Creating an OCCDS ADaM

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

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, ADSL, SUPPAE, ’SUPPDM`.

For example purpose, the CDISC Pilot SDTM and ADaM datasets—which are included in {admiral}—are used.

library(admiral)
library(dplyr)
library(admiral.test)
library(lubridate)

data("ae")
data("suppae")
data("adsl")

The SUPPAE domain can be joined to the ’AEdomain using the functionderive_vars_suppqual()`.

This function will transpose the supplemental SDTM domain (e.g. SUPPAE) and join the transposed data to the parent domain (e.g. ae) by STUDYID, USUBJID using the IDVAR and IDVARVAL as an additional join variable.

Example call:

To derive Supplemental Qualifiers, derive_vars_suppqual() can be used.

ae <- derive_vars_suppqual(ae, suppae)
USUBJID AETERM AEDECOD AESTDTC AETRTEM
01-701-1015 APPLICATION SITE ERYTHEMA APPLICATION SITE ERYTHEMA 2014-01-03 Y
01-701-1015 APPLICATION SITE PRURITUS APPLICATION SITE PRURITUS 2014-01-03 Y
01-701-1015 DIARRHOEA DIARRHOEA 2014-01-09 Y

At this step, it may be useful to join ADSL to your AE domain. Only the ADSL variables used for derivations are selected at this step. The rest of the relevant ADSL would be added later.


adsl_vars <- vars(TRTSDT, TRTEDT, TRT01A, TRT01P, DTHDT, EOSDT)

adae <- left_join(
  ae,
  select(adsl, STUDYID, USUBJID, !!!adsl_vars),
  by = c("STUDYID", "USUBJID")
)
USUBJID AESEQ AETERM AESTDTC TRTSDT TRTEDT TRT01A TRT01P DTHDT EOSDT
01-701-1015 1 APPLICATION SITE ERYTHEMA 2014-01-03 2014-01-02 2014-07-02 Pbo Pbo NA 2014-07-02
01-701-1015 2 APPLICATION SITE PRURITUS 2014-01-03 2014-01-02 2014-07-02 Pbo Pbo NA 2014-07-02
01-701-1015 3 DIARRHOEA 2014-01-09 2014-01-02 2014-07-02 Pbo Pbo NA 2014-07-02
01-701-1023 3 ATRIOVENTRICULAR BLOCK SECOND DEGREE 2012-08-26 2012-08-05 2012-09-01 Pbo Pbo NA 2012-09-02
01-701-1023 1 ERYTHEMA 2012-08-07 2012-08-05 2012-09-01 Pbo Pbo NA 2012-09-02
01-701-1023 2 ERYTHEMA 2012-08-07 2012-08-05 2012-09-01 Pbo Pbo NA 2012-09-02
01-701-1023 4 ERYTHEMA 2012-08-07 2012-08-05 2012-09-01 Pbo Pbo NA 2012-09-02
01-701-1028 1 APPLICATION SITE ERYTHEMA 2013-07-21 2013-07-19 2014-01-14 Xan_Hi Xan_Hi NA 2014-01-14
01-701-1028 2 APPLICATION SITE PRURITUS 2013-08-08 2013-07-19 2014-01-14 Xan_Hi Xan_Hi NA 2014-01-14
01-701-1034 1 APPLICATION SITE PRURITUS 2014-08-27 2014-07-01 2014-12-30 Xan_Hi Xan_Hi NA 2014-12-30

Derive/Impute End and Start Analysis Date/time and Relative Day

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_var_astdy() and derive_var_aendy() can be used to create ASTDY and AENDY, respectively.

