 1 (33.3%)
#> Primary cause by days from last study drug administration
#> <=30
#> n 2 1 2
#> Adverse Event 1 (50.0%) 0 1 (50.0%)
#> Progressive Disease 1 (50.0%) 0 1 (50.0%)
#> Other 0 1 (100%) 0
#> >30
#> n 0 3 1
#> Adverse Event 0 2 (66.7%) 0
#> Progressive Disease 0 0 1 (100%)
#> Other 0 1 (33.3%) 0
EGT01
)The egt01
template produces the
standard ECG results and change from baseline by visit summary.
run(egt01, syn_data)
#> A: Drug X B: Placebo C: Combination
#> Change from Change from Change from
#> Value at Visit Baseline Value at Visit Baseline Value at Visit Baseline
#> Analysis Visit (N=15) (N=15) (N=15) (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Heart Rate
#> BASELINE
#> n 15 15 15
#> Mean (SD) 76.594 (17.889) 69.899 (18.788) 70.492 (18.175)
#> Median 77.531 77.174 74.111
#> Min - Max 46.50 - 106.68 26.42 - 97.69 45.37 - 115.49
#> WEEK 1 DAY 8
#> n 15 15 15 15 15 15
#> Mean (SD) 71.140 (23.441) -5.454 (25.128) 70.958 (14.877) 1.059 (23.345) 67.450 (18.932) -3.043 (23.753)
#> Median 77.210 -2.152 70.033 -8.403 68.471 0.181
#> Min - Max 8.53 - 102.63 -50.97 - 36.54 44.85 - 93.79 -25.34 - 60.50 38.90 - 100.05 -52.20 - 33.13
#> WEEK 2 DAY 15
#> n 15 15 15 15 15 15
#> Mean (SD) 69.350 (16.083) -7.244 (28.960) 76.096 (14.958) 6.198 (29.319) 63.694 (12.920) -6.799 (23.949)
#> Median 65.746 -11.369 75.323 0.255 61.076 -4.954
#> Min - Max 47.22 - 101.44 -49.59 - 42.91 47.50 - 111.40 -37.51 - 69.34 43.25 - 86.13 -52.70 - 40.76
#> WEEK 3 DAY 22
#> n 15 15 15 15 15 15
#> Mean (SD) 73.894 (24.576) -2.700 (32.079) 67.635 (19.114) -2.263 (29.989) 72.054 (19.308) 1.562 (27.494)
#> Median 69.296 5.492 68.468 -2.093 68.686 -5.848
#> Min - Max 44.15 - 131.73 -62.53 - 38.19 31.89 - 108.87 -52.26 - 66.81 32.16 - 109.86 -49.61 - 35.23
#> WEEK 4 DAY 29
#> n 15 15 15 15 15 15
#> Mean (SD) 73.241 (19.256) -3.353 (29.170) 66.524 (25.487) -3.374 (36.024) 66.600 (22.839) -3.892 (24.140)
#> Median 68.689 0.232 66.397 -11.730 64.969 -6.827
#> Min - Max 33.71 - 111.54 -55.14 - 65.04 19.66 - 111.29 -60.39 - 61.00 10.35 - 100.88 -50.72 - 26.77
#> WEEK 5 DAY 36
#> n 15 15 15 15 15 15
#> Mean (SD) 61.690 (22.182) -14.904 (30.330) 60.712 (20.025) -9.187 (24.587) 72.683 (23.495) 2.191 (26.654)
#> Median 57.925 -12.660 60.454 -16.100 77.585 14.635
#> Min - Max 23.89 - 103.74 -60.00 - 57.24 32.53 - 102.02 -52.56 - 50.96 31.21 - 105.05 -42.90 - 34.64
#> QT Duration
#> BASELINE
#> n 15 15 15
#> Mean (SD) 335.294 (123.231) 363.104 (68.160) 347.311 (86.236)
#> Median 372.731 386.316 348.254
#> Min - Max 121.28 - 554.97 214.65 - 445.53 170.80 - 508.54
#> WEEK 1 DAY 8
#> n 15 15 15 15 15 15
#> Mean (SD) 357.361 (85.688) 22.067 (144.166) 415.225 (105.425) 52.121 (144.259) 321.078 (107.553) -26.233 (129.135)
#> Median 344.797 49.432 421.950 62.762 307.962 -17.006
#> Min - Max 241.22 - 517.39 -207.23 - 245.36 234.11 - 604.72 -190.70 - 364.94 118.36 - 480.29 -363.11 - 163.67
#> WEEK 2 DAY 15
#> n 15 15 15 15 15 15
#> Mean (SD) 344.883 (106.793) 9.589 (174.797) 370.548 (80.862) 7.444 (91.301) 354.129 (95.133) 6.818 (142.397)
#> Median 312.236 -9.264 388.515 -9.429 365.292 39.930
#> Min - Max 187.77 - 501.87 -278.91 - 372.71 204.55 - 514.43 -190.58 - 173.87 200.19 - 493.40 -279.46 - 265.56
#> WEEK 3 DAY 22
#> n 15 15 15 15 15 15
#> Mean (SD) 342.062 (92.568) 6.768 (151.505) 326.684 (116.421) -36.420 (145.415) 366.245 (99.106) 18.935 (168.417)
#> Median 352.930 -22.771 298.353 -78.409 329.688 -21.584
#> Min - Max 199.40 - 476.04 -230.25 - 303.00 151.05 - 561.23 -205.30 - 293.76 249.42 - 580.81 -252.73 - 410.01
#> WEEK 4 DAY 29
#> n 15 15 15 15 15 15
#> Mean (SD) 371.650 (44.805) 36.356 (139.308) 333.697 (110.377) -29.407 (125.592) 333.181 (96.466) -14.130 (107.622)
#> Median 375.412 58.958 308.020 -40.987 330.911 -25.820
#> Min - Max 302.32 - 451.62 -214.07 - 258.04 183.09 - 531.08 -241.72 - 134.12 126.95 - 488.57 -234.92 - 152.49
#> WEEK 5 DAY 36
#> n 15 15 15 15 15 15
#> Mean (SD) 345.504 (130.543) 10.210 (198.224) 309.919 (84.624) -53.185 (105.730) 322.931 (67.801) -24.380 (117.331)
#> Median 355.730 -23.213 306.219 -12.373 341.988 -26.952
#> Min - Max 88.38 - 661.12 -271.06 - 539.84 189.01 - 448.58 -256.52 - 91.57 217.51 - 427.16 -291.03 - 171.19
#> RR Duration
#> BASELINE
#> n 15 15 15
#> Mean (SD) 1086.908 (363.811) 1050.034 (390.444) 1102.659 (310.359)
#> Median 1116.849 1089.193 1250.037
#> Min - Max 626.19 - 1653.12 414.61 - 1721.89 385.51 - 1430.81
#> WEEK 1 DAY 8
#> n 15 15 15 15 15 15
#> Mean (SD) 968.499 (287.811) -118.409 (546.796) 1041.186 (211.201) -8.848 (435.281) 948.491 (213.746) -154.168 (442.882)
#> Median 961.296 -147.460 1013.786 24.754 965.429 -224.054
#> Min - Max 358.92 - 1593.51 -1014.82 - 911.82 714.44 - 1417.52 -618.80 - 847.31 513.35 - 1229.09 -736.69 - 843.58
#> WEEK 2 DAY 15
#> n 15 15 15 15 15 15
#> Mean (SD) 932.717 (259.634) -154.191 (331.884) 1139.332 (454.231) 89.298 (582.750) 1021.283 (233.529) -81.376 (415.781)
#> Median 950.533 -205.949 1068.007 -5.449 964.616 -142.180
#> Min - Max 409.68 - 1269.35 -649.69 - 473.09 486.51 - 2048.73 -846.72 - 1148.61 667.36 - 1367.25 -647.47 - 616.15
#> WEEK 3 DAY 22
#> n 15 15 15 15 15 15
#> Mean (SD) 1068.865 (319.540) -18.043 (513.412) 1110.882 (259.523) 60.848 (432.700) 1105.918 (306.185) 3.259 (516.734)
#> Median 1201.998 -65.085 1163.690 51.200 1187.130 30.318
#> Min - Max 380.49 - 1551.65 -832.86 - 703.74 621.41 - 1453.29 -887.06 - 822.18 446.02 - 1648.32 -984.79 - 816.30
#> WEEK 4 DAY 29
#> n 15 15 15 15 15 15
#> Mean (SD) 1087.915 (205.940) 1.008 (403.039) 1161.681 (293.257) 111.647 (460.979) 992.134 (283.177) -110.525 (334.932)
#> Median 1084.658 146.611 1055.223 191.008 1028.997 -112.599
#> Min - Max 697.59 - 1499.17 -801.16 - 402.97 722.35 - 1762.04 -528.27 - 1191.83 497.14 - 1382.12 -597.95 - 757.99
#> WEEK 5 DAY 36
#> n 15 15 15 15 15 15
#> Mean (SD) 1016.880 (424.428) -70.027 (505.078) 1135.131 (224.684) 85.097 (497.679) 1089.527 (238.909) -13.132 (362.606)
#> Median 962.584 -142.925 1158.815 -9.553 1081.015 16.706
#> Min - Max 352.97 - 1843.86 -894.83 - 1162.79 714.34 - 1436.68 -843.41 - 992.34 699.72 - 1611.38 -696.03 - 561.53
EGT02_1
)The egt02_1
template produces the
standard ECG abnormalities summary where the abnormalities are
summarized regardless of the abnormality at baseline.
run(egt02_1, syn_data)
#> Assessment A: Drug X B: Placebo C: Combination
#> Abnormality (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————
#> Heart Rate
#> Low 4/15 (26.7%) 4/15 (26.7%) 4/15 (26.7%)
#> High 4/15 (26.7%) 3/15 (20%) 3/15 (20%)
#> QT Duration
#> Low 2/15 (13.3%) 5/15 (33.3%) 3/15 (20%)
#> High 3/15 (20%) 6/15 (40%) 2/15 (13.3%)
#> RR Duration
#> Low 6/15 (40%) 2/15 (13.3%) 4/15 (26.7%)
#> High 4/15 (26.7%) 5/15 (33.3%) 2/15 (13.3%)
EGT02_2
)The egt02_2
template produces the
standard ECG abnormalities summary where the abnormalities are
summarized among subject without abnormality at baseline.
