---
title: "Creating an OCCDS ADaM"
output:
rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Creating an OCCDS ADaM}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(admiraldev)
```
# Introduction
This article describes creating an OCCDS ADaM. Examples are currently
presented and tested in the context of `ADAE`.
However, the examples could be applied to other OCCDS ADaMs such as
`ADCM`, `ADMH`, `ADDV`, etc.
**Note**: *All examples assume CDISC SDTM and/or ADaM format as input unless
otherwise specified.*
# Programming Workflow
* [Read in Data](#readdata)
* [Derive/Impute End and Start Analysis Date/time and Relative Day](#datetime)
* [Derive Durations](#duration)
* [Derive ATC variables](#atc)
* [Derive Planned and Actual Treatment](#trtpa)
* [Derive Date/Date-time of Last Dose](#last_dose)
* [Derive Severity, Causality, and Toxicity Grade](#severity)
* [Derive Treatment Emergent Flag](#trtflag)
* [Derive Occurrence Flags](#occflag)
* [Derive Query Variables](#query)
* [Add ADSL variables](#adsl_vars)
* [Derive Analysis Sequence Number](#aseq)
* [Add Labels and Attributes](#attributes)
## Read in Data {#readdata}
To start, all data frames needed for the creation of `ADAE` should be read into
the environment. This will be a company specific process. Some of the data
frames needed may be `AE` and `ADSL`
For example purpose, the CDISC Pilot SDTM and ADaM datasets ---which are
included in `{pharmaversesdtm}`--- are used.
```{r, message=FALSE, warning=FALSE}
library(admiral)
library(dplyr, warn.conflicts = FALSE)
library(pharmaversesdtm)
library(lubridate)
ae <- pharmaversesdtm::ae
adsl <- admiral::admiral_adsl
ex_single <- admiral::ex_single
ae <- convert_blanks_to_na(ae)
```
```{r echo = FALSE}
ae <- filter(ae, USUBJID %in% c("01-701-1015", "01-701-1023", "01-703-1086", "01-703-1096", "01-707-1037", "01-716-1024"))
```
At this step, it may be useful to join `ADSL` to your `AE` 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.
```{r eval=TRUE}
adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01A, TRT01P, DTHDT, EOSDT)
adae <- derive_vars_merged(
ae,
dataset_add = adsl,
new_vars = adsl_vars,
by = exprs(STUDYID, USUBJID)
)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
adae,
display_vars = exprs(
USUBJID, AESEQ, AETERM, AESTDTC, TRTSDT,
TRTEDT, TRT01A, TRT01P, DTHDT, EOSDT
)
)
```
## Derive/Impute End and Start Analysis Date/time and Relative Day {#datetime}
This part derives `ASTDTM`, `ASTDT`, `ASTDY`, `AENDTM`, `AENDT`, and `AENDY`.
The function `derive_vars_dtm()` can be used to derive `ASTDTM` and `AENDTM`
where `ASTDTM` could be company-specific. `ASTDT` and `AENDT` can be derived
from `ASTDTM` and `AENDTM`, respectively, using function `derive_vars_dtm_to_dt()`.
`derive_vars_dy()` can be used to create `ASTDY` and `AENDY`.
```{r eval=TRUE}
adae <- adae %>%
derive_vars_dtm(
dtc = AESTDTC,
new_vars_prefix = "AST",
highest_imputation = "M",
min_dates = exprs(TRTSDT)
) %>%
derive_vars_dtm(
dtc = AEENDTC,
new_vars_prefix = "AEN",
highest_imputation = "M",
date_imputation = "last",
time_imputation = "last",
max_dates = exprs(DTHDT, EOSDT)
) %>%
derive_vars_dtm_to_dt(exprs(ASTDTM, AENDTM)) %>%
derive_vars_dy(
reference_date = TRTSDT,
source_vars = exprs(ASTDT, AENDT)
)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
adae,
display_vars = exprs(
USUBJID, AESTDTC, AEENDTC, ASTDTM, ASTDT,
ASTDY, AENDTM, AENDT, AENDY
)
)
```
See also [Date and Time Imputation](imputation.html).