adae <- adae %>% 
  derive_vars_dtm(
    dtc = AESTDTC,
    new_vars_prefix = "AST",
    date_imputation = "first",
    time_imputation = "first",
    min_dates = vars(TRTSDT)
  ) %>% 
  derive_vars_dtm(
    dtc = AEENDTC,
    new_vars_prefix = "AEN",
    date_imputation = "last",
    time_imputation = "last",
    max_dates = vars(DTHDT, EOSDT)
  ) %>%
  derive_vars_dtm_to_dt(vars(ASTDTM, AENDTM)
  ) %>% 
  derive_var_astdy(
    reference_date = TRTSDT, 
    date = ASTDT
  ) %>%
  derive_var_aendy(
    reference_date = TRTSDT, 
    date = AENDT
  )
USUBJID AESTDTC AEENDTC ASTDTM ASTDT ASTDY AENDTM AENDT AENDY
01-701-1015 2014-01-03 2014-01-03 2014-01-03 2 NA NA NA
01-701-1015 2014-01-03 2014-01-03 2014-01-03 2 NA NA NA
01-701-1015 2014-01-09 2014-01-11 2014-01-09 2014-01-09 8 2014-01-11 23:59:59 2014-01-11 10
01-701-1023 2012-08-26 2012-08-26 2012-08-26 22 NA NA NA
01-701-1023 2012-08-07 2012-08-30 2012-08-07 2012-08-07 3 2012-08-30 23:59:59 2012-08-30 26
01-701-1023 2012-08-07 2012-08-07 2012-08-07 3 NA NA NA
01-701-1023 2012-08-07 2012-08-30 2012-08-07 2012-08-07 3 2012-08-30 23:59:59 2012-08-30 26
01-701-1028 2013-07-21 2013-07-21 2013-07-21 3 NA NA NA
01-701-1028 2013-08-08 2013-08-08 2013-08-08 21 NA NA NA
01-701-1034 2014-08-27 2014-08-27 2014-08-27 58 NA NA NA

See also Date and Time Imputation.

Derive Durations

The function derive_vars_duration() can be used to create the variables ADURN and ADURU.

adae <- adae %>%
  derive_vars_duration(
    new_var = ADURN,
    new_var_unit = ADURU,
    start_date = ASTDT,
    end_date = AENDT
  )
USUBJID AESTDTC AEENDTC ASTDT AENDT ADURN ADURU
01-701-1015 2014-01-03 2014-01-03 NA NA DAYS
01-701-1015 2014-01-03 2014-01-03 NA NA DAYS
01-701-1015 2014-01-09 2014-01-11 2014-01-09 2014-01-11 3 DAYS
01-701-1023 2012-08-26 2012-08-26 NA NA DAYS
01-701-1023 2012-08-07 2012-08-30 2012-08-07 2012-08-30 24 DAYS
01-701-1023 2012-08-07 2012-08-07 NA NA DAYS
01-701-1023 2012-08-07 2012-08-30 2012-08-07 2012-08-30 24 DAYS
01-701-1028 2013-07-21 2013-07-21 NA NA DAYS
01-701-1028 2013-08-08 2013-08-08 NA NA DAYS
01-701-1034 2014-08-27 2014-08-27 NA NA DAYS

Derive ATC variables

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.

cm <- tibble::tribble(
         ~USUBJID, ~CMGRPID,  ~CMREFID,            ~CMDECOD,
   "BP40257-1001",     "14", "1192056",       "PARACETAMOL",
   "BP40257-1001",     "18", "2007001",        "SOLUMEDROL",
   "BP40257-1002",     "19", "2791596",    "SPIRONOLACTONE"
 )
 facm <- tibble::tribble(
         ~USUBJID, ~FAGRPID,  ~FAREFID,   ~FATESTCD, ~FASTRESC,
   "BP40257-1001",      "1", "1192056",  "CMATC1CD",       "N",
   "BP40257-1001",      "1", "1192056",  "CMATC2CD",     "N02",
   "BP40257-1001",      "1", "1192056",  "CMATC3CD",    "N02B",
   "BP40257-1001",      "1", "1192056",  "CMATC4CD",   "N02BE",

