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This article describes creating an Exposure ADaM using the BDS
structure. Examples are currently presented using an underlying
EX
domain where the EX
domain represents data
as collected on the CRF and the ADEX
ADaM is output.
However, the examples can be applied to situations where an
EC
domain is used as input and/or ADEC
or
another exposure ADaM is created.
There are many different approaches to modeling exposure data. This
vignette gives examples of creating PARAMCD
and
AVAL
combinations using exposure data. This vignette is not
meant to be a guide or standard for the structure of exposure analysis
datasets.
Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.
ADT
, ADTM
, ADY
,
ADTF
, ATMF
)PARAMCD
, PARAMN
,
etc. from Reference TablesAVALCATx
)ASEQ
ASEQ
To start, all data frames needed for the creation of
ADEX
should be read into the environment. This will be a
company specific process. Some of the data frames needed may be
EX
and ADSL
.
For example purpose, the CDISC Pilot SDTM and ADaM datasets—which are
included in {pharmaversesdtm}
—are used.
library(admiral)
library(dplyr, warn.conflicts = FALSE)
library(pharmaversesdtm)
library(lubridate)
library(stringr)
library(tibble)
data("admiral_adsl")
data("ex")
adsl <- admiral_adsl
ex <- convert_blanks_to_na(ex)
At this step, it may be useful to join ADSL
to your
EX
domain as well. 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, TRTSDTM, TRTEDT, TRTEDTM)
adex <- derive_vars_merged(
ex,
dataset_add = adsl,
new_vars = adsl_vars,
by_vars = exprs(STUDYID, USUBJID)
)
USUBJID | EXTRT | EXDOSE | EXDOSFRQ | VISIT | EXSTDTC | EXENDTC | TRTSDTM | TRTEDTM |
---|---|---|---|---|---|---|---|---|
01-701-1015 | PLACEBO | 0 | QD | BASELINE | 2014-01-02 | 2014-01-16 | 2014-01-02 | 2014-07-02 23:59:59 |
01-701-1015 | PLACEBO | 0 | QD | WEEK 2 | 2014-01-17 | 2014-06-18 | 2014-01-02 | 2014-07-02 23:59:59 |
01-701-1015 | PLACEBO | 0 | QD | WEEK 24 | 2014-06-19 | 2014-07-02 | 2014-01-02 | 2014-07-02 23:59:59 |
01-701-1023 | PLACEBO | 0 | QD | BASELINE | 2012-08-05 | 2012-08-27 | 2012-08-05 | 2012-09-01 23:59:59 |
01-701-1023 | PLACEBO | 0 | QD | WEEK 2 | 2012-08-28 | 2012-09-01 | 2012-08-05 | 2012-09-01 23:59:59 |
01-703-1086 | XANOMELINE | 54 | QD | BASELINE | 2012-09-02 | 2012-09-16 | 2012-09-02 | 2012-12-04 23:59:59 |
01-703-1086 | XANOMELINE | 54 | QD | WEEK 2 | 2012-09-17 | 2012-12-04 | 2012-09-02 | 2012-12-04 23:59:59 |
01-703-1096 | PLACEBO | 0 | QD | BASELINE | 2013-01-25 | 2013-02-09 | 2013-01-25 | 2013-03-16 23:59:59 |
01-703-1096 | PLACEBO | 0 | QD | WEEK 2 | 2013-02-10 | 2013-03-16 | 2013-01-25 | 2013-03-16 23:59:59 |
01-707-1037 | XANOMELINE | 54 | QD | BASELINE | 2013-12-20 | 2013-12-24 | 2013-12-20 | 2013-12-24 23:59:59 |
The CDISC pilot EX
domain data does not contain a dose
adjustment flag or the planned dose information. For demonstration
purposes, this will be added to the data.
adex <- adex %>%
mutate(
EXADJ = case_when(
USUBJID == "01-701-1028" & VISIT %in% c("WEEK 2") ~ "ADVERSE EVENT",
USUBJID == "01-701-1148" & VISIT %in% c("WEEK 2", "WEEK 24") ~ "MEDICATION ERROR",
TRUE ~ NA_character_
),
EXDOSE = case_when(
USUBJID == "01-701-1028" & VISIT %in% c("WEEK 2") ~ 0,
USUBJID == "01-701-1148" & VISIT %in% c("WEEK 2", "WEEK 24") ~ 0,
TRUE ~ EXDOSE
)
) %>%
mutate(EXPLDOS = if_else(EXTRT == "PLACEBO", 0, 54))
distinct(adex, EXTRT, EXPLDOS)
#> # A tibble: 2 × 2
#> EXTRT EXPLDOS
#> <chr> <dbl>
#> 1 PLACEBO 0
#> 2 XANOMELINE 54
count(adex, EXADJ)
#> # A tibble: 1 × 2
#> EXADJ n
#> <chr> <int>
#> 1 <NA> 13
ADT
,
ADTM
, ADY
, ADTF
,
ATMF
)The function derive_vars_dt()
can be used to derive
ADT
. This function allows the user to impute the date as
well.