## Derive Durations {#duration}
The function `derive_vars_duration()` can be used to create the variables
`ADURN` and `ADURU`.
```{r eval=TRUE}
adae <- adae %>%
derive_vars_duration(
new_var = ADURN,
new_var_unit = ADURU,
start_date = ASTDT,
end_date = AENDT
)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
adae,
display_vars = exprs(
USUBJID, AESTDTC, AEENDTC, ASTDT, AENDT,
ADURN, ADURU
)
)
```
## Derive ATC variables {#atc}
The function `derive_vars_atc()` can be used to derive
ATC Class Variables.
It helps to add Anatomical Therapeutic Chemical class variables from `FACM` to `ADCM`.
The expected result is the input dataset with ATC variables added.
```{r eval=TRUE}
cm <- tibble::tribble(
~STUDYID, ~USUBJID, ~CMGRPID, ~CMREFID, ~CMDECOD,
"STUDY01", "BP40257-1001", "14", "1192056", "PARACETAMOL",
"STUDY01", "BP40257-1001", "18", "2007001", "SOLUMEDROL",
"STUDY01", "BP40257-1002", "19", "2791596", "SPIRONOLACTONE"
)
facm <- tibble::tribble(
~STUDYID, ~USUBJID, ~FAGRPID, ~FAREFID, ~FATESTCD, ~FASTRESC,
"STUDY01", "BP40257-1001", "1", "1192056", "CMATC1CD", "N",
"STUDY01", "BP40257-1001", "1", "1192056", "CMATC2CD", "N02",
"STUDY01", "BP40257-1001", "1", "1192056", "CMATC3CD", "N02B",
"STUDY01", "BP40257-1001", "1", "1192056", "CMATC4CD", "N02BE",
"STUDY01", "BP40257-1001", "1", "2007001", "CMATC1CD", "D",
"STUDY01", "BP40257-1001", "1", "2007001", "CMATC2CD", "D10",
"STUDY01", "BP40257-1001", "1", "2007001", "CMATC3CD", "D10A",
"STUDY01", "BP40257-1001", "1", "2007001", "CMATC4CD", "D10AA",
"STUDY01", "BP40257-1001", "2", "2007001", "CMATC1CD", "D",
"STUDY01", "BP40257-1001", "2", "2007001", "CMATC2CD", "D07",
"STUDY01", "BP40257-1001", "2", "2007001", "CMATC3CD", "D07A",
"STUDY01", "BP40257-1001", "2", "2007001", "CMATC4CD", "D07AA",
"STUDY01", "BP40257-1001", "3", "2007001", "CMATC1CD", "H",
"STUDY01", "BP40257-1001", "3", "2007001", "CMATC2CD", "H02",
"STUDY01", "BP40257-1001", "3", "2007001", "CMATC3CD", "H02A",
"STUDY01", "BP40257-1001", "3", "2007001", "CMATC4CD", "H02AB",
"STUDY01", "BP40257-1002", "1", "2791596", "CMATC1CD", "C",
"STUDY01", "BP40257-1002", "1", "2791596", "CMATC2CD", "C03",
"STUDY01", "BP40257-1002", "1", "2791596", "CMATC3CD", "C03D",
"STUDY01", "BP40257-1002", "1", "2791596", "CMATC4CD", "C03DA"
)
derive_vars_atc(cm, dataset_facm = facm, id_vars = exprs(FAGRPID))
```
## Derive Planned and Actual Treatment {#trtpa}
`TRTA` and `TRTP` must match at least one value of the character treatment
variables in ADSL (e.g., `TRTxxA`/`TRTxxP`, `TRTSEQA`/`TRTSEQP`,
`TRxxAGy`/`TRxxPGy`).
An example of a simple implementation for a study without periods could be:
```{r eval=TRUE}
adae <- mutate(adae, TRTP = TRT01P, TRTA = TRT01A)
count(adae, TRTP, TRTA, TRT01P, TRT01A)
```
For studies with periods see the ["Visit and Period Variables"
vignette](visits_periods.html#treatment_bds).