   "BP40257-1001",      "1", "2007001",  "CMATC1CD",       "D",
   "BP40257-1001",      "1", "2007001",  "CMATC2CD",     "D10",
   "BP40257-1001",      "1", "2007001",  "CMATC3CD",    "D10A",
   "BP40257-1001",      "1", "2007001",  "CMATC4CD",   "D10AA",
   "BP40257-1001",      "2", "2007001",  "CMATC1CD",       "D",
   "BP40257-1001",      "2", "2007001",  "CMATC2CD",     "D07",
   "BP40257-1001",      "2", "2007001",  "CMATC3CD",    "D07A",
   "BP40257-1001",      "2", "2007001",  "CMATC4CD",   "D07AA",
   "BP40257-1001",      "3", "2007001",  "CMATC1CD",       "H",
   "BP40257-1001",      "3", "2007001",  "CMATC2CD",     "H02",
   "BP40257-1001",      "3", "2007001",  "CMATC3CD",    "H02A",
   "BP40257-1001",      "3", "2007001",  "CMATC4CD",   "H02AB",

   "BP40257-1002",      "1", "2791596",  "CMATC1CD",       "C",
   "BP40257-1002",      "1", "2791596",  "CMATC2CD",     "C03",
   "BP40257-1002",      "1", "2791596",  "CMATC3CD",    "C03D",
   "BP40257-1002",      "1", "2791596",  "CMATC4CD",   "C03DA"
 )

derive_vars_atc(cm, facm)
#> # A tibble: 5 x 8
#>   USUBJID      CMGRPID CMREFID CMDECOD        ATC1CD ATC2CD ATC3CD ATC4CD
#>   <chr>        <chr>   <chr>   <chr>          <chr>  <chr>  <chr>  <chr> 
#> 1 BP40257-1001 14      1192056 PARACETAMOL    N      N02    N02B   N02BE 
#> 2 BP40257-1001 18      2007001 SOLUMEDROL     D      D10    D10A   D10AA 
#> 3 BP40257-1001 18      2007001 SOLUMEDROL     D      D07    D07A   D07AA 
#> 4 BP40257-1001 18      2007001 SOLUMEDROL     H      H02    H02A   H02AB 
#> 5 BP40257-1002 19      2791596 SPIRONOLACTONE C      C03    C03D   C03DA

Derive Planned and Actual Treatment

TRTA and TRTP must correlate to treatment TRTxxP and/or TRTxxA in ADSL. The derivation of TRTA and TRTP for a record are protocol and analysis specific. {admiral}does not currently have functionality to assist with TRTA and TRTP assignment.

However, an example of a simple implementation could be:

adae <- mutate(adae, TRTP = TRT01P, TRTA = TRT01A)

count(adae, TRTP, TRTA, TRT01P, TRT01A)
#> # A tibble: 3 x 5
#>   TRTP   TRTA   TRT01P TRT01A     n
#>   <chr>  <chr>  <chr>  <chr>  <int>
#> 1 Pbo    Pbo    Pbo    Pbo      301
#> 2 Xan_Hi Xan_Hi Xan_Hi Xan_Hi   455
#> 3 Xan_Lo Xan_Lo Xan_Lo Xan_Lo   435

Derive Date/Date-time of Last Dose

The function derive_var_last_dose_date() can be used to derive the last dose date before the start of the event.

Additionally, this function can also provide the traceability variables (e.g. LDOSEDOM, LDOSESEQ) using the traceability_vars argument.

data(ex_single)
adae <- adae %>% 
  derive_var_last_dose_date(
    ex_single,
    filter_ex = (EXDOSE > 0 | (EXDOSE == 0 & grepl("PLACEBO", EXTRT))) &
      nchar(EXENDTC) >= 10,
    dose_date = EXSTDTC,
    analysis_date = ASTDT,
    single_dose_condition = (EXSTDTC == EXENDTC),
    new_var = LDOSEDTM,
    output_datetime = TRUE
  )
USUBJID AEDECOD AESEQ AESTDTC AEENDTC ASTDT AENDT LDOSEDTM
01-701-1015 APPLICATION SITE ERYTHEMA 1 2014-01-03 2014-01-03 NA 2014-01-03
01-701-1015 APPLICATION SITE PRURITUS 2 2014-01-03 2014-01-03 NA 2014-01-03
01-701-1015 DIARRHOEA 3 2014-01-09 2014-01-11 2014-01-09 2014-01-11 2014-01-09
01-701-1023 ATRIOVENTRICULAR BLOCK SECOND DEGREE 3 2012-08-26 2012-08-26 NA 2012-08-26
01-701-1023 ERYTHEMA 1 2012-08-07 2012-08-30 2012-08-07 2012-08-30 2012-08-07
01-701-1023 ERYTHEMA 2 2012-08-07 2012-08-07 NA 2012-08-07
01-701-1023 ERYTHEMA 4 2012-08-07 2012-08-30 2012-08-07 2012-08-30 2012-08-07
01-701-1028 APPLICATION SITE ERYTHEMA 1 2013-07-21 2013-07-21 NA 2013-07-21
01-701-1028 APPLICATION SITE PRURITUS 2 2013-08-08 2013-08-08 NA 2013-08-01
01-701-1034 APPLICATION SITE PRURITUS 1 2014-08-27 2014-08-27 NA 2014-07-15