Example calls:
adex <- derive_vars_dt(adex, new_vars_prefix = "AST", dtc = EXSTDTC)
adex <- derive_vars_dt(adex, new_vars_prefix = "AEN", dtc = EXENDTC)
USUBJID | VISIT | EXSTDTC | EXENDTC | ASTDT | AENDT |
---|---|---|---|---|---|
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | 2014-06-19 | 2014-07-02 | 2014-06-19 | 2014-07-02 |
01-701-1023 | BASELINE | 2012-08-05 | 2012-08-27 | 2012-08-05 | 2012-08-27 |
01-701-1023 | WEEK 2 | 2012-08-28 | 2012-09-01 | 2012-08-28 | 2012-09-01 |
01-703-1086 | BASELINE | 2012-09-02 | 2012-09-16 | 2012-09-02 | 2012-09-16 |
01-703-1086 | WEEK 2 | 2012-09-17 | 2012-12-04 | 2012-09-17 | 2012-12-04 |
01-703-1096 | BASELINE | 2013-01-25 | 2013-02-09 | 2013-01-25 | 2013-02-09 |
01-703-1096 | WEEK 2 | 2013-02-10 | 2013-03-16 | 2013-02-10 | 2013-03-16 |
01-707-1037 | BASELINE | 2013-12-20 | 2013-12-24 | 2013-12-20 | 2013-12-24 |
The next examples demonstrates the datetime imputation features
available in the derive_vars_dtm()
function, where the time
is imputed as “00:00:00”:
adex <- derive_vars_dtm(
adex,
dtc = EXSTDTC,
highest_imputation = "M",
new_vars_prefix = "AST"
)
adex <- derive_vars_dtm(
adex,
dtc = EXENDTC,
highest_imputation = "M",
date_imputation = "last",
new_vars_prefix = "AEN"
)
USUBJID | VISIT | EXSTDTC | EXENDTC | ASTDTM | AENDTM |
---|---|---|---|---|---|
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | 2014-06-19 | 2014-07-02 | 2014-06-19 | 2014-07-02 |
01-701-1023 | BASELINE | 2012-08-05 | 2012-08-27 | 2012-08-05 | 2012-08-27 |
01-701-1023 | WEEK 2 | 2012-08-28 | 2012-09-01 | 2012-08-28 | 2012-09-01 |
01-703-1086 | BASELINE | 2012-09-02 | 2012-09-16 | 2012-09-02 | 2012-09-16 |
01-703-1086 | WEEK 2 | 2012-09-17 | 2012-12-04 | 2012-09-17 | 2012-12-04 |
01-703-1096 | BASELINE | 2013-01-25 | 2013-02-09 | 2013-01-25 | 2013-02-09 |
01-703-1096 | WEEK 2 | 2013-02-10 | 2013-03-16 | 2013-02-10 | 2013-03-16 |
01-707-1037 | BASELINE | 2013-12-20 | 2013-12-24 | 2013-12-20 | 2013-12-24 |
The example above imputes the start date to the first first day of the month and imputes the end date to the last day of the month.
Please see the Date and Time Imputation for additional examples on calculating and imputing analysis dates.