## Derive Date/Date-time of Last Dose {#last_dose}
The function `derive_vars_joined()` can be used to derive the last dose date before the start of the event.
```{r eval=TRUE}
ex_single <- derive_vars_dtm(
ex_single,
dtc = EXSTDTC,
new_vars_prefix = "EXST",
flag_imputation = "none"
)
adae <- derive_vars_joined(
adae,
ex_single,
by_vars = exprs(STUDYID, USUBJID),
new_vars = exprs(LDOSEDTM = EXSTDTM),
join_vars = exprs(EXSTDTM),
join_type = "all",
order = exprs(EXSTDTM),
filter_add = (EXDOSE > 0 | (EXDOSE == 0 & grepl("PLACEBO", EXTRT))) & !is.na(EXSTDTM),
filter_join = EXSTDTM <= ASTDTM,
mode = "last"
)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
adae,
display_vars = exprs(
USUBJID, AEDECOD, AESEQ, AESTDTC, AEENDTC,
ASTDT, AENDT, LDOSEDTM
)
)
```
## Derive Severity, Causality, and Toxicity Grade {#severity}
The variables `ASEV`, `AREL`, and `ATOXGR` can be added using simple
`dplyr::mutate()` assignments, if no imputation is required.
```{r eval=TRUE}
adae <- adae %>%
mutate(
ASEV = AESEV,
AREL = AEREL
)
```
## Derive Treatment Emergent Flag {#trtflag}
To derive the treatment emergent flag `TRTEMFL`, one can call
`derive_var_trtemfl()`. In the example below, we use 30 days in the flag
derivation.
```{r eval=TRUE}
adae <- adae %>%
derive_var_trtemfl(
trt_start_date = TRTSDT,
trt_end_date = TRTEDT,
end_window = 30
)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
adae,
display_vars = exprs(
USUBJID, TRTSDT, TRTEDT, AESTDTC, ASTDT,
TRTEMFL
)
)
```
To derive on-treatment flag (`ONTRTFL`) in an ADaM dataset with a single occurrence date, we
use `derive_var_ontrtfl()`.
The expected result is the input dataset with an additional column named
`ONTRTFL` with a value of `"Y"` or `NA`.
If you want to also check an end date, you could add the `end_date` argument.
Note that in this scenario you could set `span_period = TRUE` if you want occurrences that started
prior to drug intake, and was ongoing or ended after this time to be considered as on-treatment.
```{r eval=TRUE}
bds1 <- tibble::tribble(
~USUBJID, ~ADT, ~TRTSDT, ~TRTEDT,
"P01", ymd("2020-02-24"), ymd("2020-01-01"), ymd("2020-03-01"),
"P02", ymd("2020-01-01"), ymd("2020-01-01"), ymd("2020-03-01"),
"P03", ymd("2019-12-31"), ymd("2020-01-01"), ymd("2020-03-01")
)
derive_var_ontrtfl(
bds1,
start_date = ADT,
ref_start_date = TRTSDT,
ref_end_date = TRTEDT
)
bds2 <- tibble::tribble(
~USUBJID, ~ADT, ~TRTSDT, ~TRTEDT,
"P01", ymd("2020-07-01"), ymd("2020-01-01"), ymd("2020-03-01"),
"P02", ymd("2020-04-30"), ymd("2020-01-01"), ymd("2020-03-01"),
"P03", ymd("2020-03-15"), ymd("2020-01-01"), ymd("2020-03-01")
)
derive_var_ontrtfl(
bds2,
start_date = ADT,
ref_start_date = TRTSDT,
ref_end_date = TRTEDT,
ref_end_window = 60
)
bds3 <- tibble::tribble(
~ADTM, ~TRTSDTM, ~TRTEDTM, ~TPT,
"2020-01-02T12:00", "2020-01-01T12:00", "2020-03-01T12:00", NA,
"2020-01-01T12:00", "2020-01-01T12:00", "2020-03-01T12:00", "PRE",
"2019-12-31T12:00", "2020-01-01T12:00", "2020-03-01T12:00", NA
) %>%
mutate(
ADTM = ymd_hm(ADTM),
TRTSDTM = ymd_hm(TRTSDTM),
TRTEDTM = ymd_hm(TRTEDTM)
)
derive_var_ontrtfl(
bds3,
start_date = ADTM,
ref_start_date = TRTSDTM,
ref_end_date = TRTEDTM,
filter_pre_timepoint = TPT == "PRE"
)
```
## Derive Occurrence Flags {#occflag}
The function `derive_var_extreme_flag()` can help derive variables such as
`AOCCIFL`, `AOCCPIFL`, `AOCCSIFL`, and `AOCCzzFL`.