Derive Severity, Causality, and Toxicity Grade

The variables ASEV, AREL, and ATOXGR can be added by simply mutate() if no imputation is required.

adae <- adae %>%
  mutate(
    ASEV = AESEV, 
    AREL = AEREL
  )

Derive Treatment Emergent Flag

To derive the treatment emergent flag TRTEMFL, one can use simple dplyr::mutate(). In the example below, we use 30 days in the flag derivation.

adae <- adae %>% 
  mutate(
    TRTEMFL = ifelse(ASTDT >= TRTSDT & ASTDT <= TRTEDT + days(30), "Y", NA_character_)
  )
USUBJID TRTSDT TRTEDT AESTDTC ASTDT TRTEMFL
01-701-1015 2014-01-02 2014-07-02 2014-01-03 2014-01-03 Y
01-701-1015 2014-01-02 2014-07-02 2014-01-03 2014-01-03 Y
01-701-1015 2014-01-02 2014-07-02 2014-01-09 2014-01-09 Y
01-701-1023 2012-08-05 2012-09-01 2012-08-26 2012-08-26 Y
01-701-1023 2012-08-05 2012-09-01 2012-08-07 2012-08-07 Y
01-701-1023 2012-08-05 2012-09-01 2012-08-07 2012-08-07 Y
01-701-1023 2012-08-05 2012-09-01 2012-08-07 2012-08-07 Y
01-701-1028 2013-07-19 2014-01-14 2013-07-21 2013-07-21 Y
01-701-1028 2013-07-19 2014-01-14 2013-08-08 2013-08-08 Y
01-701-1034 2014-07-01 2014-12-30 2014-08-27 2014-08-27 Y

To derive on-treatment flag (ONTRTFL) in an ADaM dataset with a single assessment 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.

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
)
#> # A tibble: 3 x 5
#>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL
#>   <chr>   <date>     <date>     <date>     <chr>  
#> 1 P01     2020-02-24 2020-01-01 2020-03-01 Y      
#> 2 P02     2020-01-01 2020-01-01 2020-03-01 Y      
#> 3 P03     2019-12-31 2020-01-01 2020-03-01 <NA>

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
)
#> # A tibble: 3 x 5
#>   USUBJID ADT        TRTSDT     TRTEDT     ONTRTFL
#>   <chr>   <date>     <date>     <date>     <chr>  
#> 1 P01     2020-07-01 2020-01-01 2020-03-01 <NA>   
#> 2 P02     2020-04-30 2020-01-01 2020-03-01 Y      
#> 3 P03     2020-03-15 2020-01-01 2020-03-01 Y

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"
)
#> # A tibble: 3 x 5
#>   ADTM                TRTSDTM             TRTEDTM             TPT   ONTRTFL
#>   <dttm>              <dttm>              <dttm>              <chr> <chr>  
#> 1 2020-01-02 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 <NA>  Y      
#> 2 2020-01-01 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 PRE   <NA>   
#> 3 2019-12-31 12:00:00 2020-01-01 12:00:00 2020-03-01 12:00:00 <NA>  <NA>

Derive Occurrence Flags

The function derive_var_extreme_flag() can help derive variables such as AOCCIFL, AOCCPIFL, AOCCSIFL, AOCXIFL, AOCXPIFL, and AOCXSIFL.