run(egt02_2, syn_data)
#> Assessment A: Drug X B: Placebo C: Combination
#> Abnormality (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————
#> Heart Rate
#> Low 4/15 (26.7%) 4/14 (28.6%) 4/15 (26.7%)
#> High 3/13 (23.1%) 3/15 (20%) 2/14 (14.3%)
#> QT Duration
#> Low 2/12 (16.7%) 5/15 (33.3%) 3/14 (21.4%)
#> High 3/14 (21.4%) 6/15 (40%) 2/14 (14.3%)
#> RR Duration
#> Low 6/15 (40%) 2/13 (15.4%) 4/14 (28.6%)
#> High 4/13 (30.8%) 5/13 (38.5%) 2/15 (13.3%)
EGT03
)The egt03
template produces the
standard shift table of ECG interval data - baseline versus minimum
post-baseline summary.
proc_data <- log_filter(syn_data, PARAMCD == "HR", "adeg")
run(egt03, proc_data)
#> Actual Arm Code Minimum Post-Baseline Assessment
#> Baseline Reference Range Indicator LOW NORMAL HIGH Missing
#> ————————————————————————————————————————————————————————————————————————————————
#> Heart Rate
#> ARM A (N=15)
#> LOW 0 0 0 0
#> NORMAL 4 (26.7%) 9 (60.0%) 0 0
#> HIGH 0 2 (13.3%) 0 0
#> Missing 0 0 0 0
#> ARM B (N=15)
#> LOW 0 1 (6.7%) 0 0
#> NORMAL 4 (26.7%) 10 (66.7%) 0 0
#> HIGH 0 0 0 0
#> Missing 0 0 0 0
#> ARM C (N=15)
#> LOW 0 0 0 0
#> NORMAL 4 (26.7%) 10 (66.7%) 0 0
#> HIGH 0 1 (6.7%) 0 0
#> Missing 0 0 0 0
To produce the standard shift table of ECG interval data - baseline versus maximum post-baseline summary….TBA
EGT05_QTCAT
)The egt05_qtcat
template produces the
standard ECG actual values and changes from baseline by visit
summary.
run(egt05_qtcat, syn_data)
#> Parameter
#> Analysis Visit A: Drug X B: Placebo C: Combination
#> Category (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————
#> QT Duration
#> BASELINE
#> Value at Visit
#> n 15 15 15
#> <=450 msec 13 (86.7%) 15 (100%) 13 (86.7%)
#> >450 to <=480 msec 1 (6.7%) 0 0
#> >480 to <=500 msec 0 0 1 (6.7%)
#> >500 msec 1 (6.7%) 0 1 (6.7%)
#> WEEK 1 DAY 8
#> Value at Visit
#> n 15 15 15
#> <=450 msec 12 (80.0%) 9 (60.0%) 13 (86.7%)
#> >450 to <=480 msec 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> >480 to <=500 msec 1 (6.7%) 3 (20.0%) 1 (6.7%)
#> >500 msec 1 (6.7%) 2 (13.3%) 0
#> Change from Baseline
#> n 15 15 15
#> <=30 msec 7 (46.7%) 6 (40.0%) 9 (60.0%)
#> >30 to <=60 msec 2 (13.3%) 1 (6.7%) 1 (6.7%)
#> >60 msec 6 (40.0%) 8 (53.3%) 5 (33.3%)
#> WEEK 2 DAY 15
#> Value at Visit
#> n 15 15 15
#> <=450 msec 11 (73.3%) 14 (93.3%) 12 (80.0%)
#> >450 to <=480 msec 2 (13.3%) 0 2 (13.3%)
#> >480 to <=500 msec 1 (6.7%) 0 1 (6.7%)
#> >500 msec 1 (6.7%) 1 (6.7%) 0
#> Change from Baseline
#> n 15 15 15
#> <=30 msec 9 (60.0%) 12 (80.0%) 7 (46.7%)
#> >30 to <=60 msec 2 (13.3%) 0 3 (20.0%)
#> >60 msec 4 (26.7%) 3 (20.0%) 5 (33.3%)
#> WEEK 3 DAY 22
#> Value at Visit
#> n 15 15 15
#> <=450 msec 12 (80.0%) 12 (80.0%) 12 (80.0%)
#> >450 to <=480 msec 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> >500 msec 0 2 (13.3%) 2 (13.3%)
#> Change from Baseline
#> n 15 15 15
#> <=30 msec 9 (60.0%) 11 (73.3%) 9 (60.0%)
#> >30 to <=60 msec 1 (6.7%) 1 (6.7%) 0
#> >60 msec 5 (33.3%) 3 (20.0%) 6 (40.0%)
#> WEEK 4 DAY 29
#> Value at Visit
#> n 15 15 15
#> <=450 msec 14 (93.3%) 12 (80.0%) 13 (86.7%)
#> >450 to <=480 msec 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> >480 to <=500 msec 0 0 1 (6.7%)
#> >500 msec 0 2 (13.3%) 0
#> Change from Baseline
#> n 15 15 15
#> <=30 msec 6 (40.0%) 9 (60.0%) 9 (60.0%)
#> >30 to <=60 msec 2 (13.3%) 1 (6.7%) 2 (13.3%)
#> >60 msec 7 (46.7%) 5 (33.3%) 4 (26.7%)
#> WEEK 5 DAY 36
#> Value at Visit
#> n 15 15 15
#> <=450 msec 12 (80.0%) 15 (100%) 15 (100%)
#> >450 to <=480 msec 2 (13.3%) 0 0
#> >500 msec 1 (6.7%) 0 0
#> Change from Baseline
#> n 15 15 15
#> <=30 msec 9 (60.0%) 11 (73.3%) 9 (60.0%)
#> >30 to <=60 msec 0 3 (20.0%) 2 (13.3%)
#> >60 msec 6 (40.0%) 1 (6.7%) 4 (26.7%)
The template have two default analyses of ADEG.AVALCAT1
and ADEG.CHGCAT1
. To keep only the analyses needed, this
can be achieved by modifying the parameter summaryvars
.
run(egt05_qtcat, syn_data, summaryvars = c("AVALCAT1"))
#> Parameter
#> Analysis Visit A: Drug X B: Placebo C: Combination
#> Category (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————
#> QT Duration
#> BASELINE
#> n 15 15 15
#> <=450 msec 13 (86.7%) 15 (100%) 13 (86.7%)
#> >450 to <=480 msec 1 (6.7%) 0 0
#> >480 to <=500 msec 0 0 1 (6.7%)
#> >500 msec 1 (6.7%) 0 1 (6.7%)
#> WEEK 1 DAY 8
#> n 15 15 15
#> <=450 msec 12 (80.0%) 9 (60.0%) 13 (86.7%)
#> >450 to <=480 msec 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> >480 to <=500 msec 1 (6.7%) 3 (20.0%) 1 (6.7%)
#> >500 msec 1 (6.7%) 2 (13.3%) 0
#> WEEK 2 DAY 15
#> n 15 15 15
#> <=450 msec 11 (73.3%) 14 (93.3%) 12 (80.0%)
#> >450 to <=480 msec 2 (13.3%) 0 2 (13.3%)
#> >480 to <=500 msec 1 (6.7%) 0 1 (6.7%)
#> >500 msec 1 (6.7%) 1 (6.7%) 0
#> WEEK 3 DAY 22
#> n 15 15 15
#> <=450 msec 12 (80.0%) 12 (80.0%) 12 (80.0%)
#> >450 to <=480 msec 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> >500 msec 0 2 (13.3%) 2 (13.3%)
#> WEEK 4 DAY 29
#> n 15 15 15
#> <=450 msec 14 (93.3%) 12 (80.0%) 13 (86.7%)
#> >450 to <=480 msec 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> >480 to <=500 msec 0 0 1 (6.7%)
#> >500 msec 0 2 (13.3%) 0
#> WEEK 5 DAY 36
#> n 15 15 15
#> <=450 msec 12 (80.0%) 15 (100%) 15 (100%)
#> >450 to <=480 msec 2 (13.3%) 0 0
#> >500 msec 1 (6.7%) 0 0
EXT01
)ext01
template displays total
number of doses administered and total dose administered by defaultrun(ext01, syn_data)
#> A: Drug X B: Placebo C: Combination
#> PARCAT2 (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————
#> Drug A
#> Overall duration (days)
#> n 11 7 7
#> Mean (SD) 157.5 (67.4) 115.4 (62.8) 98.6 (68.8)
#> Median 174.0 119.0 89.0
#> Min - Max 53.0 - 239.0 22.0 - 219.0 1.0 - 182.0
#> Total dose administered
#> n 11 7 7
#> Mean (SD) 6567.3 (1127.1) 7028.6 (1626.1) 6377.1 (863.7)
#> Median 6720.0 7200.0 6480.0
#> Min - Max 4800.0 - 8400.0 5280.0 - 9360.0 5280.0 - 7440.0
#> Drug B
#> Overall duration (days)
#> n 4 8 8
#> Mean (SD) 142.2 (100.3) 105.9 (60.0) 158.2 (96.2)
#> Median 160.0 95.0 203.0
#> Min - Max 17.0 - 232.0 37.0 - 211.0 27.0 - 249.0
#> Total dose administered
#> n 4 8 8
#> Mean (SD) 7020.0 (1148.9) 5250.0 (864.7) 5940.0 (1187.9)
#> Median 6960.0 5160.0 5880.0
#> Min - Max 5760.0 - 8400.0 4080.0 - 6480.0 4320.0 - 7680.0
LBT01
)lbt01
template produces the
standard laboratory test results and change from baseline by visit.t_lb_chg <- run(lbt01, syn_data)
head(t_lb_chg, 20)
#> A: Drug X B: Placebo C: Combination
#> Change from Change from Change from
#> Value at Visit Baseline Value at Visit Baseline Value at Visit Baseline
#> (N=15) (N=15) (N=15) (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement
#> BASELINE
#> n 15 15 15
#> Mean (SD) 18.655 (12.455) 16.835 (11.080) 22.385 (9.452)
#> Median 16.040 17.453 25.250
#> Min - Max 2.43 - 44.06 1.48 - 31.99 0.57 - 37.23
#> WEEK 1 DAY 8
#> n 15 15 15 15 15 15
#> Mean (SD) 16.308 (10.850) -2.348 (17.558) 22.055 (7.537) 5.220 (16.359) 19.574 (9.876) -2.811 (10.902)
#> Median 14.664 -5.369 22.476 7.252 19.425 -0.995
#> Min - Max 0.10 - 36.30 -30.18 - 22.66 9.72 - 33.81 -16.82 - 32.33 1.03 - 36.28 -19.61 - 18.45
#> WEEK 2 DAY 15
#> n 15 15 15 15 15 15
