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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 and ADSL

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

library(dplyr, warn.conflicts = FALSE)


ae <- admiral_ae
adsl <- admiral_adsl

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.

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

adae <- derive_vars_merged(
  dataset_add = adsl,
  new_vars = adsl_vars,
  by = vars(STUDYID, USUBJID)

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_vars_dy() can be used to create ASTDY and AENDY.

adae <- adae %>%
    dtc = AESTDTC,
    new_vars_prefix = "AST",
    highest_imputation = "M",
    min_dates = vars(TRTSDT)
  ) %>%
    dtc = AEENDTC,
    new_vars_prefix = "AEN",
    highest_imputation = "M",
    date_imputation = "last",
    time_imputation = "last",
    max_dates = vars(DTHDT, EOSDT)
  ) %>%
  derive_vars_dtm_to_dt(vars(ASTDTM, AENDTM)) %>%
    reference_date = TRTSDT,
    source_vars = vars(ASTDT, AENDT)

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 %>%
    new_var = ADURN,
    new_var_unit = ADURU,
    start_date = ASTDT,
    end_date = AENDT

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(
  "BP40257-1001",     "14", "1192056", "PARACETAMOL",
  "BP40257-1001",     "18", "2007001", "SOLUMEDROL",
  "BP40257-1002",     "19", "2791596", "SPIRONOLACTONE"
facm <- tibble::tribble(
  "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
#>   <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 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:

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

count(adae, TRTP, TRTA, TRT01P, TRT01A)
#> # A tibble: 2 x 5
#>   TRTP               TRTA              TRT01P            TRT01A                n
#>   <chr>              <chr>             <chr>             <chr>             <int>
#> 1 Placebo            Placebo           Placebo           Placebo              10
#> 2 Xanomeline Low Do… Xanomeline Low D… Xanomeline Low D… Xanomeline Low D…     6

For studies with periods see the “Visit and Period Variables” vignette.

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 traceability variables (e.g. LDOSEDOM, LDOSESEQ) using the traceability_vars argument.

ex_single <- derive_vars_dtm(
  dtc = EXSTDTC,
  new_vars_prefix = "EXST",
  flag_imputation = "none"
adae <- adae %>%
    filter_ex = (EXDOSE > 0 | (EXDOSE == 0 & grepl("PLACEBO", EXTRT))) &
    dose_date = EXSTDTM,
    analysis_date = ASTDT,
    single_dose_condition = (EXSTDTC == EXENDTC),
    new_var = LDOSEDTM,
    output_datetime = TRUE

Derive Severity, Causality, and Toxicity Grade

The variables ASEV, AREL, and ATOXGR can be added using simple dplyr::mutate() assignments, if no imputation is required.

adae <- adae %>%

Derive Treatment Emergent Flag

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.

adae <- adae %>%
    trt_start_date = TRTSDT,
    trt_end_date = TRTEDT,
    end_window = 30

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 = "Y" if you want occurrences that started prior to drug intake, and was ongoing or ended after this time to be considered as on-treatment.

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")
  start_date = ADT,
  ref_start_date = TRTSDT,
  ref_end_date = TRTEDT
#> # A tibble: 3 x 5
#>   <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")
  start_date = ADT,
  ref_start_date = TRTSDT,
  ref_end_date = TRTEDT,
  ref_end_window = 60
#> # A tibble: 3 x 5
#>   <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
) %>%
    ADTM = ymd_hm(ADTM),
    TRTSDTM = ymd_hm(TRTSDTM),
    TRTEDTM = ymd_hm(TRTEDTM)
  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 %>%
    derivation = derive_var_extreme_flag,
    args = params(
      by_vars = vars(USUBJID),
      order = vars(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:

adae <- adae %>%
    ASEVN = as.integer(factor(ASEV, levels = c("MILD", "MODERATE", "SEVERE", "DEATH THREATENING")))
  ) %>%
    derivation = derive_var_extreme_flag,
    args = params(
      by_vars = vars(USUBJID),
      order = vars(desc(ASEVN), ASTDTM, AESEQ),
      new_var = AOCCIFL,
      mode = "first"
    filter = TRTEMFL == "Y"

Derive Query Variables

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 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.

adae1 <- tibble::tribble(
  "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)

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

sdg <- tibble::tribble(
  "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)

Add the ADSL variables

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

adae <- adae %>%
    dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
    by_vars = vars(STUDYID, USUBJID)

Derive Analysis Sequence Number

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:

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 %>%
    by_vars    = vars(USUBJID),
    order      = vars(ASTDTM, CMSEQ, ATC1CD, ATC2CD, ATC3CD, ATC4CD),
    new_var    = ASEQ,
    check_type = "error"

Add Labels and Attributes

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.

Example Scripts

ADaM Sample Code
ADAE ad_adae.R
ADCM ad_adcm.R