If grades were collected, the following can be used to flag first occurrence of maximum toxicity grade.

adae <- adae %>% 
  derive_var_extreme_flag(
    by_vars = vars(USUBJID),
    order = vars(desc(ATOXGR), ASTDTM, AESEQ),
    new_var = AOCCIFL,
    filter = TRTEMFL == "Y", 
    mode = "first"
  )

Similarly, ASEV can also be used to derive the occurrence flags if severity is collected. In this case, the variable may need to be firstly recorded into a numeric one. Flag first occurrence of most severe adverse event:

adae <- adae %>%
  mutate(
    ASEVN = as.integer(factor(ASEV, levels = c("MILD", "MODERATE", "SEVERE", "DEATH THREATENING")))
  ) %>% 
  derive_var_extreme_flag(
    by_vars = vars(USUBJID),
    order = vars(desc(ASEVN), ASTDTM, AESEQ),
    new_var = AOCCIFL,
    filter = TRTEMFL == "Y", 
    mode = "first"
  )
USUBJID ASTDTM ASEV ASEVN AESEQ TRTEMFL AOCCIFL
01-701-1015 2014-01-03 MILD 1 1 Y Y
01-701-1015 2014-01-03 MILD 1 2 Y NA
01-701-1015 2014-01-09 MILD 1 3 Y NA
01-701-1023 2012-08-07 MODERATE 2 2 Y Y
01-701-1023 2012-08-07 MILD 1 1 Y NA
01-701-1023 2012-08-07 MILD 1 4 Y NA
01-701-1023 2012-08-26 MILD 1 3 Y NA
01-701-1028 2013-07-21 MILD 1 1 Y Y
01-701-1028 2013-08-08 MILD 1 2 Y NA
01-701-1034 2014-08-27 MILD 1 1 Y Y

Derive Query Variables

It is necessary for the dictionary query information to be passed into this function in a particular format which is detailed in derive_vars_query() to an ADaM.

For example, in ADAE, MedDRA SMQs and/or Customized Query variables may be needed.

This function expects the dictionary and/or lookup information to be provided as input in a standard structure.

The expected result is the input dataset with query variables added: See also Queries dataset documentation.

data("queries")
VAR_PREFIX QUERY_NAME QUERY_ID QUERY_SCOPE QUERY_SCOPE_NUM TERM_LEVEL TERM_NAME TERM_ID
CQ01 Dermatologic events NA NA NA AELLT APPLICATION SITE ERYTHEMA NA
CQ01 Dermatologic events NA NA NA AELLT APPLICATION SITE PRURITUS NA
CQ01 Dermatologic events NA NA NA AELLT ERYTHEMA NA
CQ01 Dermatologic events NA NA NA AELLT LOCALIZED ERYTHEMA NA
CQ01 Dermatologic events NA NA NA AELLT GENERALIZED PRURITUS NA
SMQ02 Immune-Mediated Hypothyroidism 20000160 BROAD 1 AEDECOD BIOPSY THYROID GLAND ABNORMAL NA
SMQ02 Immune-Mediated Hypothyroidism 20000160 BROAD 1 AEDECOD BLOOD THYROID STIMULATING HORMONE ABNORMAL NA
SMQ02 Immune-Mediated Hypothyroidism 20000160 NARROW 1 AEDECOD BIOPSY THYROID GLAND INCREASED NA
SMQ03 Immune-Mediated Guillain-Barre Syndrome 20000131 NARROW 2 AEDECOD GUILLAIN-BARRE SYNDROME NA
SMQ03 Immune-Mediated Guillain-Barre Syndrome 20000131 NARROW 2 AEDECOD MILLER FISHER SYNDROME NA 
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)
USUBJID ASTDTM AETERM AESEQ AEDECOD AELLT AELLTCD SMQ02NAM SMQ02CD SMQ02SC SMQ02SCN SMQ03NAM SMQ03CD SMQ03SC SMQ03SCN SMQ05NAM SMQ05CD SMQ05SC SMQ05SCN CQ01NAM CQ04NAM CQ04CD CQ06NAM CQ06CD
01 2020-06-02 23:59:59 ALANINE AMINOTRANSFERASE ABNORMAL 3 Alanine aminotransferase abnormal NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
02 2020-06-05 23:59:59 BASEDOW’S DISEASE 5 Basedow’s disease NA 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Immune-Mediated Colitis 10009888
03 2020-06-07 23:59:59 SOME TERM 2 Some query Some term NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
05 2020-06-09 23:59:59 ALVEOLAR PROTEINOSIS 7 Alveolar proteinosis NA NA NA NA NA NA NA NA NA NA Immune-Mediated Pneumonitis 20000042 NARROW 2 NA NA NA NA NA