#> Mean (SD) 16.646 (10.528) -2.010 (15.773) 20.758 (9.578) 3.923 (14.084) 10.911 (7.721) -11.474 (11.002)
#> Median 15.470 -6.427 18.499 6.248 9.850 -8.657
#> Min - Max 0.40 - 35.29 -29.99 - 32.86 1.56 - 42.84 -24.92 - 29.85 0.35 - 25.01 -27.38 - 2.52
#> WEEK 3 DAY 22
#> n 15 15 15 15 15 15
#> Mean (SD) 17.488 (10.679) -1.167 (15.759) 20.055 (8.086) 3.219 (16.285) 18.413 (9.513) -3.973 (9.966)
#> Median 14.224 1.355 21.852 5.345 19.529 -7.194
TBA
LBT04
)lbt04
template produces the
standard laboratory abnormalities summary.run(lbt04, syn_data)
#> Laboratory Test A: Drug X B: Placebo C: Combination
#> Direction of Abnormality (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————
#> CHEMISTRY
#> Alanine Aminotransferase Measurement
#> Low 0/7 0/2 1/7 (14.3%)
#> High 0/7 0/3 0/8
#> C-Reactive Protein Measurement
#> Low 0/8 1/2 (50.0%) 0/6
#> High 3/8 (37.5%) 0/2 0/7
#> Immunoglobulin A Measurement
#> Low 0/5 0/8 0/7
#> High 1/3 (33.3%) 1/8 (12.5%) 0/6
#> COAGULATION
#> Alanine Aminotransferase Measurement
#> Low 0/3 0/6 0/4
#> High 0/5 0/7 0/4
#> C-Reactive Protein Measurement
#> Low 0/5 0/5 1/3 (33.3%)
#> High 0/5 1/6 (16.7%) 1/4 (25.0%)
#> Immunoglobulin A Measurement
#> Low 0/8 0/9 0/6
#> High 0/8 0/9 1/6 (16.7%)
#> HEMATOLOGY
#> Alanine Aminotransferase Measurement
#> Low 0/4 0/5 0/4
#> High 0/6 0/5 0/4
#> C-Reactive Protein Measurement
#> Low 0/5 0/4 0/3
#> High 0/5 0/4 0/5
#> Immunoglobulin A Measurement
#> Low 0/3 0/4 0/8
#> High 0/3 0/4 0/7
LBT05
)lbt05
template produces the
standard laboratory abnormalities summary for marked abnormalities.run(lbt05, syn_data)
#> Laboratory Test A: Drug X B: Placebo C: Combination
#> Direction of Abnormality (N=15) (N=15) (N=15)
#> ——————————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement (n) 15 14 14
#> Low
#> Single, not last 1 (6.7%) 0 4 (28.6%)
#> Last or replicated 5 (33.3%) 4 (28.6%) 4 (28.6%)
#> Any Abnormality 6 (40.0%) 4 (28.6%) 8 (57.1%)
#> High
#> Single, not last 0 0 0
#> Last or replicated 0 0 0
#> Any Abnormality 0 0 0
#> C-Reactive Protein Measurement (n) 15 15 15
#> Low
#> Single, not last 4 (26.7%) 0 3 (20.0%)
#> Last or replicated 3 (20.0%) 5 (33.3%) 6 (40.0%)
#> Any Abnormality 7 (46.7%) 5 (33.3%) 9 (60.0%)
#> High
#> Single, not last 1 (6.7%) 3 (20.0%) 0
#> Last or replicated 4 (26.7%) 3 (20.0%) 6 (40.0%)
#> Any Abnormality 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> Immunoglobulin A Measurement (n) 13 14 14
#> Low
#> Single, not last 0 0 0
#> Last or replicated 0 0 0
#> Any Abnormality 0 0 0
#> High
#> Single, not last 6 (46.2%) 1 (7.1%) 2 (14.3%)
#> Last or replicated 3 (23.1%) 4 (28.6%) 3 (21.4%)
#> Any Abnormality 9 (69.2%) 5 (35.7%) 5 (35.7%)
MLAs
LBT06
)lbt06
template produces the
standard laboratory abnormalities by visit and baseline status
summary.run(lbt06, syn_data)
#> Visit
#> Abnormality at Visit A: Drug X B: Placebo C: Combination
#> Baseline Status (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement
#> WEEK 1 DAY 8
#> Low
#> Not low 0/1 0/6 0/1
#> Low 0/1 0/1 0/1
#> Total 0/2 0/7 0/2
#> High
#> Not high 0/2 0/7 0/2
#> High 0/0 0/0 0/0
#> Total 0/2 0/7 0/2
#> WEEK 2 DAY 15
#> Low
#> Not low 0/3 0/2 0/2
#> Low 0/0 0/0 0/0
#> Total 0/3 0/2 0/2
#> High
#> Not high 0/3 0/2 0/2
#> High 0/0 0/0 0/0
#> Total 0/3 0/2 0/2
#> WEEK 3 DAY 22
#> Low
#> Not low 0/5 0/3 1/6 (16.7%)
#> Low 0/0 0/0 0/0
#> Total 0/5 0/3 1/6 (16.7%)
#> High
#> Not high 0/5 0/3 0/6
#> High 0/0 0/0 0/0
#> Total 0/5 0/3 0/6
#> WEEK 4 DAY 29
#> Low
#> Not low 0/3 0/1 0/1
#> Low 0/3 0/2 0/0
#> Total 0/6 0/3 0/1
#> High
#> Not high 0/6 0/3 0/1
#> High 0/0 0/0 0/0
#> Total 0/6 0/3 0/1
#> WEEK 5 DAY 36
#> Low
#> Not low 0/2 0/2 0/5
#> Low 0/1 0/1 0/0
#> Total 0/3 0/3 0/5
#> High
#> Not high 0/3 0/3 0/5
#> High 0/0 0/0 0/0
#> Total 0/3 0/3 0/5
#> C-Reactive Protein Measurement
#> WEEK 1 DAY 8
#> Low
#> Not low 0/5 0/3 0/3
#> Low 0/0 0/1 0/0
#> Total 0/5 0/4 0/3
#> High
#> Not high 0/5 0/3 1/3 (33.3%)
#> High 0/0 0/1 0/0
#> Total 0/5 0/4 1/3 (33.3%)
#> WEEK 2 DAY 15
#> Low
#> Not low 0/8 0/2 0/0
#> Low 0/0 0/0 0/1
#> Total 0/8 0/2 0/1
#> High
#> Not high 1/8 (12.5%) 0/1 0/1
#> High 0/0 0/1 0/0
#> Total 1/8 (12.5%) 0/2 0/1
#> WEEK 3 DAY 22
#> Low
#> Not low 0/5 0/4 0/4
#> Low 0/0 1/1 (100%) 0/2
#> Total 0/5 1/5 (20%) 0/6
#> High
#> Not high 1/5 (20%) 1/5 (20%) 0/6
#> High 0/0 0/0 0/0
#> Total 1/5 (20%) 1/5 (20%) 0/6
#> WEEK 4 DAY 29
#> Low
#> Not low 0/2 1/2 (50%) 1/3 (33.3%)
#> Low 0/0 0/0 0/0
#> Total 0/2 1/2 (50%) 1/3 (33.3%)
#> High
#> Not high 0/2 0/2 0/3
#> High 0/0 0/0 0/0
#> Total 0/2 0/2 0/3
#> WEEK 5 DAY 36
#> Low
#> Not low 0/2 0/2 0/5
#> Low 0/0 1/1 (100%) 0/1
#> Total 0/2 1/3 (33.3%) 0/6
#> High
#> Not high 1/2 (50%) 0/3 0/6
#> High 0/0 0/0 0/0
#> Total 1/2 (50%) 0/3 0/6
#> Immunoglobulin A Measurement
#> WEEK 1 DAY 8
#> Low
#> Not low 0/6 0/6 0/2
#> Low 0/0 0/0 0/0
#> Total 0/6 0/6 0/2
#> High
#> Not high 0/5 1/6 (16.7%) 0/2
#> High 0/1 0/0 0/0
#> Total 0/6 1/6 (16.7%) 0/2
#> WEEK 2 DAY 15
#> Low
#> Not low 0/3 0/7 0/4
#> Low 0/0 0/0 0/0
#> Total 0/3 0/7 0/4
#> High
#> Not high 0/3 0/7 1/4 (25%)
#> High 0/0 0/0 0/0
#> Total 0/3 0/7 1/4 (25%)
#> WEEK 3 DAY 22
#> Low
#> Not low 0/4 0/5 0/9
#> Low 0/0 0/0 0/0
#> Total 0/4 0/5 0/9
#> High
#> Not high 0/3 0/5 0/8
#> High 0/1 0/0 0/1
#> Total 0/4 0/5 0/9
#> WEEK 4 DAY 29
#> Low
#> Not low 0/2 0/6 0/4
#> Low 0/0 0/0 0/0
#> Total 0/2 0/6 0/4
#> High
#> Not high 1/1 (100%) 0/6 0/3
#> High 0/1 0/0 0/1
#> Total 1/2 (50%) 0/6 0/4
#> WEEK 5 DAY 36
#> Low
#> Not low 0/6 0/5 0/5
#> Low 0/0 0/0 0/0
#> Total 0/6 0/5 0/5
#> High
#> Not high 0/5 0/5 0/5
#> High 0/1 0/0 0/0
#> Total 0/6 0/5 0/5
NCI CTCAE
Grade Post-Baseline (LBT07
)NCI CTCAE
Grade Post-Baselinelbt07
template produces the
standard laboratory test results with highest NCI CTCAE
grade post-baseline summary.NCI CTCAE
grade will be incorporated in
future release.run(lbt07, syn_data)
#> Parameter
#> Direction of Abnormality A: Drug X B: Placebo C: Combination
#> Highest NCI CTCAE Grade (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement (n) 15 15 15
#> LOW
#> 1 3 (20.0%) 0 0
#> 2 2 (13.3%) 1 (6.7%) 1 (6.7%)
#> 3 1 (6.7%) 1 (6.7%) 6 (40.0%)
#> 4 3 (20.0%) 2 (13.3%) 3 (20.0%)
#> Any 9 (60.0%) 4 (26.7%) 10 (66.7%)
#> C-Reactive Protein Measurement (n) 15 15 15
#> LOW
#> 1 2 (13.3%) 1 (6.7%) 2 (13.3%)
#> 2 5 (33.3%) 2 (13.3%) 5 (33.3%)
#> 3 3 (20.0%) 4 (26.7%) 3 (20.0%)
#> 4 0 1 (6.7%) 0
#> Any 10 (66.7%) 8 (53.3%) 10 (66.7%)
#> HIGH
#> 1 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> 2 4 (26.7%) 4 (26.7%) 2 (13.3%)
#> 3 1 (6.7%) 2 (13.3%) 4 (26.7%)
#> 4 0 1 (6.7%) 0
#> Any 8 (53.3%) 8 (53.3%) 7 (46.7%)
#> Immunoglobulin A Measurement (n) 15 15 15
#> HIGH
#> 1 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> 2 5 (33.3%) 4 (26.7%) 2 (13.3%)
#> 3 3 (20.0%) 3 (20.0%) 2 (13.3%)
#> 4 0 0 1 (6.7%)
#> Any 11 (73.3%) 8 (53.3%) 6 (40.0%)
NCI-CTCAE
Grade Post-Baseline by Baseline
NCI-CTCAE
Grade (LBT14
)NCI-CTCAE
Grade Post-Baseline by Baseline
NCI-CTCAE
Grade (High)To produce the standard laboratory test results shift table - highest
NCI-CTCAE
grade post-baseline by baseline
NCI-CTCAE
grade summary for high abnormalities, use the
lbt14
template and set the parameter
direction
to high
.