Next, the analysis study days can be derived:
USUBJID | VISIT | ASTDT | ASTDY | AENDT | AENDY | TRTSDT |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 2014-01-02 | 1 | 2014-01-16 | 15 | 2014-01-02 |
01-701-1015 | WEEK 2 | 2014-01-17 | 16 | 2014-06-18 | 168 | 2014-01-02 |
01-701-1015 | WEEK 24 | 2014-06-19 | 169 | 2014-07-02 | 182 | 2014-01-02 |
01-701-1023 | BASELINE | 2012-08-05 | 1 | 2012-08-27 | 23 | 2012-08-05 |
01-701-1023 | WEEK 2 | 2012-08-28 | 24 | 2012-09-01 | 28 | 2012-08-05 |
01-703-1086 | BASELINE | 2012-09-02 | 1 | 2012-09-16 | 15 | 2012-09-02 |
01-703-1086 | WEEK 2 | 2012-09-17 | 16 | 2012-12-04 | 94 | 2012-09-02 |
01-703-1096 | BASELINE | 2013-01-25 | 1 | 2013-02-09 | 16 | 2013-01-25 |
01-703-1096 | WEEK 2 | 2013-02-10 | 17 | 2013-03-16 | 51 | 2013-01-25 |
01-707-1037 | BASELINE | 2013-12-20 | 1 | 2013-12-24 | 5 | 2013-12-20 |
To compute the duration of treatment or exposure for a record, the
derive_vars_duration()
function can be used.
USUBJID | VISIT | ASTDT | ASTDY | AENDT | AENDY | EXDURD |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 2014-01-02 | 1 | 2014-01-16 | 15 | 15 |
01-701-1015 | WEEK 2 | 2014-01-17 | 16 | 2014-06-18 | 168 | 153 |
01-701-1015 | WEEK 24 | 2014-06-19 | 169 | 2014-07-02 | 182 | 14 |
01-701-1023 | BASELINE | 2012-08-05 | 1 | 2012-08-27 | 23 | 23 |
01-701-1023 | WEEK 2 | 2012-08-28 | 24 | 2012-09-01 | 28 | 5 |
01-703-1086 | BASELINE | 2012-09-02 | 1 | 2012-09-16 | 15 | 15 |
01-703-1086 | WEEK 2 | 2012-09-17 | 16 | 2012-12-04 | 94 | 79 |
01-703-1096 | BASELINE | 2013-01-25 | 1 | 2013-02-09 | 16 | 16 |
01-703-1096 | WEEK 2 | 2013-02-10 | 17 | 2013-03-16 | 51 | 35 |
01-707-1037 | BASELINE | 2013-12-20 | 1 | 2013-12-24 | 5 | 5 |
The units of the calculated duration can also be changed. In this example, the duration is output as years:
adex <- adex %>%
derive_vars_duration(
new_var = EXDURDY,
out_unit = "years",
start_date = ASTDT,
end_date = AENDT
)
USUBJID | VISIT | ASTDT | AENDT | EXDURD | EXDURDY |
---|---|---|---|---|---|
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | 15 | 0.0410678 |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | 153 | 0.4188912 |
01-701-1015 | WEEK 24 | 2014-06-19 | 2014-07-02 | 14 | 0.0383299 |
01-701-1023 | BASELINE | 2012-08-05 | 2012-08-27 | 23 | 0.0629706 |
01-701-1023 | WEEK 2 | 2012-08-28 | 2012-09-01 | 5 | 0.0136893 |
01-703-1086 | BASELINE | 2012-09-02 | 2012-09-16 | 15 | 0.0410678 |
01-703-1086 | WEEK 2 | 2012-09-17 | 2012-12-04 | 79 | 0.2162902 |
01-703-1096 | BASELINE | 2013-01-25 | 2013-02-09 | 16 | 0.0438056 |
01-703-1096 | WEEK 2 | 2013-02-10 | 2013-03-16 | 35 | 0.0958248 |
01-707-1037 | BASELINE | 2013-12-20 | 2013-12-24 | 5 | 0.0136893 |
Please refer to the derive_vars_duration()
documentation
for detailed information on the input parameters.
It may be necessary to calculate additional intermediate values. For example, the cumulative doses received and cumulative planned doses may be calculated as:
USUBJID | EXDOSE | EXPLDOS | EXDURD | DOSEO | PDOSEO |
---|---|---|---|---|---|
01-701-1015 | 0 | 0 | 15 | 0 | 0 |
01-701-1015 | 0 | 0 | 153 | 0 | 0 |
01-701-1015 | 0 | 0 | 14 | 0 | 0 |
01-701-1023 | 0 | 0 | 23 | 0 | 0 |
01-701-1023 | 0 | 0 | 5 | 0 | 0 |
01-703-1086 | 54 | 54 | 15 | 810 | 810 |
01-703-1086 | 54 | 54 | 79 | 4266 | 4266 |
01-703-1096 | 0 | 0 | 16 | 0 | 0 |
01-703-1096 | 0 | 0 | 35 | 0 | 0 |
01-707-1037 | 54 | 54 | 5 | 270 | 270 |
It may be of additional interest to turn a single record containing
dosing summary information into a set of multiple single records, each
representing a single dose over the interval specified by the summary
record. This is another approach to deriving a total dose parameter when
EXDOSFRQ != ONCE
.
