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 TablesAVALCATy)ASEQASEQTo 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)
ex <- pharmaversesdtm::ex
adsl <- admiral::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 = get_admiral_option("subject_keys")
)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 13ADT, 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)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"
)
#> The default value of `ignore_seconds_flag` will change to "TRUE" in admiral
#> 1.4.0.
adex <- derive_vars_dtm(
adex,
dtc = EXENDTC,
highest_imputation = "M",
date_imputation = "last",
new_vars_prefix = "AEN"
)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:
adex <-
derive_vars_dy(adex,
reference_date = TRTSDT,
source_vars = exprs(ASTDT, AENDT)
)To compute the duration of treatment or exposure for a record, the derive_vars_duration() function can be used.
adex <- adex %>%
derive_vars_duration(
new_var = EXDURD,
start_date = ASTDT,
end_date = AENDT
)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
)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:
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))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 13Exposure 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 = c(get_admiral_option("subject_keys"), adsl_vars),
input_code = "DOSE",
set_values_to = exprs(
PARAMCD = "TDOSE",
PARCAT1 = "OVERALL",
AVAL = sum(AVAL, na.rm = TRUE)
)
)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 = c(get_admiral_option("subject_keys"), 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 6Dose 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 = c(get_admiral_option("subject_keys"), adsl_vars),
set_values_to = exprs(PARAMCD = "TNDOSINT"),
tadm_code = "TDOSE",
tpadm_code = "TPDOSE"
)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 tables
To 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 6Please note, this is an example only and additional columns may be needed for the join depending on your lookup/metadata table.
AVALCATy)
We can use the derive_vars_cat() function to derive the categorization variables.
avalcax_lookup <- exprs(
~PARAMCD, ~condition, ~AVALCAT1,
"TDURD", AVAL >= 90, ">= 90 days",
"TDURD", AVAL >= 30 & AVAL < 90, ">= 30 and < 90 days",
"TDURD", AVAL < 30, "< 30 days",
"TDOSE", AVAL < 1000, "< 1000 mg",
"TDOSE", AVAL >= 1000, ">= 1000 mg",
"TPDOSE", AVAL < 1000, "< 1000 mg",
"TPDOSE", AVAL >= 1000, ">= 1000 mg"
)
adex <- adex %>%
derive_vars_cat(
definition = avalcax_lookup,
by_vars = exprs(PARAMCD)
)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 = get_admiral_option("subject_keys"),
order = exprs(PARCAT1, ASTDT, VISIT, VISITNUM, EXSEQ, PARAMN),
check_type = "error"
)ADSL variables
If needed, the other ADSL variables can now be added:
adex <- adex %>%
derive_vars_merged(
dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
by_vars = get_admiral_option("subject_keys")
)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.