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This article describes creating an ADCE
ADaM for the
analysis of Vaccine Reactogenicity Data collected in SDTM
CE
domain. The current presented example is tested using
CE
SDTM domains and ADSL
ADaM domain. However,
other domains could be used if needed (eg temperature data collected in
VS
).
Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.
TOXGR
or SEV
)ADSL
Assumption: The CE
domain has already been merged with
the SUPPCE
dataset. If this is not yet the case, join
SUPPCE
onto parent CE
domain using
metatools::combine_supp(CE, SUPPCE)
.
library(admiraldev)
library(admiral)
library(dplyr)
library(lubridate)
library(admiralvaccine)
library(pharmaversesdtm)
data("ce_vaccine")
data("admiralvaccine_adsl")
adsl <- admiralvaccine_adsl
ce <- ce_vaccine
ce <- convert_blanks_to_na(ce)
adsl <- convert_blanks_to_na(adsl)
USUBJID | TRTSDT | TRTEDT | TRT01A | AP01SDT | AP01EDT | AP02SDT | AP02EDT |
---|---|---|---|---|---|---|---|
ABC-1001 | 2021-11-03 | 2021-12-30 | VACCINE A | 2021-11-03 | 2021-12-29 | 2021-12-30 | 2022-04-27 |
ABC-1002 | 2021-10-07 | 2021-12-16 | VACCINE A | 2021-10-07 | 2021-12-15 | 2021-12-16 | 2022-06-14 |
This step involves company-specific pre-processing of required input dataset for further analysis. In this step, we will filter records that has only reactogenicity events.
Create period dataset - for joining period information onto
CE
records. Need to remove datetime variables as otherwise
causes duplicate issues.
adsl2 <- adsl %>%
select(-c(starts_with("AP") & ends_with("DTM")))
adperiods <- create_period_dataset(
adsl2,
new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT)
)
USUBJID | APERIOD | APERSDT | APEREDT |
---|---|---|---|
ABC-1001 | 1 | 2021-11-03 | 2021-12-29 |
ABC-1001 | 2 | 2021-12-30 | 2022-04-27 |
ABC-1002 | 1 | 2021-10-07 | 2021-12-15 |
ABC-1002 | 2 | 2021-12-16 | 2022-06-14 |
At this step, it may be useful to join ADSL
to your
CE
domain. Only the ADSL
variables used for
derivations are selected at this step. The rest of the relevant
ADSL
variables would be added later.
adsl_vars <- exprs(TRTSDT, TRTEDT)
adce <- adce %>%
# join ADSL to CE
derive_vars_merged(
dataset_add = adsl,
new_vars = adsl_vars,
by = exprs(STUDYID, USUBJID)
) %>%
derive_vars_dt(
dtc = CESTDTC,
new_vars_prefix = "AST",
highest_imputation = "n"
) %>%
derive_vars_dt(
dtc = CEENDTC,
new_vars_prefix = "AEN",
highest_imputation = "n"
) %>%
derive_vars_dy(
reference_date = TRTSDT,
source_vars = exprs(ASTDT, AENDT)
)
USUBJID | TRTSDT | CESTDTC | CEENDTC | ASTDT | AENDT | ASTDY | AENDY |
---|---|---|---|---|---|---|---|
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | NA | NA |
ABC-1001 | 2021-11-03 | 2021-11-04 | 2021-11-07 | 2021-11-04 | 2021-11-07 | 2 | 5 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 2021-11-04 | 2021-11-04 | 2021-11-04 | 2 | 2 |
ABC-1001 | 2021-11-03 | 2021-11-03 | 2021-11-09 | 2021-11-03 | 2021-11-09 | 1 | 7 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | NA | NA |
ABC-1001 | 2021-11-03 | 2021-11-03 | 2021-11-04 | 2021-11-03 | 2021-11-04 | 1 | 2 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | NA | NA |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | NA | NA |
ABC-1001 | 2021-11-03 | 2021-11-04 | 2021-11-04 | 2021-11-04 | 2021-11-04 | 2 | 2 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 2021-11-04 | 2021-11-04 | 2021-11-04 | 2 | 2 |
Also add analysis version of
CEREL
(AREL
).
adce <-
derive_vars_joined(
adce,
dataset_add = adperiods,
by_vars = exprs(STUDYID, USUBJID),
filter_join = ASTDT >= APERSDT & ASTDT <= APEREDT,
join_type = "all"
) %>%
mutate(
APERSTDY = as.integer(ASTDT - APERSDT) + 1,
AREL = CEREL
)
USUBJID | TRTSDT | ASTDT | AENDT | ASTDY | AENDY | APERIOD | APERSDT | APERSTDY |
---|---|---|---|---|---|---|---|---|
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | NA | NA | NA |
ABC-1001 | 2021-11-03 | 2021-11-04 | 2021-11-07 | 2 | 5 | 1 | 2021-11-03 | 2 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 2021-11-04 | 2 | 2 | 1 | 2021-11-03 | 2 |
ABC-1001 | 2021-11-03 | 2021-11-03 | 2021-11-09 | 1 | 7 | 1 | 2021-11-03 | 1 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | NA | NA | NA |
ABC-1001 | 2021-11-03 | 2021-11-03 | 2021-11-04 | 1 | 2 | 1 | 2021-11-03 | 1 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | NA | NA | NA |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | NA | NA | NA |
ABC-1001 | 2021-11-03 | 2021-11-04 | 2021-11-04 | 2 | 2 | 1 | 2021-11-03 | 2 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 2021-11-04 | 2 | 2 | 1 | 2021-11-03 | 2 |
TOXGR
or SEV
)Depending on which variable is collected for the Grading
(TOXGR
or SEV
) in CE
domain,
derive the associated analysis version. In current example,
SEV
is collected, so the code is using this as an example.