The function create_single_dose_dataset()
can be used to
expand a record containing a start date, an end date, and a dosing
frequency to a corresponding set of records each representing one dose
(i.e. EXDOSFRQ == "ONCE"
).
single_dose <- adex %>%
filter(USUBJID == "01-701-1015" & EXSTDY == 1) %>%
create_single_dose_dataset(keep_source_vars = exprs(USUBJID, EXDOSE, EXPLDOS, EXDOSFRQ, ASTDT, AENDT))
USUBJID | EXDOSE | EXPLDOS | EXDOSFRQ | ASTDT | AENDT |
---|---|---|---|---|---|
01-701-1015 | 0 | 0 | ONCE | 2014-01-02 | 2014-01-02 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-03 | 2014-01-03 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-04 | 2014-01-04 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-05 | 2014-01-05 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-06 | 2014-01-06 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-07 | 2014-01-07 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-08 | 2014-01-08 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-09 | 2014-01-09 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-10 | 2014-01-10 |
01-701-1015 | 0 | 0 | ONCE | 2014-01-11 | 2014-01-11 |
The first set of exposure records to create will be records mapped
1:1 to an existing collected exposure record in SDTM. For these records,
the AVAL
or AVALC
would be calculated using
columns that exist on the data and no summarizing of records would be
necessary.
These records may be used for input into summary records or be used individually for summarization in outputs. Some examples may be exposure duration, dose administered, dose adjusted, etc. based on one exposure record in SDTM.
These records can be derived using simple dplyr::mutate
assignments and then combined:
adex_durd <- adex %>%
mutate(
PARAMCD = "DURD",
AVAL = EXDURD
)
adex_dose <- adex %>%
mutate(
PARAMCD = "DOSE",
AVAL = DOSEO
)
adex_pldos <- adex %>%
mutate(
PARAMCD = "PLDOSE",
AVAL = PDOSEO
)
adex_adj <- adex %>%
mutate(
PARAMCD = "ADJ",
AVALC = if_else(!is.na(EXADJ), "Y", NA_character_)
)
adex_adjae <- adex %>%
mutate(
PARAMCD = "ADJAE",
AVALC = if_else(EXADJ == "ADVERSE EVENT", "Y", NA_character_)
)
adex <- bind_rows(
adex_durd,
adex_dose,
adex_pldos,
adex_adj,
adex_adjae
) %>%
mutate(PARCAT1 = "INDIVIDUAL")
count(adex, PARAMCD)
#> # A tibble: 5 × 2
#> PARAMCD n
#> <chr> <int>
#> 1 ADJ 13
#> 2 ADJAE 13
#> 3 DOSE 13
#> 4 DURD 13
#> 5 PLDOSE 13
USUBJID | VISIT | ASTDT | AENDT | PARAMCD | AVAL | AVALC |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | PLDOSE | 0 | NA |
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | DURD | 15 | NA |
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | DOSE | 0 | NA |
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | ADJAE | NA | NA |
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | ADJ | NA | NA |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | PLDOSE | 0 | NA |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | DURD | 153 | NA |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | DOSE | 0 | NA |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | ADJAE | NA | NA |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | ADJ | NA | NA |
Exposure is commonly analyzed by a timing interval
(e.g. APHASE
, APERIOD
, AVISIT
,
etc.). For these types of calculations, the
derive_param_exposure()
function may be used. In addition
to creating a summarized AVAL
, the function will also
compute minimum and maximum dates for the record.