run(lbt14, syn_data, direction = "high")
#> Baseline Toxicity Grade A: Drug X B: Placebo C: Combination
#> Post-baseline NCI-CTCAE Grade (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement
#> Not High 15 15 15
#> Not High 15 (100%) 15 (100%) 15 (100%)
#> C-Reactive Protein Measurement
#> Not High 15 13 14
#> Not High 7 (46.7%) 7 (46.7%) 8 (53.3%)
#> 1 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> 2 4 (26.7%) 3 (20.0%) 1 (6.7%)
#> 3 1 (6.7%) 1 (6.7%) 4 (26.7%)
#> 4 0 1 (6.7%) 0
#> 1 0 0 1
#> 2 0 0 1 (6.7%)
#> 3 0 1 0
#> 2 0 1 (6.7%) 0
#> 4 0 1 0
#> 3 0 1 (6.7%) 0
#> Immunoglobulin A Measurement
#> Not High 12 14 13
#> Not High 3 (20.0%) 7 (46.7%) 8 (53.3%)
#> 1 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> 2 3 (20.0%) 3 (20.0%) 2 (13.3%)
#> 3 3 (20.0%) 3 (20.0%) 2 (13.3%)
#> 1 2 0 1
#> Not High 1 (6.7%) 0 1 (6.7%)
#> 2 1 (6.7%) 0 0
#> 3 0 0 1
#> 4 0 0 1 (6.7%)
#> 4 1 1 0
#> 2 1 (6.7%) 1 (6.7%) 0
NCI-CTCAE
Grade Post-Baseline by Baseline
NCI-CTCAE
Grade (Low)To produce the standard laboratory test results shift table - highest
NCI-CTCAE
grade post-baseline by baseline
NCI-CTCAE
grade summary for high abnormalities, use the
lbt14
template and the argument
direction
is low
by default.
run(lbt14, syn_data)
#> Baseline Toxicity Grade A: Drug X B: Placebo C: Combination
#> Post-baseline NCI-CTCAE Grade (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement
#> Not Low 12 12 14
#> Not Low 5 (33.3%) 8 (53.3%) 5 (33.3%)
#> 1 3 (20.0%) 0 0
#> 2 2 (13.3%) 1 (6.7%) 1 (6.7%)
#> 3 0 1 (6.7%) 5 (33.3%)
#> 4 2 (13.3%) 2 (13.3%) 3 (20.0%)
#> 1 1 2 0
#> Not Low 1 (6.7%) 2 (13.3%) 0
#> 2 1 1 0
#> Not Low 0 1 (6.7%) 0
#> 4 1 (6.7%) 0 0
#> 3 1 0 1
#> 3 1 (6.7%) 0 1 (6.7%)
#> C-Reactive Protein Measurement
#> Not Low 14 13 12
#> Not Low 5 (33.3%) 7 (46.7%) 4 (26.7%)
#> 1 2 (13.3%) 0 2 (13.3%)
#> 2 5 (33.3%) 2 (13.3%) 4 (26.7%)
#> 3 2 (13.3%) 3 (20.0%) 2 (13.3%)
#> 4 0 1 (6.7%) 0
#> 1 0 0 2
#> Not Low 0 0 1 (6.7%)
#> 2 0 0 1 (6.7%)
#> 2 0 1 0
#> 1 0 1 (6.7%) 0
#> 3 1 1 1
#> 3 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> Immunoglobulin A Measurement
#> Not Low 15 15 15
#> Not Low 15 (100%) 15 (100%) 15 (100%)
NCI-CTCAE
Grade Post-Baseline by Baseline
NCI-CTCAE
Grade (High) Without Patients with Missing
BaselineTo exclude patients with missing baseline grade, set the argument
gr_missing
to excl
.
run(lbt14, syn_data, direction = "high", gr_missing = "excl")
#> Baseline Toxicity Grade A: Drug X B: Placebo C: Combination
#> Post-baseline NCI-CTCAE Grade (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement
#> Not High 15 15 15
#> Not High 15 (100%) 15 (100%) 15 (100%)
#> C-Reactive Protein Measurement
#> Not High 15 13 14
#> Not High 7 (46.7%) 7 (46.7%) 8 (53.3%)
#> 1 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> 2 4 (26.7%) 3 (20.0%) 1 (6.7%)
#> 3 1 (6.7%) 1 (6.7%) 4 (26.7%)
#> 4 0 1 (6.7%) 0
#> 1 0 0 1
#> 2 0 0 1 (6.7%)
#> 3 0 1 0
#> 2 0 1 (6.7%) 0
#> 4 0 1 0
#> 3 0 1 (6.7%) 0
#> Immunoglobulin A Measurement
#> Not High 12 14 13
#> Not High 3 (20.0%) 7 (46.7%) 8 (53.3%)
#> 1 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> 2 3 (20.0%) 3 (20.0%) 2 (13.3%)
#> 3 3 (20.0%) 3 (20.0%) 2 (13.3%)
#> 1 2 0 1
#> Not High 1 (6.7%) 0 1 (6.7%)
#> 2 1 (6.7%) 0 0
#> 3 0 0 1
#> 4 0 0 1 (6.7%)
#> 4 1 1 0
#> 2 1 (6.7%) 1 (6.7%) 0
NCI-CTCAE
Grade Post-Baseline by Baseline
NCI-CTCAE
Grade (Low) with Missing Baseline Considered as
Grade 0To count patients with missing baseline grade as grade 0, set the
argument gr_missing
to gr_0
.
run(lbt14, syn_data, gr_missing = "gr_0")
#> Baseline Toxicity Grade A: Drug X B: Placebo C: Combination
#> Post-baseline NCI-CTCAE Grade (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement
#> 1 1 2 0
#> Not Low 1 (6.7%) 2 (13.3%) 0
#> 2 1 1 0
#> Not Low 0 1 (6.7%) 0
#> 4 1 (6.7%) 0 0
#> 3 1 0 1
#> 3 1 (6.7%) 0 1 (6.7%)
#> Not Low 12 12 14
#> Not Low 5 (33.3%) 8 (53.3%) 5 (33.3%)
#> 1 3 (20.0%) 0 0
#> 2 2 (13.3%) 1 (6.7%) 1 (6.7%)
#> 3 0 1 (6.7%) 5 (33.3%)
#> 4 2 (13.3%) 2 (13.3%) 3 (20.0%)
#> C-Reactive Protein Measurement
#> 1 0 0 2
#> 1 0 0 1 (6.7%)
#> 3 0 0 1 (6.7%)
#> 2 0 1 0
#> 2 0 1 (6.7%) 0
#> 3 1 1 1
#> 3 1 (6.7%) 1 (6.7%) 1 (6.7%)
#> Not Low 14 13 12
#> Not Low 5 (33.3%) 7 (46.7%) 4 (26.7%)
#> 1 2 (13.3%) 0 2 (13.3%)
#> 2 5 (33.3%) 2 (13.3%) 4 (26.7%)
#> 3 2 (13.3%) 3 (20.0%) 2 (13.3%)
#> 4 0 1 (6.7%) 0
#> Immunoglobulin A Measurement
#> Not Low 15 15 15
#> Not Low 15 (100%) 15 (100%) 15 (100%)
NCI-CTCAE
Grade Post-Baseline by Baseline
NCI-CTCAE
Grade (with fill in of grades)To display all possible grades even if they do not occur in the data,
set the argument prune_0
to FALSE
.