In addition, derivation of Extreme Flags: in current example: flag the
first occurrence of the most severe grade within a Period
(AOCC01FL
).
adce <- adce %>%
mutate(
ASEV = CESEV,
ASEVN = as.integer(factor(ASEV,
levels = c("MILD", "MODERATE", "SEVERE", "DEATH THREATENING")
))
) %>%
restrict_derivation(
derivation = derive_var_extreme_flag,
args = params(
by_vars = exprs(USUBJID, APERIOD),
order = exprs(desc(ASEVN), ASTDY, CEDECOD),
new_var = AOCC01FL,
mode = "first"
),
filter = !is.na(APERIOD) & !is.na(ASEV)
)
USUBJID | TRTSDT | ASTDT | APERIOD | APERSDT | APERSTDY | CEDECOD | ASEVN | AOCC01FL | CESEQ |
---|---|---|---|---|---|---|---|---|---|
ABC-1001 | 2021-11-03 | 2021-11-03 | 1 | 2021-11-03 | 1 | Swelling | 2 | Y | 4 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 1 | 2021-11-03 | 2 | Erythema | 2 | NA | 3 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 1 | 2021-11-03 | 2 | Injection site pain | 2 | NA | 2 |
ABC-1001 | 2021-11-03 | 2021-11-03 | 1 | 2021-11-03 | 1 | Fatigue | 1 | NA | 6 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 1 | 2021-11-03 | 2 | Arthralgia | 1 | NA | 9 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 1 | 2021-11-03 | 2 | Myalgia | 1 | NA | 10 |
ABC-1002 | 2021-10-07 | 2021-10-11 | 1 | 2021-10-07 | 5 | Headache | 2 | Y | 8 |
ABC-1002 | 2021-10-07 | 2021-10-09 | 1 | 2021-10-07 | 3 | Erythema | 1 | NA | 3 |
ABC-1002 | 2021-10-07 | 2021-12-16 | 2 | 2021-12-16 | 1 | Injection site pain | 1 | Y | 13 |
ABC-1002 | 2021-10-07 | 2021-12-17 | 2 | 2021-12-16 | 2 | Erythema | 1 | NA | 14 |
adce <- adce %>%
derive_var_obs_number(
new_var = ASEQ,
by_vars = exprs(STUDYID, USUBJID),
order = exprs(CEDECOD, CELAT, CETPTREF, APERIOD),
check_type = "error"
) %>%
derive_vars_duration(
new_var = ADURN,
new_var_unit = ADURU,
start_date = ASTDT,
end_date = AENDT,
in_unit = "days",
out_unit = "days",
add_one = TRUE,
trunc_out = FALSE
)
USUBJID | TRTSDT | ASTDT | APERIOD | APERSDT | APERSTDY | CEDECOD | ASEVN | AOCC01FL | CESEQ | ASEQ |
---|---|---|---|---|---|---|---|---|---|---|
ABC-1001 | 2021-11-03 | 2021-11-04 | 1 | 2021-11-03 | 2 | Arthralgia | 1 | NA | 9 | 1 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Arthralgia | NA | NA | 20 | 2 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Chills | NA | NA | 1 | 3 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Chills | NA | NA | 12 | 4 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Diarrhoea | NA | NA | 5 | 5 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Diarrhoea | NA | NA | 16 | 6 |
ABC-1001 | 2021-11-03 | 2021-11-04 | 1 | 2021-11-03 | 2 | Erythema | 2 | NA | 3 | 7 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Erythema | NA | NA | 14 | 8 |
ABC-1001 | 2021-11-03 | 2021-11-03 | 1 | 2021-11-03 | 1 | Fatigue | 1 | NA | 6 | 9 |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Fatigue | NA | NA | 17 | 10 |
ADSL
Get list of ADSL
vars as per trial specific which needs
to be adjusted when using the template
adsl_list <- adsl %>%
select(STUDYID, USUBJID, TRT01A, TRT01P, AGE, AGEU, SEX, RACE, COUNTRY, ETHNIC, SITEID, SUBJID)
adce <- adce %>%
derive_vars_merged(
dataset_add = adsl_list,
by_vars = exprs(STUDYID, USUBJID)
)
USUBJID | TRTSDT | ASTDT | APERIOD | APERSDT | APERSTDY | CEDECOD | ASEVN | AOCC01FL | CESEQ | ASEQ | AGE | SEX |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ABC-1001 | 2021-11-03 | 2021-11-04 | 1 | 2021-11-03 | 2 | Arthralgia | 1 | NA | 9 | 1 | 74 | F |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Arthralgia | NA | NA | 20 | 2 | 74 | F |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Chills | NA | NA | 1 | 3 | 74 | F |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Chills | NA | NA | 12 | 4 | 74 | F |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Diarrhoea | NA | NA | 5 | 5 | 74 | F |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Diarrhoea | NA | NA | 16 | 6 | 74 | F |
ABC-1001 | 2021-11-03 | 2021-11-04 | 1 | 2021-11-03 | 2 | Erythema | 2 | NA | 3 | 7 | 74 | F |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Erythema | NA | NA | 14 | 8 | 74 | F |
ABC-1001 | 2021-11-03 | 2021-11-03 | 1 | 2021-11-03 | 1 | Fatigue | 1 | NA | 6 | 9 | 74 | F |
ABC-1001 | 2021-11-03 | NA | NA | NA | NA | Fatigue | NA | NA | 17 | 10 | 74 | F |
ADaM | Sample Code |
---|---|
ADCE | ad_adce.R |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.