For example, to calculate the total dose by subject and treatment,
adex <- derive_param_exposure(
adex,
dataset_add = adex,
by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars),
input_code = "DOSE",
set_values_to = exprs(
PARAMCD = "TDOSE",
PARCAT1 = "OVERALL",
AVAL = sum(AVAL, na.rm = TRUE)
)
)
USUBJID | VISIT | PARCAT1 | PARAMCD | AVAL | ASTDT | AENDT |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | INDIVIDUAL | ADJ | NA | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | INDIVIDUAL | ADJ | NA | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | INDIVIDUAL | ADJ | NA | 2014-06-19 | 2014-07-02 |
01-701-1015 | BASELINE | INDIVIDUAL | ADJAE | NA | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | INDIVIDUAL | ADJAE | NA | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | INDIVIDUAL | ADJAE | NA | 2014-06-19 | 2014-07-02 |
01-701-1015 | BASELINE | INDIVIDUAL | DOSE | 0 | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | INDIVIDUAL | DOSE | 0 | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | INDIVIDUAL | DOSE | 0 | 2014-06-19 | 2014-07-02 |
01-701-1015 | BASELINE | INDIVIDUAL | DURD | 15 | 2014-01-02 | 2014-01-16 |
A record with PARAMCD == "TDOSE"
is created with
PARCAT1
set to "OVERALL"
using the records in
ADEX
where PARAMCD == "DOSE"
by summing
AVAL
. In addition, the ASTDT
, and
AENDT
are created as the minimum and maximum date/times
associated with each by_vars
grouping. Note that, in
addition to PARAMCD
, PARCAT1
,
AVAL
, ASTDT
and AENDT
, only those
variables specified in the by_vars
argument will be
populated in the new records.
Multiple parameters (records) may be created at one time using the
call_derivation()
function:
adex <- adex %>%
call_derivation(
derivation = derive_param_exposure,
variable_params = list(
params(
set_values_to = exprs(
PARAMCD = "TDOSE",
PARCAT1 = "OVERALL",
AVAL = sum(AVAL, na.rm = TRUE)
),
input_code = "DOSE"
),
params(
set_values_to = exprs(
PARAMCD = "TPDOSE",
PARCAT1 = "OVERALL",
AVAL = sum(AVAL, na.rm = TRUE)
),
input_code = "PLDOSE"
),
params(
set_values_to = exprs(
PARAMCD = "TDURD",
PARCAT1 = "OVERALL",
AVAL = sum(AVAL, na.rm = TRUE)
),
input_code = "DURD"
),
params(
set_values_to = exprs(
PARAMCD = "TADJ",
PARCAT1 = "OVERALL",
AVALC = if_else(sum(!is.na(AVALC)) > 0, "Y", NA_character_)
),
input_code = "ADJ"
),
params(
set_values_to = exprs(
PARAMCD = "TADJAE",
PARCAT1 = "OVERALL",
AVALC = if_else(sum(!is.na(AVALC)) > 0, "Y", NA_character_)
),
input_code = "ADJAE"
)
),
dataset_add = adex,
by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars)
)
count(adex, PARAMCD, PARCAT1)
#> # A tibble: 10 × 3
#> PARAMCD PARCAT1 n
#> <chr> <chr> <int>
#> 1 ADJ INDIVIDUAL 13
#> 2 ADJAE INDIVIDUAL 13
#> 3 DOSE INDIVIDUAL 13
#> 4 DURD INDIVIDUAL 13
#> 5 PLDOSE INDIVIDUAL 13
#> 6 TADJ OVERALL 6
#> 7 TADJAE OVERALL 6
#> 8 TDOSE OVERALL 6
#> 9 TDURD OVERALL 6
#> 10 TPDOSE OVERALL 6
USUBJID | VISIT | PARCAT1 | PARAMCD | AVAL | AVALC | ASTDT | AENDT |
---|---|---|---|---|---|---|---|
01-701-1015 | BASELINE | INDIVIDUAL | ADJ | NA | NA | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | INDIVIDUAL | ADJ | NA | NA | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | INDIVIDUAL | ADJ | NA | NA | 2014-06-19 | 2014-07-02 |
01-701-1015 | BASELINE | INDIVIDUAL | ADJAE | NA | NA | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | INDIVIDUAL | ADJAE | NA | NA | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | INDIVIDUAL | ADJAE | NA | NA | 2014-06-19 | 2014-07-02 |
01-701-1015 | BASELINE | INDIVIDUAL | DOSE | 0 | NA | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | INDIVIDUAL | DOSE | 0 | NA | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | INDIVIDUAL | DOSE | 0 | NA | 2014-06-19 | 2014-07-02 |
01-701-1015 | BASELINE | INDIVIDUAL | DURD | 15 | NA | 2014-01-02 | 2014-01-16 |
Dose intensity can be calculated using the function
derive_param_doseint()
. The planned dose and administered
dose are passed into the function and a new record is created with the
dose intensity calculation. Again, only those variables specified in the
by_vars
argument will be populated in this new record.