run(lbt14, syn_data, direction = "high", prune_0 = FALSE)
#> Baseline Toxicity Grade A: Drug X B: Placebo C: Combination
#> Post-baseline NCI-CTCAE Grade (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————
#> Alanine Aminotransferase Measurement
#> Not High 15 15 15
#> Not High 15 (100%) 15 (100%) 15 (100%)
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 1 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 2 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 3 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 4 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> Missing 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> C-Reactive Protein Measurement
#> Not High 15 13 14
#> Not High 7 (46.7%) 7 (46.7%) 8 (53.3%)
#> 1 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> 2 4 (26.7%) 3 (20.0%) 1 (6.7%)
#> 3 1 (6.7%) 1 (6.7%) 4 (26.7%)
#> 4 0 1 (6.7%) 0
#> Missing 0 0 0
#> 1 0 0 1
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 1 (6.7%)
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 2 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 3 0 1 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 1 (6.7%) 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 4 0 1 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 1 (6.7%) 0
#> 4 0 0 0
#> Missing 0 0 0
#> Missing 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> Immunoglobulin A Measurement
#> Not High 12 14 13
#> Not High 3 (20.0%) 7 (46.7%) 8 (53.3%)
#> 1 3 (20.0%) 1 (6.7%) 1 (6.7%)
#> 2 3 (20.0%) 3 (20.0%) 2 (13.3%)
#> 3 3 (20.0%) 3 (20.0%) 2 (13.3%)
#> 4 0 0 0
#> Missing 0 0 0
#> 1 2 0 1
#> Not High 1 (6.7%) 0 1 (6.7%)
#> 1 0 0 0
#> 2 1 (6.7%) 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 2 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> 3 0 0 1
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 1 (6.7%)
#> Missing 0 0 0
#> 4 1 1 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 1 (6.7%) 1 (6.7%) 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
#> Missing 0 0 0
#> Not High 0 0 0
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> Missing 0 0 0
MHT01
)mht01
template displays medical
conditions by MedDRA system organ class and Preferred Name by
default."ADSL.ARM"
.run(mht01, 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 condition 13 (86.7%) 14 (93.3%) 15 (100%)
#> Total number of conditions 58 59 99
#> cl A
#> Total number of patients with at least one condition 7 (46.7%) 6 (40.0%) 10 (66.7%)
#> Total number of conditions 8 11 16
#> trm A_2/2 5 (33.3%) 6 (40.0%) 6 (40.0%)
#> trm A_1/2 3 (20.0%) 1 (6.7%) 6 (40.0%)
#> cl B
#> Total number of patients with at least one condition 12 (80.0%) 11 (73.3%) 12 (80.0%)
#> Total number of conditions 24 21 32
#> trm B_3/3 8 (53.3%) 6 (40.0%) 7 (46.7%)
#> trm B_1/3 5 (33.3%) 6 (40.0%) 8 (53.3%)
#> trm B_2/3 5 (33.3%) 6 (40.0%) 5 (33.3%)
#> cl C
#> Total number of patients with at least one condition 8 (53.3%) 6 (40.0%) 11 (73.3%)
#> Total number of conditions 10 13 22
#> trm C_2/2 6 (40.0%) 4 (26.7%) 8 (53.3%)
#> trm C_1/2 4 (26.7%) 4 (26.7%) 5 (33.3%)
#> cl D
#> Total number of patients with at least one condition 10 (66.7%) 7 (46.7%) 13 (86.7%)
#> Total number of conditions 16 14 29
#> trm D_1/3 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> trm D_2/3 6 (40.0%) 2 (13.3%) 7 (46.7%)
#> trm D_3/3 2 (13.3%) 5 (33.3%) 7 (46.7%)
run(mht01, syn_data, lbl_overall = "All Patients")
#> MedDRA System Organ Class A: Drug X B: Placebo C: Combination All Patients
#> MedDRA Preferred Term (N=15) (N=15) (N=15) (N=45)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one condition 13 (86.7%) 14 (93.3%) 15 (100%) 42 (93.3%)
#> Total number of conditions 58 59 99 216
#> cl A
#> Total number of patients with at least one condition 7 (46.7%) 6 (40.0%) 10 (66.7%) 23 (51.1%)
#> Total number of conditions 8 11 16 35
#> trm A_2/2 5 (33.3%) 6 (40.0%) 6 (40.0%) 17 (37.8%)
#> trm A_1/2 3 (20.0%) 1 (6.7%) 6 (40.0%) 10 (22.2%)
#> cl B
#> Total number of patients with at least one condition 12 (80.0%) 11 (73.3%) 12 (80.0%) 35 (77.8%)
#> Total number of conditions 24 21 32 77
#> trm B_3/3 8 (53.3%) 6 (40.0%) 7 (46.7%) 21 (46.7%)
#> trm B_1/3 5 (33.3%) 6 (40.0%) 8 (53.3%) 19 (42.2%)
#> trm B_2/3 5 (33.3%) 6 (40.0%) 5 (33.3%) 16 (35.6%)
#> cl C
#> Total number of patients with at least one condition 8 (53.3%) 6 (40.0%) 11 (73.3%) 25 (55.6%)
#> Total number of conditions 10 13 22 45
#> trm C_2/2 6 (40.0%) 4 (26.7%) 8 (53.3%) 18 (40.0%)
#> trm C_1/2 4 (26.7%) 4 (26.7%) 5 (33.3%) 13 (28.9%)
#> cl D
#> Total number of patients with at least one condition 10 (66.7%) 7 (46.7%) 13 (86.7%) 30 (66.7%)
#> Total number of conditions 16 14 29 59
#> trm D_1/3 4 (26.7%) 4 (26.7%) 7 (46.7%) 15 (33.3%)
#> trm D_2/3 6 (40.0%) 2 (13.3%) 7 (46.7%) 15 (33.3%)
#> trm D_3/3 2 (13.3%) 5 (33.3%) 7 (46.7%) 14 (31.1%)
PDT01
)pdt01
template produces the
standard major protocol deviations output.addv
to only include
records where DVCAT == "MAJOR"
in pre-processing.proc_data <- syn_data
proc_data$addv <- proc_data$addv %>%
filter(DVCAT == "MAJOR")
run(pdt01, proc_data)
#> Category A: Drug X B: Placebo C: Combination
#> Description (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one major protocol deviation 2 (13.3%) 4 (26.7%) 0
#> Total number of major protocol deviations 2 5 0
#> EXCLUSION CRITERIA
#> Active or untreated or other excluded cns metastases 0 1 (6.7%) 0
#> Pregnancy criteria 0 1 (6.7%) 0
#> INCLUSION CRITERIA
#> Ineligible cancer type or current cancer stage 1 (6.7%) 0 0
#> MEDICATION
#> Discontinued study drug for unspecified reason 0 1 (6.7%) 0
#> Received prohibited concomitant medication 0 1 (6.7%) 0
#> PROCEDURAL
#> Eligibility-related test not done/out of window 0 1 (6.7%) 0
#> Failure to sign updated ICF within two visits 1 (6.7%) 0 0
RMPT01
)The rmpt01
template produces the
standard duration of exposure output for the Risk Management Plan
(RMP
).
Person time is the sum of exposure across all patients in days.
run(rmpt01, syn_data)
#> Patients Person time
#> Duration of exposure (N=45) (N=45)
#> —————————————————————————————————————————————————————————————
#> < 1 month 4 (8.9%) 67
#> 1 to <3 months 13 (28.9%) 837
#> 3 to <6 months 13 (28.9%) 1728
#> >=6 months 15 (33.3%) 3281
#> Total patients number/person time 45 (100.0%) 5913
RMPT03
)The rmpt03
template produces the
standard extent of exposure by age group and gender output for the Risk
Management Plan (RMP
).
By default, the AGEGR1
variable is used as the age
group. If AGEGR1
is available in ADSL
only but
not in ADEX
, it needs to be added to ADEX
first.
proc_data <- syn_data
proc_data <- propagate(proc_data, "adsl", "AGEGR1", "USUBJID")
#>
#> Updating: adae with: AGEGR1
#> Updating: adsaftte with: AGEGR1
#> Updating: adcm with: AGEGR1
#> Updating: addv with: AGEGR1
#> Updating: adeg with: AGEGR1
#> Updating: adex with: AGEGR1
#> Updating: adlb with: AGEGR1
#> Updating: admh with: AGEGR1
#> Skipping: adrs
#> Updating: adsub with: AGEGR1
#> Skipping: adtte
#> Updating: advs with: AGEGR1
run(rmpt03, proc_data)
#> F M All Genders
#> Patients Person time Patients Person time Patients Person time
#> Age Group (N=30) (N=30) (N=15) (N=15) (N=45) (N=45)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> <65 30 (100.0%) 4088 15 (100.0%) 1825 45 (100.0%) 5913
#> Total patients number/person time 30 (100.0%) 4088 15 (100.0%) 1825 45 (100.0%) 5913
Any other study specific age group can be used by editing the
parameter summaryvars
. For all RMP
tables, if
the variable specified per summaryvars
is unavailable in
ADEX
, it needs to be added to ADEX
first.
proc_data <- syn_data
proc_data$adsl <- proc_data$adsl %>%
mutate(
AGEGR2 = with_label(
factor(case_when(
AAGE < 18 ~ "<18",
AAGE >= 18 & AAGE <= 65 ~ "18 - 65",
AAGE > 65 ~ ">65",
), levels = c("<18", "18 - 65", ">65")),
"Age Group 2"
)
)
proc_data <- propagate(proc_data, "adsl", "AGEGR2", "USUBJID")
#>
#> Updating: adae with: AGEGR2
#> Updating: adsaftte with: AGEGR2
#> Updating: adcm with: AGEGR2
#> Updating: addv with: AGEGR2
#> Updating: adeg with: AGEGR2
#> Updating: adex with: AGEGR2
#> Updating: adlb with: AGEGR2
#> Updating: admh with: AGEGR2
#> Updating: adrs with: AGEGR2
#> Updating: adsub with: AGEGR2
#> Updating: adtte with: AGEGR2
#> Updating: advs with: AGEGR2
run(rmpt03, proc_data, summaryvars = "AGEGR2")
#> F M All Genders
#> Patients Person time Patients Person time Patients Person time
#> Age Group 2 (N=30) (N=30) (N=15) (N=15) (N=45) (N=45)
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> 18 - 65 30 (100.0%) 4088 15 (100.0%) 1825 45 (100.0%) 5913
#> Total patients number/person time 30 (100.0%) 4088 15 (100.0%) 1825 45 (100.0%) 5913
RMPT04
)The rmpt04
template produces the
standard extent of exposure by ethnic origin output for the Risk
Management Plan (RMP
).
RMPT05
)The rmpt05
template produces the
standard extent of exposure by race output for the Risk Management Plan
(RMP
).
run(rmpt05, syn_data)
#> Patients Person time
#> RACE (N=45) (N=45)
#> —————————————————————————————————————————————————————————————
#> ASIAN 26 (57.8%) 3309
#> BLACK OR AFRICAN AMERICAN 9 (20.0%) 1139
#> WHITE 7 (15.6%) 1231
#> AMERICAN INDIAN OR ALASKA NATIVE 3 (6.7%) 234
#> Total patients number/person time 45 (100.0%) 5913
RSPT01
)rspt01
template produces the
standard best overall response output.RECIST 1.1
. By
default, the subjects with response results of "CR"
or
"PR"
are considered as responders.BICR
.proc_data <- log_filter(syn_data, PARAMCD == "BESRSPI", "adrs")
run(rspt01, proc_data, ref_group = NULL, perform_analysis = "unstrat", strata = NULL)
#> Warning in stats::prop.test(tbl, correct = FALSE): Chi-squared approximation
#> may be incorrect
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————————
#> Responders 10 (66.7%) 9 (60.0%) 11 (73.3%)
#> 95% CI (Wald, with correction) (39.5, 93.9) (31.9, 88.1) (47.6, 99.0)
#> Unstratified Analysis
#> Difference in Response rate (%) -6.7 6.7
#> 95% CI (Wald, with correction) (-47.7, 34.4) (-32.7, 46.0)
#> p-value (Chi-Squared Test) 0.7048 0.6903
#> Odds Ratio (95% CI) 0.75 (0.17 - 3.33) 1.37 (0.29 - 6.60)
#> Complete Response (CR) 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> 95% CI (Wald, with correction) (0.95, 52.38) (0.95, 52.38) (18.09, 75.25)
#> Partial Response (PR) 6 (40.0%) 5 (33.3%) 4 (26.7%)
#> 95% CI (Wald, with correction) (11.87, 68.13) (6.14, 60.52) (0.95, 52.38)
#> Stable Disease (SD) 5 (33.3%) 6 (40.0%) 4 (26.7%)
#> 95% CI (Wald, with correction) (6.14, 60.52) (11.87, 68.13) (0.95, 52.38)
arm_var
(default to "ADSL.ARM"
unless specified) is treated as the
reference group without specification.ref_group
, e.g.,
ref_group = "PLACEBO"
.rtables
displays the reference group at the very
left column, the order of displayed treatment groups may not be exactly
the same as the order factorized, depending on which group is selected
as the reference group. See below for examples:Factorized trt order |
ref_group |
Displayed trt order |
Reference group used in analysis |
---|---|---|---|
ARM C, ARM B, ARM A | NULL | ARM C, ARM B, ARM A | ARM C |
NULL | ARM B | ARM B, ARM A, ARM C | ARM B |
ARM C, ARM B, ARM A | ARM B | ARM B, ARM C, ARM A | ARM B |
Odds Ratio
can be suppressed with the
argument odds_ratio = FALSE
.Difference in response rate
can be
suppressed with the argument perform_analysis = NULL
.proc_data <- log_filter(syn_data, PARAMCD == "BESRSPI", "adrs")
run(rspt01, proc_data, odds_ratio = FALSE, perform_analysis = NULL)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————
#> Responders 10 (66.7%) 9 (60.0%) 11 (73.3%)
#> 95% CI (Wald, with correction) (39.5, 93.9) (31.9, 88.1) (47.6, 99.0)
#> Complete Response (CR) 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> 95% CI (Wald, with correction) (0.95, 52.38) (0.95, 52.38) (18.09, 75.25)
#> Partial Response (PR) 6 (40.0%) 5 (33.3%) 4 (26.7%)
#> 95% CI (Wald, with correction) (11.87, 68.13) (6.14, 60.52) (0.95, 52.38)
#> Stable Disease (SD) 5 (33.3%) 6 (40.0%) 4 (26.7%)
#> 95% CI (Wald, with correction) (6.14, 60.52) (11.87, 68.13) (0.95, 52.38)
perform_analysis = "strat"
and providing the stratification
variable to the argument strata
. The argument
strata
is expected if perform_analysis
is set
to include stratified analysis.adrs
.perform_analysis = c("unstrat", "strat")
proc_data <- log_filter(syn_data, PARAMCD == "BESRSPI", "adrs")
run(rspt01, proc_data, perform_analysis = "strat", strata = c("STRATA1", "STRATA2"))
#> Warning in prop_diff_cmh(rsp, grp, strata, conf_level): Less than 5
#> observations in some strata.