Similarly to SMQ, the derive_vars_query() function can be used to derive Standardized Drug Groupings (SDG).

sdg <- tibble::tribble(
  ~VAR_PREFIX, ~QUERY_NAME,       ~SDG_ID, ~QUERY_SCOPE, ~QUERY_SCOPE_NUM, ~TERM_LEVEL, ~TERM_NAME,         ~TERM_ID,
  "SDG01",     "Diuretics",       11,      "BROAD",      1,                "CMDECOD",   "Diuretic 1",       NA,
  "SDG01",     "Diuretics",       11,      "BROAD",      2,                "CMDECOD",   "Diuretic 2",       NA,
  "SDG02",     "Costicosteroids", 12,      "BROAD",      1,                "CMDECOD",   "Costicosteroid 1", NA,
  "SDG02",     "Costicosteroids", 12,      "BROAD",      2,                "CMDECOD",   "Costicosteroid 2", NA,
  "SDG02",     "Costicosteroids", 12,      "BROAD",      2,                "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)
USUBJID ASTDTM CMDECOD SDG01NAM SDG01SC SDG01SCN SDG02NAM SDG02SC SDG02SCN
01 2020-06-02 23:59:59 Diuretic 1 Diuretics BROAD 1 NA NA NA
02 2020-06-05 23:59:59 Diuretic 1 Diuretics BROAD 1 NA NA NA
03 2020-06-07 23:59:59 Costicosteroid 2 NA NA NA Costicosteroids BROAD 2
05 2020-06-09 23:59:59 Diuretic 2 Diuretics BROAD 2 NA NA NA

Add the ADSL variables

If needed, the other ADSL variables can now be added:

adae <- adae %>%
  left_join(select(adsl, !!!admiral:::negate_vars(adsl_vars)),
            by = c("STUDYID", "USUBJID")
  )
#> Warning: Column `STUDYID` has different attributes on LHS and RHS of join
#> Warning: Column `USUBJID` has different attributes on LHS and RHS of join
USUBJID AEDECOD ASTDTM DTHDT RFSTDTC RFENDTC AGE AGEU SEX
01-701-1015 APPLICATION SITE ERYTHEMA 2014-01-03 NA 2014-01-02 2014-07-02 63 YEARS F
01-701-1015 APPLICATION SITE PRURITUS 2014-01-03 NA 2014-01-02 2014-07-02 63 YEARS F
01-701-1015 DIARRHOEA 2014-01-09 NA 2014-01-02 2014-07-02 63 YEARS F
01-701-1023 ERYTHEMA 2012-08-07 NA 2012-08-05 2012-09-02 64 YEARS M
01-701-1023 ERYTHEMA 2012-08-07 NA 2012-08-05 2012-09-02 64 YEARS M
01-701-1023 ERYTHEMA 2012-08-07 NA 2012-08-05 2012-09-02 64 YEARS M
01-701-1023 ATRIOVENTRICULAR BLOCK SECOND DEGREE 2012-08-26 NA 2012-08-05 2012-09-02 64 YEARS M
01-701-1028 APPLICATION SITE ERYTHEMA 2013-07-21 NA 2013-07-19 2014-01-14 71 YEARS M
01-701-1028 APPLICATION SITE PRURITUS 2013-08-08 NA 2013-07-19 2014-01-14 71 YEARS M
01-701-1034 APPLICATION SITE PRURITUS 2014-08-27 NA 2014-07-01 2014-12-30 77 YEARS F

Example Scripts

ADaM Sample Code
ADAE ad_adae.R
ADCM ad_adcm.R