adex <- adex %>%
derive_param_doseint(
by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars),
set_values_to = exprs(PARAMCD = "TNDOSINT"),
tadm_code = "TDOSE",
tpadm_code = "TPDOSE"
)
USUBJID | VISIT | EXSTDTC | EXENDTC | PARCAT1 | PARAMCD | AVAL | ASTDT | AENDT |
---|---|---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-16 | INDIVIDUAL | DURD | 15 | 2014-01-02 | 2014-01-16 |
01-701-1015 | WEEK 2 | 2014-01-17 | 2014-06-18 | INDIVIDUAL | DURD | 153 | 2014-01-17 | 2014-06-18 |
01-701-1015 | WEEK 24 | 2014-06-19 | 2014-07-02 | INDIVIDUAL | DURD | 14 | 2014-06-19 | 2014-07-02 |
01-701-1023 | BASELINE | 2012-08-05 | 2012-08-27 | INDIVIDUAL | DURD | 23 | 2012-08-05 | 2012-08-27 |
01-701-1023 | WEEK 2 | 2012-08-28 | 2012-09-01 | INDIVIDUAL | DURD | 5 | 2012-08-28 | 2012-09-01 |
01-703-1086 | BASELINE | 2012-09-02 | 2012-09-16 | INDIVIDUAL | DURD | 15 | 2012-09-02 | 2012-09-16 |
01-703-1086 | WEEK 2 | 2012-09-17 | 2012-12-04 | INDIVIDUAL | DURD | 79 | 2012-09-17 | 2012-12-04 |
01-703-1096 | BASELINE | 2013-01-25 | 2013-02-09 | INDIVIDUAL | DURD | 16 | 2013-01-25 | 2013-02-09 |
01-703-1096 | WEEK 2 | 2013-02-10 | 2013-03-16 | INDIVIDUAL | DURD | 35 | 2013-02-10 | 2013-03-16 |
01-707-1037 | BASELINE | 2013-12-20 | 2013-12-24 | INDIVIDUAL | DURD | 5 | 2013-12-20 | 2013-12-24 |
The default calculation for dose intensity is: Administered Doses / Planned Doses * 100.
Please see the derive_param_doseint()
documentation to
see how planned doses of 0 or NA
are handled.
PARAMCD
, PARAMN
, etc. from
Reference tablesTo assign parameter level values such as PARAM
,
PARAMN
, PARCAT1
, etc., a lookup can be created
to join to the source data.
For example, when creating ADEX
, a lookup based on the
ADaM PARAMCD
value may be created:
PARAMCD | PARAM | PARAMN |
---|---|---|
DURD | Study drug duration during constant dosing interval (days) | 1 |
DOSE | Dose administered during constant dosing interval (mg) | 2 |
PLDOSE | Planned dose during constant dosing interval (mg) | 3 |
ADJ | Dose adjusted during constant dosing interval | 4 |
ADJAE | Dose adjusted due to AE during constant dosing interval | 5 |
TDURD | Overall duration (days) | 6 |
TDOSE | Total dose administered (mg) | 7 |
TPDOSE | Total planned dose (mg) | 9 |
TADJ | Dose adjusted during study | 10 |
TADJAE | Dose adjusted during study due to AE | 11 |
TNDOSINT | Overall dose intensity (%) | 12 |
adex <- derive_vars_merged(
adex,
dataset_add = param_lookup,
by_vars = exprs(PARAMCD)
)
count(adex, PARAMCD, PARAM, PARAMN)
#> # A tibble: 11 × 4
#> PARAMCD PARAM PARAMN n
#> <chr> <chr> <dbl> <int>
#> 1 ADJ Dose adjusted during constant dosing interval 4 13
#> 2 ADJAE Dose adjusted due to AE during constant dosing interv… 5 13
#> 3 DOSE Dose administered during constant dosing interval (mg) 2 13
#> 4 DURD Study drug duration during constant dosing interval (d… 1 13
#> 5 PLDOSE Planned dose during constant dosing interval (mg) 3 13
#> 6 TADJ Dose adjusted during study 10 6
#> 7 TADJAE Dose adjusted during study due to AE 11 6
#> 8 TDOSE Total dose administered (mg) 7 6
#> 9 TDURD Overall duration (days) 6 6
#> 10 TNDOSINT Overall dose intensity (%) 12 6
#> 11 TPDOSE Total planned dose (mg) 9 6
Please note, this is an example only and additional columns may be needed for the join depending on your lookup/metadata table.