#> Warning in prop_diff_cmh(rsp, grp, strata, conf_level): Less than 5
#> observations in some strata.
#> Warning in prop_cmh(tbl): <5 data points in some strata. CMH test may be
#> incorrect.
#> Warning in prop_cmh(tbl): <5 data points in some strata. CMH test may be
#> incorrect.
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————
#> Responders 10 (66.7%) 9 (60.0%) 11 (73.3%)
#> 95% CI (Wald, with correction) (39.5, 93.9) (31.9, 88.1) (47.6, 99.0)
#> Stratified Analysis
#> Difference in Response rate (%) -11.0 22.5
#> 95% CI (CMH, without correction) (-42.7, 20.7) (-3.5, 48.5)
#> p-value (Cochran-Mantel-Haenszel Test) 0.5731 0.3088
#> Odds Ratio (95% CI) 0.75 (0.17 - 3.33) 1.37 (0.29 - 6.60)
#> Complete Response (CR) 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> 95% CI (Wald, with correction) (0.95, 52.38) (0.95, 52.38) (18.09, 75.25)
#> Partial Response (PR) 6 (40.0%) 5 (33.3%) 4 (26.7%)
#> 95% CI (Wald, with correction) (11.87, 68.13) (6.14, 60.52) (0.95, 52.38)
#> Stable Disease (SD) 5 (33.3%) 6 (40.0%) 4 (26.7%)
#> 95% CI (Wald, with correction) (6.14, 60.52) (11.87, 68.13) (0.95, 52.38)
conf_level
.methods
. It is a named list with
five optional sub-arguments. For example,
methods = list(prop_conf_method = "wald", diff_conf_method = "wald", strat_diff_conf_method = "ha", diff_pval_method = "fisher", strat_diff_pval_method = "schouten")
See table below for what each argument controls and the available method options:
Arguments | Methods Controlled | Methods Options |
---|---|---|
prop_conf_method |
proportion confidence interval | "waldcc" (default), "wald" ,
etc. |
diff_conf_method |
unstratified difference confidence interval | "waldcc" (default), "wald" ,
etc. |
diff_pval_method |
unstratified p-value for odds ratio | "chisq" (default),
"fisher" |
strat_diff_conf_method |
stratified difference confidence interval | "cmh" (default), "ha" |
strat_diff_pval_method |
stratified p-value for odds ratio | "cmh" (default),
"schouten" |
See in the table below the method options for estimates of proportions and the associated statistical methods:
Method Options | Statistical Methods |
---|---|
"clopper-pearson" |
Clopper-Pearson |
"wald" |
Wald, without correction |
"waldcc" |
Wald, with correction |
"wilson" |
Wilson, without correction |
"strat_wilson" |
Stratified Wilson, without correction |
"wilsonc" |
Wilson, with correction |
"strat_wilsonc" |
Stratified Wilson, with correction |
"agresti-coull" |
Agresti-Coull |
"jeffreys" |
Jeffreys |
See in the table below the method options for estimates of proportion difference and the associated statistical methods:
Method Options | Statistical Methods |
---|---|
"cmh" |
CMH , without correction |
"wald" |
Wald, with correction |
"waldcc" |
Wald, without correction |
"ha" |
Anderson-Hauck |
"newcombe" |
Newcombe, without correction |
"newcombecc" |
Newcombe, with correction |
"strat_wilsonc" |
Stratified Wilson, with correction |
"strat_newcombe" |
Stratified Newcombe, without correction |
"strat_newcombecc" |
Stratified Newcombe, with correction |
See in the table below the method options for testing proportion difference and the associated statistical methods:
Method Options | Statistical Methods |
---|---|
"chisq" |
Chi-Squared test |
"fisher" |
the Fisher’s exact test |
"cmh" |
stratified Cochran-Mantel-Haenszel test |
"shouten" |
Chi-Squared test with Schouten correction |
An example:
proc_data <- log_filter(syn_data, PARAMCD == "BESRSPI", "adrs")
run(rspt01, proc_data,
conf_level = 0.90,
methods = list(
prop_conf_method = "wald",
diff_conf_method = "wald",
diff_pval_method = "fisher"
)
)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————————————————
#> Responders 10 (66.7%) 9 (60.0%) 11 (73.3%)
#> 90% CI (Wald, without correction) (46.6, 86.7) (39.2, 80.8) (54.6, 92.1)
#> Unstratified Analysis
#> Difference in Response rate (%) -6.7 6.7
#> 90% CI (Wald, without correction) (-35.5, 22.2) (-20.8, 34.1)
#> p-value (Fisher's Exact Test) 1.0000 1.0000
#> Odds Ratio (95% CI) 0.75 (0.17 - 3.33) 1.37 (0.29 - 6.60)
#> Complete Response (CR) 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> 90% CI (Wald, without correction) (7.89, 45.45) (7.89, 45.45) (25.48, 67.85)
#> Partial Response (PR) 6 (40.0%) 5 (33.3%) 4 (26.7%)
#> 90% CI (Wald, without correction) (19.19, 60.81) (13.31, 53.35) (7.89, 45.45)
#> Stable Disease (SD) 5 (33.3%) 6 (40.0%) 4 (26.7%)
#> 90% CI (Wald, without correction) (13.31, 53.35) (19.19, 60.81) (7.89, 45.45)
The following example shows how to customize the definition of responder, e.g, consider only complete response as response.
proc_data <- log_filter(syn_data, PARAMCD == "BESRSPI", "adrs")
preprocess(rspt01) <- function(adam_db, ...) {
adam_db$adrs <- adam_db$adrs %>%
mutate(RSP_LAB = tern::d_onco_rsp_label(.data$AVALC)) %>%
mutate(IS_RSP = .data$AVALC %in% c("CR"))
adam_db
}
run(rspt01, proc_data)
#> Warning in stats::prop.test(tbl, correct = FALSE): Chi-squared approximation
#> may be incorrect
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ——————————————————————————————————————————————————————————————————————————————————————————————
#> Responders 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> 95% CI (Wald, with correction) (1.0, 52.4) (1.0, 52.4) (18.1, 75.2)
#> Unstratified Analysis
#> Difference in Response rate (%) 0.0 20.0
#> 95% CI (Wald, with correction) (-38.3, 38.3) (-20.4, 60.4)
#> p-value (Chi-Squared Test) 1.0000 0.2557
#> Odds Ratio (95% CI) 1.00 (0.20 - 5.04) 2.41 (0.52 - 11.10)
#> Complete Response (CR) 4 (26.7%) 4 (26.7%) 7 (46.7%)
#> 95% CI (Wald, with correction) (0.95, 52.38) (0.95, 52.38) (18.09, 75.25)
#> Partial Response (PR) 6 (40.0%) 5 (33.3%) 4 (26.7%)
#> 95% CI (Wald, with correction) (11.87, 68.13) (6.14, 60.52) (0.95, 52.38)
#> Stable Disease (SD) 5 (33.3%) 6 (40.0%) 4 (26.7%)
#> 95% CI (Wald, with correction) (6.14, 60.52) (11.87, 68.13) (0.95, 52.38)
TTET01
)ttet01
template produces the
standard time-to-event summary.PARAMCD == "PFS"
) in pre-processing.proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
run(ttet01, proc_data)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 7 (46.7%) 12 (80%) 8 (53.3%)
#> Earliest contributing event
#> Death 5 11 7
#> Disease Progression 2 1 1
#> Patients without event (%) 8 (53.3%) 3 (20%) 7 (46.7%)
#> Time to Event (MONTHS)
#> Median 8.6 6.2 8.4
#> 95% CI (7.3, NE) (4.8, 7.6) (7.0, NE)
#> 25% and 75%-ile 3.8, NE 4.7, 8.4 5.8, NE
#> Range 1.2 to 9.5 {1} 0.9 to 9.1 0.9 to 9.5 {1}
#> Unstratified Analysis
#> p-value (log-rank) 0.0973 0.9111
#> Hazard Ratio 2.18 1.06
#> 95% CI (0.85, 5.60) (0.38, 2.94)
#> 6 MONTHS
#> Patients remaining at risk 11 8 11
#> Event Free Rate (%) 73.33 53.33 73.33
#> 95% CI (50.95, 95.71) (28.09, 78.58) (50.95, 95.71)
#> Difference in Event Free Rate -20.00 0.00
#> 95% CI (-53.74, 13.74) (-31.65, 31.65)
#> p-value (Z-test) 0.2453 1.0000
#> ————————————————————————————————————————————————————————————————————————————————————
#>
#> {1} - Censored observation: range maximum
#> ————————————————————————————————————————————————————————————————————————————————————
To suspend the section of earliest contributing events, use
summarize_event = FALSE
.
proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
run(ttet01, proc_data, summarize_event = FALSE)
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 7 (46.7%) 12 (80%) 8 (53.3%)
#> Patients without event (%) 8 (53.3%) 3 (20%) 7 (46.7%)
#> Time to Event (MONTHS)
#> Median 8.6 6.2 8.4
#> 95% CI (7.3, NE) (4.8, 7.6) (7.0, NE)
#> 25% and 75%-ile 3.8, NE 4.7, 8.4 5.8, NE
#> Range 1.2 to 9.5 {1} 0.9 to 9.1 0.9 to 9.5 {1}
#> Unstratified Analysis
#> p-value (log-rank) 0.0973 0.9111
#> Hazard Ratio 2.18 1.06
#> 95% CI (0.85, 5.60) (0.38, 2.94)
#> 6 MONTHS
#> Patients remaining at risk 11 8 11
#> Event Free Rate (%) 73.33 53.33 73.33
#> 95% CI (50.95, 95.71) (28.09, 78.58) (50.95, 95.71)
#> Difference in Event Free Rate -20.00 0.00
#> 95% CI (-53.74, 13.74) (-31.65, 31.65)
#> p-value (Z-test) 0.2453 1.0000
#> ————————————————————————————————————————————————————————————————————————————————————
#>
#> {1} - Censored observation: range maximum
#> ————————————————————————————————————————————————————————————————————————————————————
To select either survival estimations or difference in survival or
both, please specify in the argument method
. -
surv
calls out the analysis of patients remaining at risk,
event free rate and corresponding 95% confidence interval of the rates.
- surv_diff
calls out the analysis of difference in event
free rate, the 95% confidence interval of the difference and its
corresponding p-value. - both
calls out both.
proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
run(ttet01, proc_data, method = "surv")
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 7 (46.7%) 12 (80%) 8 (53.3%)
#> Earliest contributing event
#> Death 5 11 7
#> Disease Progression 2 1 1
#> Patients without event (%) 8 (53.3%) 3 (20%) 7 (46.7%)
#> Time to Event (MONTHS)
#> Median 8.6 6.2 8.4
#> 95% CI (7.3, NE) (4.8, 7.6) (7.0, NE)
#> 25% and 75%-ile 3.8, NE 4.7, 8.4 5.8, NE
#> Range 1.2 to 9.5 {1} 0.9 to 9.1 0.9 to 9.5 {1}
#> Unstratified Analysis
#> p-value (log-rank) 0.0973 0.9111
#> Hazard Ratio 2.18 1.06
#> 95% CI (0.85, 5.60) (0.38, 2.94)
#> 6 MONTHS
#> Patients remaining at risk 11 8 11
#> Event Free Rate (%) 73.33 53.33 73.33
#> 95% CI (50.95, 95.71) (28.09, 78.58) (50.95, 95.71)
#> ————————————————————————————————————————————————————————————————————————————————
#>
#> {1} - Censored observation: range maximum
#> ————————————————————————————————————————————————————————————————————————————————
conf_level
.conf_type
. Options are "plain"
(default),
"log"
and "log-log"
.ties
.
Options are "efron"
(default),"breslow"
or
"exact"
.proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
run(ttet01, proc_data, conf_level = 0.90, conf_type = "log-log", ties = "efron")
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 7 (46.7%) 12 (80%) 8 (53.3%)
#> Earliest contributing event
#> Death 5 11 7
#> Disease Progression 2 1 1
#> Patients without event (%) 8 (53.3%) 3 (20%) 7 (46.7%)
#> Time to Event (MONTHS)
#> Median 8.6 6.2 8.4
#> 90% CI (3.8, NE) (4.7, 7.6) (5.8, NE)
#> 25% and 75%-ile 3.8, NE 4.7, 8.4 5.8, NE
#> Range 1.2 to 9.5 {1} 0.9 to 9.1 0.9 to 9.5 {1}
#> Unstratified Analysis
#> p-value (log-rank) 0.0973 0.9111
#> Hazard Ratio 2.18 1.06
#> 90% CI (0.99, 4.81) (0.45, 2.50)
#> 6 MONTHS
#> Patients remaining at risk 11 8 11
#> Event Free Rate (%) 73.33 53.33 73.33
#> 90% CI (49.25, 87.30) (30.65, 71.60) (49.25, 87.30)
#> Difference in Event Free Rate -20.00 0.00
#> 90% CI (-48.31, 8.31) (-26.56, 26.56)
#> p-value (Z-test) 0.2453 1.0000
#> ———————————————————————————————————————————————————————————————————————————————————
#>
#> {1} - Censored observation: range maximum
#> ———————————————————————————————————————————————————————————————————————————————————
perform_analysis = "strat"
and providing the stratification
variable to the argument strata
. The argument
strata
is expected if perform_analysis
is set
to include stratified analysis.adrs
.perform_analysis = c("unstrat", "strat")
.proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
run(ttet01, proc_data, perform_analysis = "strat", strata = "STRATA1")
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 7 (46.7%) 12 (80%) 8 (53.3%)
#> Earliest contributing event
#> Death 5 11 7
#> Disease Progression 2 1 1
#> Patients without event (%) 8 (53.3%) 3 (20%) 7 (46.7%)
#> Time to Event (MONTHS)
#> Median 8.6 6.2 8.4
#> 95% CI (7.3, NE) (4.8, 7.6) (7.0, NE)
#> 25% and 75%-ile 3.8, NE 4.7, 8.4 5.8, NE
#> Range 1.2 to 9.5 {1} 0.9 to 9.1 0.9 to 9.5 {1}
#> Stratified Analysis
#> p-value (log-rank) 0.0649 0.8901
#> Hazard Ratio 2.52 1.08
#> 95% CI (0.92, 6.93) (0.36, 3.22)
#> 6 MONTHS
#> Patients remaining at risk 11 8 11
#> Event Free Rate (%) 73.33 53.33 73.33
#> 95% CI (50.95, 95.71) (28.09, 78.58) (50.95, 95.71)
#> Difference in Event Free Rate -20.00 0.00
#> 95% CI (-53.74, 13.74) (-31.65, 31.65)
#> p-value (Z-test) 0.2453 1.0000
#> ————————————————————————————————————————————————————————————————————————————————————
#>
#> {1} - Censored observation: range maximum
#> ————————————————————————————————————————————————————————————————————————————————————
The time point for the “survival at xx months” analysis can be
modified by specifying the argument time_point
. By default,
the function takes AVAL
from adtte
in days and
converts it to months. The survival estimates are then summarized in
month, and the numeric values should be provided in months to
time_point
.
proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
run(ttet01, proc_data, perform_analysis = "unstrat", time_point = c(3, 6))
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 7 (46.7%) 12 (80%) 8 (53.3%)
#> Earliest contributing event
#> Death 5 11 7
#> Disease Progression 2 1 1
#> Patients without event (%) 8 (53.3%) 3 (20%) 7 (46.7%)
#> Time to Event (MONTHS)
#> Median 8.6 6.2 8.4
#> 95% CI (7.3, NE) (4.8, 7.6) (7.0, NE)
#> 25% and 75%-ile 3.8, NE 4.7, 8.4 5.8, NE
#> Range 1.2 to 9.5 {1} 0.9 to 9.1 0.9 to 9.5 {1}
#> Unstratified Analysis
#> p-value (log-rank) 0.0973 0.9111
#> Hazard Ratio 2.18 1.06
#> 95% CI (0.85, 5.60) (0.38, 2.94)
#> 3 MONTHS
#> Patients remaining at risk 12 12 13
#> Event Free Rate (%) 80.00 80.00 86.67
#> 95% CI (59.76, 100.00) (59.76, 100.00) (69.46, 100.00)
#> Difference in Event Free Rate 0.00 6.67
#> 95% CI (-28.63, 28.63) (-19.90, 33.23)
#> p-value (Z-test) 1.0000 0.6228
#> 6 MONTHS
#> Patients remaining at risk 11 8 11
#> Event Free Rate (%) 73.33 53.33 73.33
#> 95% CI (50.95, 95.71) (28.09, 78.58) (50.95, 95.71)
#> Difference in Event Free Rate -20.00 0.00
#> 95% CI (-53.74, 13.74) (-31.65, 31.65)
#> p-value (Z-test) 0.2453 1.0000
#> —————————————————————————————————————————————————————————————————————————————————————
#>
#> {1} - Censored observation: range maximum
#> —————————————————————————————————————————————————————————————————————————————————————
The following example shows how to specify the time point in user-defined unit.
proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
preprocess(ttet01) <- function(adam_db, dataset = "adtte",
...) {
adam_db[[dataset]] <- adam_db[[dataset]] %>%
mutate(
AVALU = "DAYS",
IS_EVENT = .data$CNSR == 0,
IS_NOT_EVENT = .data$CNSR == 1,
EVNT1 = factor(
case_when(
IS_EVENT == TRUE ~ render_safe("{Patient_label} with event (%)"),
IS_EVENT == FALSE ~ render_safe("{Patient_label} without event (%)")
),
levels = render_safe(c("{Patient_label} with event (%)", "{Patient_label} without event (%)"))
),
EVNTDESC = factor(.data$EVNTDESC)
)
adam_db
}
run(ttet01, proc_data, perform_analysis = "unstrat", time_point = c(91, 183))
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 7 (46.7%) 12 (80%) 8 (53.3%)
#> Earliest contributing event
#> Death 5 11 7
#> Disease Progression 2 1 1
#> Patients without event (%) 8 (53.3%) 3 (20%) 7 (46.7%)
#> Time to Event (DAYS)
#> Median 261.9 187.7 256.3
#> 95% CI (221.9, NE) (144.7, 232.2) (212.0, NE)
#> 25% and 75%-ile 114.9, NE 141.9, 254.4 175.0, NE
#> Range 37.2 to 288.3 {1} 28.0 to 276.6 26.4 to 288.1 {1}
#> Unstratified Analysis
#> p-value (log-rank) 0.0973 0.9111
#> Hazard Ratio 2.18 1.06
#> 95% CI (0.85, 5.60) (0.38, 2.94)
#> 91 DAYS
#> Patients remaining at risk 12 12 13
#> Event Free Rate (%) 80.00 80.00 86.67
#> 95% CI (59.76, 100.00) (59.76, 100.00) (69.46, 100.00)
#> Difference in Event Free Rate 0.00 6.67
#> 95% CI (-28.63, 28.63) (-19.90, 33.23)
#> p-value (Z-test) 1.0000 0.6228
#> 183 DAYS
#> Patients remaining at risk 11 8 11
#> Event Free Rate (%) 73.33 53.33 73.33
#> 95% CI (50.95, 95.71) (28.09, 78.58) (50.95, 95.71)
#> Difference in Event Free Rate -20.00 0.00
#> 95% CI (-53.74, 13.74) (-31.65, 31.65)
#> p-value (Z-test) 0.2453 1.0000
#> —————————————————————————————————————————————————————————————————————————————————————————
#>
#> {1} - Censored observation: range maximum
#> —————————————————————————————————————————————————————————————————————————————————————————
The default p-value method for testing hazard ratio is “log-rank”.