If grades were collected, the following can be used to flag first occurrence of
maximum toxicity grade.
```{r, eval=FALSE}
adae <- adae %>%
restrict_derivation(
derivation = derive_var_extreme_flag,
args = params(
by_vars = exprs(USUBJID),
order = exprs(desc(ATOXGR), ASTDTM, AESEQ),
new_var = AOCCIFL,
mode = "first"
),
filter = TRTEMFL == "Y"
)
```
Similarly, `ASEV` can also be used to derive the occurrence flags, if severity is
collected. In this case, the variable will need to be recoded to a numeric variable.
Flag first occurrence of most severe adverse event:
```{r, eval=TRUE}
adae <- adae %>%
restrict_derivation(
derivation = derive_var_extreme_flag,
args = params(
by_vars = exprs(USUBJID),
order = exprs(
as.integer(factor(
ASEV,
levels = c("DEATH THREATENING", "SEVERE", "MODERATE", "MILD")
)),
ASTDTM, AESEQ
),
new_var = AOCCIFL,
mode = "first"
),
filter = TRTEMFL == "Y"
)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
adae,
display_vars = exprs(
USUBJID, ASTDTM, ASEV, AESEQ, TRTEMFL, AOCCIFL
)
)
```
## Derive Query Variables {#query}
For deriving query variables `SMQzzNAM`, `SMQzzCD`, `SMQzzSC`, `SMQzzSCN`, or
`CQzzNAM` the `derive_vars_query()` function can be used. As input it expects a
queries dataset, which provides the definition of the queries. See [Queries
dataset documentation](queries_dataset.html) for a detailed description of the
queries dataset. The `create_query_data()` function can be used to create
queries datasets.
The following example shows how to derive query variables for Standardized MedDRA
Queries (SMQs) in ADAE.
```{r, eval=TRUE}
queries <- admiral::queries
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(queries)
```
```{r, eval=TRUE}
adae1 <- tibble::tribble(
~USUBJID, ~ASTDTM, ~AETERM, ~AESEQ, ~AEDECOD, ~AELLT, ~AELLTCD,
"01", "2020-06-02 23:59:59", "ALANINE AMINOTRANSFERASE ABNORMAL",
3, "Alanine aminotransferase abnormal", NA_character_, NA_integer_,
"02", "2020-06-05 23:59:59", "BASEDOW'S DISEASE",
5, "Basedow's disease", NA_character_, 1L,
"03", "2020-06-07 23:59:59", "SOME TERM",
2, "Some query", "Some term", NA_integer_,
"05", "2020-06-09 23:59:59", "ALVEOLAR PROTEINOSIS",
7, "Alveolar proteinosis", NA_character_, NA_integer_
)
adae_query <- derive_vars_query(dataset = adae1, dataset_queries = queries)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(adae_query)
```
Similarly to SMQ, the `derive_vars_query()` function can be used to derive
Standardized Drug Groupings (SDG).