AVALCATx
){admiral}
does not currently have a generic function to
aid in assigning AVALCATx
/ AVALCAxN
values.
Below is a simple example of how these values may be assigned using the
dplyr::mutate
function:
adex <- adex %>%
mutate(
AVALCAT1 = case_when(
PARAMCD %in% c("TDURD") & AVAL < 30 ~ "< 30 days",
PARAMCD %in% c("TDURD") & AVAL >= 30 & AVAL < 90 ~ ">= 30 and < 90 days",
PARAMCD %in% c("TDURD") & AVAL >= 90 ~ ">=90 days",
PARAMCD %in% c("TDOSE", "TPDOSE") & AVAL < 1000 ~ "< 1000 mg",
PARAMCD %in% c("TDOSE", "TPDOSE") & AVAL >= 1000 ~ ">= 1000 mg",
TRUE ~ NA_character_
)
)
USUBJID | VISIT | PARCAT1 | PARAMCD | AVAL | AVALCAT1 |
---|---|---|---|---|---|
01-701-1015 | NA | OVERALL | TDOSE | 0 | < 1000 mg |
01-701-1015 | NA | OVERALL | TPDOSE | 0 | < 1000 mg |
01-701-1015 | NA | OVERALL | TDURD | 182 | >=90 days |
01-701-1015 | BASELINE | INDIVIDUAL | DURD | 15 | NA |
01-701-1015 | BASELINE | INDIVIDUAL | DOSE | 0 | NA |
01-701-1015 | BASELINE | INDIVIDUAL | PLDOSE | 0 | NA |
01-701-1015 | BASELINE | INDIVIDUAL | ADJ | NA | NA |
01-701-1015 | BASELINE | INDIVIDUAL | ADJAE | NA | NA |
01-701-1015 | WEEK 2 | INDIVIDUAL | DURD | 153 | NA |
01-701-1015 | WEEK 2 | INDIVIDUAL | DOSE | 0 | NA |
ASEQ
The {admiral}
function
derive_var_obs_number()
can be used to derive
ASEQ
. An example call is:
adex <- derive_var_obs_number(
adex,
new_var = ASEQ,
by_vars = exprs(STUDYID, USUBJID),
order = exprs(PARCAT1, ASTDT, VISIT, VISITNUM, EXSEQ, PARAMN),
check_type = "error"
)
USUBJID | VISIT | PARCAT1 | PARAMCD | AVAL | ASTDT | ASEQ |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | INDIVIDUAL | DURD | 15 | 2014-01-02 | 1 |
01-701-1015 | BASELINE | INDIVIDUAL | DOSE | 0 | 2014-01-02 | 2 |
01-701-1015 | BASELINE | INDIVIDUAL | PLDOSE | 0 | 2014-01-02 | 3 |
01-701-1015 | BASELINE | INDIVIDUAL | ADJ | NA | 2014-01-02 | 4 |
01-701-1015 | BASELINE | INDIVIDUAL | ADJAE | NA | 2014-01-02 | 5 |
01-701-1015 | WEEK 2 | INDIVIDUAL | DURD | 153 | 2014-01-17 | 6 |
01-701-1015 | WEEK 2 | INDIVIDUAL | DOSE | 0 | 2014-01-17 | 7 |
01-701-1015 | WEEK 2 | INDIVIDUAL | PLDOSE | 0 | 2014-01-17 | 8 |
01-701-1015 | WEEK 2 | INDIVIDUAL | ADJ | NA | 2014-01-17 | 9 |
01-701-1015 | WEEK 2 | INDIVIDUAL | ADJAE | NA | 2014-01-17 | 10 |
ADSL
variablesIf needed, the other ADSL
variables can now be
added:
Adding labels and attributes for SAS transport files is supported by the following packages:
metacore: establish a common foundation for the use of metadata within an R session.
metatools: enable the use of metacore objects. Metatools can be used to build datasets or enhance columns in existing datasets as well as checking datasets against the metadata.
xportr: functionality to associate all metadata information to a local R data frame, perform data set level validation checks and convert into a transport v5 file(xpt).
NOTE: All these packages are in the experimental phase, but the vision is to have them associated with an End to End pipeline under the umbrella of the pharmaverse. An example of applying metadata and perform associated checks can be found at the pharmaverse E2E example.
ADaM | Sourcing Command |
---|---|
ADEX | use_ad_template("ADEX") |
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.