Alternative methods can be requested by specifying the argument
pval_method
and options include, log-rank
(default), wald
or likelihood
. The syntax
currently does not allow requesting more than one p-value.
Note that ttet01
has been modified in the previous
example (i.e., preprocess(ttet01)
has been overridden); to
access the default template, try chevron::ttet01
.
proc_data <- log_filter(syn_data, PARAMCD == "PFS", "adtte")
run(ttet01, proc_data, pval_method = "wald")
#> A: Drug X B: Placebo C: Combination
#> (N=15) (N=15) (N=15)
#> ————————————————————————————————————————————————————————————————————————————————————————
#> Patients with event (%) 7 (46.7%) 12 (80%) 8 (53.3%)
#> Earliest contributing event
#> Death 5 11 7
#> Disease Progression 2 1 1
#> Patients without event (%) 8 (53.3%) 3 (20%) 7 (46.7%)
#> Time to Event (DAYS)
#> Median 261.9 187.7 256.3
#> 95% CI (221.9, NE) (144.7, 232.2) (212.0, NE)
#> 25% and 75%-ile 114.9, NE 141.9, 254.4 175.0, NE
#> Range 37.2 to 288.3 {1} 28.0 to 276.6 26.4 to 288.1 {1}
#> Unstratified Analysis
#> p-value (wald) 0.1053 0.9111
#> Hazard Ratio 2.18 1.06
#> 95% CI (0.85, 5.60) (0.38, 2.94)
#> 6 DAYS
#> Patients remaining at risk 15 15 15
#> Event Free Rate (%) 100.00 100.00 100.00
#> 95% CI (100.00, 100.00) (100.00, 100.00) (100.00, 100.00)
#> 12 DAYS
#> Patients remaining at risk 15 15 15
#> Event Free Rate (%) 100.00 100.00 100.00
#> 95% CI (100.00, 100.00) (100.00, 100.00) (100.00, 100.00)
#> ————————————————————————————————————————————————————————————————————————————————————————
#>
#> {1} - Censored observation: range maximum
#> ————————————————————————————————————————————————————————————————————————————————————————
VST01
)t_vs_chg <- run(vst01, syn_data)
head(t_vs_chg, 20)
#> A: Drug X B: Placebo C: Combination
#> Change from Change from Change from
#> Value at Visit Baseline Value at Visit Baseline Value at Visit Baseline
#> (N=15) (N=15) (N=15) (N=15) (N=15) (N=15)
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Diastolic Blood Pressure
#> SCREENING
#> n 15 0 15 0 15 0
#> Mean (SD) 94.385 (17.067) NE (NE) 106.381 (20.586) NE (NE) 106.468 (12.628) NE (NE)
#> Median 94.933 NE 111.133 NE 108.359 NE
#> Min - Max 55.71 - 122.00 NE - NE 60.21 - 131.91 NE - NE 83.29 - 127.17 NE - NE
#> BASELINE
#> n 15 15 15
#> Mean (SD) 96.133 (22.458) 108.111 (15.074) 103.149 (19.752)
#> Median 93.328 108.951 102.849
#> Min - Max 60.58 - 136.59 83.44 - 131.62 66.05 - 136.55
#> WEEK 1 DAY 8
#> n 15 15 15 15 15 15
#> Mean (SD) 98.977 (21.359) 2.844 (28.106) 104.110 (16.172) -4.001 (21.867) 100.826 (19.027) -2.323 (25.018)
#> Median 92.447 -4.066 107.703 3.227 103.058 -2.476
#> Min - Max 67.55 - 130.37 -32.82 - 47.68 70.91 - 132.89 -52.94 - 28.63 70.04 - 128.68 -55.15 - 41.81
#> WEEK 2 DAY 15
#> n 15 15 15 15 15 15
#> Mean (SD) 99.758 (14.477) 3.626 (21.189) 97.473 (17.296) -10.638 (20.831) 94.272 (16.961) -8.877 (27.229)
#> Median 101.498 1.731 99.501 -9.727 96.789 -10.155
VST02_1
)run(vst02_1, syn_data)
#> Assessment A: Drug X B: Placebo C: Combination
#> Abnormality (N=15) (N=15) (N=15)
#> —————————————————————————————————————————————————————————————————————————
#> Diastolic Blood Pressure
#> Low 8/15 (53.3%) 9/15 (60%) 8/15 (53.3%)
#> High 10/15 (66.7%) 5/15 (33.3%) 8/15 (53.3%)
#> Pulse Rate
#> Low 9/15 (60%) 3/15 (20%) 5/15 (33.3%)
#> High 2/15 (13.3%) 6/15 (40%) 5/15 (33.3%)
#> Respiratory Rate
#> Low 13/15 (86.7%) 10/15 (66.7%) 13/15 (86.7%)
#> High 7/15 (46.7%) 10/15 (66.7%) 11/15 (73.3%)
#> Systolic Blood Pressure
#> Low 7/15 (46.7%) 9/15 (60%) 11/15 (73.3%)
#> High 10/15 (66.7%) 9/15 (60%) 9/15 (60%)
#> Temperature
#> Low 12/15 (80%) 13/15 (86.7%) 11/15 (73.3%)
#> High 14/15 (93.3%) 12/15 (80%) 14/15 (93.3%)
#> Weight
#> Low 3/15 (20%) 3/15 (20%) 4/15 (26.7%)
#> High 4/15 (26.7%) 4/15 (26.7%) 5/15 (33.3%)
VST02_2
)run(vst02_2, syn_data)
#> Assessment A: Drug X B: Placebo C: Combination
#> Abnormality (N=15) (N=15) (N=15)
#> ———————————————————————————————————————————————————————————————————————
#> Diastolic Blood Pressure
#> Low 6/11 (54.5%) 9/15 (60%) 6/12 (50%)
#> High 8/12 (66.7%) 4/11 (36.4%) 7/13 (53.8%)
#> Pulse Rate
#> Low 9/15 (60%) 3/15 (20%) 5/13 (38.5%)
#> High 2/14 (14.3%) 4/12 (33.3%) 5/15 (33.3%)
#> Respiratory Rate
#> Low 7/9 (77.8%) 7/11 (63.6%) 11/12 (91.7%)
#> High 6/14 (42.9%) 7/11 (63.6%) 9/13 (69.2%)
#> Systolic Blood Pressure
#> Low 5/13 (38.5%) 8/12 (66.7%) 10/14 (71.4%)
#> High 8/13 (61.5%) 8/13 (61.5%) 8/13 (61.5%)
#> Temperature
#> Low 8/10 (80%) 7/9 (77.8%) 8/10 (80%)
#> High 8/8 (100%) 7/8 (87.5%) 12/13 (92.3%)
#> Weight
#> Low 3/15 (20%) 3/15 (20%) 3/14 (21.4%)
#> High 4/14 (28.6%) 4/15 (26.7%) 5/14 (35.7%)
AEL01_NOLLT
)ael01_nollt
template produces the
standard glossary of adverse event preferred terms and
investigator-specified terms.head
function to print only the
first 10 lines of the output.l_ae_nollt <- run(ael01_nollt, syn_data)
head(l_ae_nollt, 10)
#> MedDRA System Organ Class MedDRA Preferred Term Reported Term for the Adverse Event
#> ———————————————————————————————————————————————————————————————————————————————————————
#> cl A.1 dcd A.1.1.1.1 trm A.1.1.1.1
#> dcd A.1.1.1.2 trm A.1.1.1.2
#> cl B.1 dcd B.1.1.1.1 trm B.1.1.1.1
#> cl B.2 dcd B.2.1.2.1 trm B.2.1.2.1
#> dcd B.2.2.3.1 trm B.2.2.3.1
#> cl C.1 dcd C.1.1.1.3 trm C.1.1.1.3
#> cl C.2 dcd C.2.1.2.1 trm C.2.1.2.1
#> cl D.1 dcd D.1.1.1.1 trm D.1.1.1.1
#> dcd D.1.1.4.2 trm D.1.1.4.2
#> cl D.2 dcd D.2.1.5.3 trm D.2.1.5.3
FSTG01
)fstg01
template produces the
standard forest plot for odds ratio.PARAMCD == "BESRSPI"
) in pre-processing.ARM %in% c("A: Drug X", "B: Placebo")
)."SEX"
, "AGEGR1"
and "RACE"
.The confidence level of the confidence interval can be adjusted by
the conf_level
argument.
The interaction p-values and a different set of statistics can be
displayed using the stat_var
argument. Note that the users
are expected to select a method for p-value computation. see
[tern::prop_diff_test]
.
The subgroups
arguments controls which variables are
used for subgroup analysis. If NULL
the subgroup analysis is
removed.
The strata_var
argument is used to pass the columns used
for stratified analysis.
run(fstg01, proc_data, strata_var = "STRATA1")
#> Warning in coxexact.fit(X, Y, istrat, offset, init, control, weights = weights,
#> : Ran out of iterations and did not converge
#> Warning in s_odds_ratio(df = l_df[[2]], .var = "rsp", .ref_group = l_df[[1]], :
#> Unable to compute the odds ratio estimate. Please try re-running the function
#> with parameter `method` set to "approximate".
#> Warning in coxexact.fit(X, Y, istrat, offset, init, control, weights = weights,
#> : Ran out of iterations and did not converge
The col_symbol_size
argument controls the size of the
odds ratio symbols which are by default proportional in size to the
sample size of the subgroup. If NULL
the same symbol size
is used for all subgroups.
FSTG02
)fstg02
template produces the
standard forest plot for hazard ratio.PARAMCD == "OS"
) in pre-processing.ARM %in% c("A: Drug X", "B: Placebo")
)."SEX"
, "AGEGR1"
and "RACE"
.The interaction p-values and a different set of statistics can be
displayed using the control
argument. More details about
the control options are available in
[tern::extract_survival_subgroups]
The subgroups
arguments controls which variables are
used for subgroup analysis. If NULL
the subgroup analysis is
removed.
The strata_var
argument is used to pass the columns used
for stratified analysis.
The col_symbol_size
argument controls the size of the
hazard ratio symbols which are by default proportional in size to the
number of events in the subgroup. If NULL
the same symbol
size is used for all subgroups.