```{r, eval=TRUE}
sdg <- tibble::tribble(
~PREFIX, ~GRPNAME, ~GRPID, ~SCOPE, ~SCOPEN, ~SRCVAR, ~TERMCHAR, ~TERMNUM,
"SDG01", "Diuretics", 11, "BROAD", 1, "CMDECOD", "Diuretic 1", NA,
"SDG01", "Diuretics", 11, "BROAD", 1, "CMDECOD", "Diuretic 2", NA,
"SDG02", "Costicosteroids", 12, "BROAD", 1, "CMDECOD", "Costicosteroid 1", NA,
"SDG02", "Costicosteroids", 12, "BROAD", 1, "CMDECOD", "Costicosteroid 2", NA,
"SDG02", "Costicosteroids", 12, "BROAD", 1, "CMDECOD", "Costicosteroid 3", NA,
)
adcm <- tibble::tribble(
~USUBJID, ~ASTDTM, ~CMDECOD,
"01", "2020-06-02 23:59:59", "Diuretic 1",
"02", "2020-06-05 23:59:59", "Diuretic 1",
"03", "2020-06-07 23:59:59", "Costicosteroid 2",
"05", "2020-06-09 23:59:59", "Diuretic 2"
)
adcm_query <- derive_vars_query(adcm, sdg)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(adcm_query)
```
## Add the `ADSL` variables {#adsl_vars}
If needed, the other `ADSL` variables can now be added:
```{r eval=TRUE, echo=TRUE}
adae <- adae %>%
derive_vars_merged(
dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
by_vars = exprs(STUDYID, USUBJID)
)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
adae,
display_vars = exprs(
USUBJID, AEDECOD, ASTDTM, DTHDT, RFSTDTC,
RFENDTC, AGE, AGEU, SEX
)
)
```
## Derive Analysis Sequence Number {#aseq}
The function `derive_var_obs_number()` can be used for deriving `ASEQ` variable to ensure the uniqueness of subject records within the dataset.
For example, there can be multiple records present in `ADCM` for a single subject with the same `ASTDTM` and `CMSEQ` variables. But these records still differ at ATC level:
``` {r eval=TRUE, echo=TRUE}
adcm <- tibble::tribble(
~USUBJID, ~ASTDTM, ~CMSEQ, ~CMDECOD, ~ATC1CD, ~ATC2CD, ~ATC3CD, ~ATC4CD,
"BP40257-1001", "2013-07-05 UTC", "14", "PARACETAMOL", "N", "N02", "N02B", "N02BE",
"BP40257-1001", "2013-08-15 UTC", "18", "SOLUMEDROL", "D", "D10", "D10A", "D10AA",
"BP40257-1001", "2013-08-15 UTC", "18", "SOLUMEDROL", "D", "D07", "D07A", "D07AA",
"BP40257-1001", "2013-08-15 UTC", "18", "SOLUMEDROL", "H", "H02", "H02A", "H02AB",
"BP40257-1002", "2012-12-15 UTC", "19", "SPIRONOLACTONE", "C", "C03", "C03D", "C03DA"
)
adcm_aseq <- adcm %>%
derive_var_obs_number(
by_vars = exprs(USUBJID),
order = exprs(ASTDTM, CMSEQ, ATC1CD, ATC2CD, ATC3CD, ATC4CD),
new_var = ASEQ,
check_type = "error"
)
```
```{r, eval=TRUE, echo=FALSE}
dataset_vignette(adcm_aseq)
```
## Add Labels and Attributes {#attributes}
Adding labels and attributes for SAS transport files is supported by the
following packages:
- [metacore](https://atorus-research.github.io/metacore/): establish a common
foundation for the use of metadata within an R session.
- [metatools](https://pharmaverse.github.io/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](https://atorus-research.github.io/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)](https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/movefile/n1xbwdre0giahfn11c99yjkpi2yb.htm).
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](https://github.com/pharmaverse). An example of applying metadata
and perform associated checks can be found at the [pharmaverse E2E example](https://pharmaverse.github.io/examples/adam/adsl).
# Example Scripts
ADaM | Sourcing Command
---- | --------------
ADAE | `use_ad_template("ADAE")`
ADCM | `use_ad_template("ADCM")`