![[Stable]](figures/lifecycle-stable.svg)
Usage
get_datasets(x)
# S3 method for TealDataAbstract
get_datasets(x)
# S3 method for TealDatasetConnector
get_datasets(x)
# S3 method for TealDataset
get_datasets(x)Arguments
- x
(
TealData)
object containing datasets.
Examples
# TealData --------
library(scda)
latest_data <- synthetic_cdisc_data("latest")
adsl <- cdisc_dataset(
dataname = "ADSL",
x = latest_data$adsl,
code = "library(scda)\nADSL <- synthetic_cdisc_data(\"latest\")$adsl"
)
adae <- cdisc_dataset(
dataname = "ADAE",
x = latest_data$adae,
code = "library(scda)\nADTTE <- synthetic_cdisc_data(\"latest\")$adae"
)
rd <- cdisc_data(adsl, adae)
get_datasets(rd)
#> $ADSL
#> A CDISCTealDataset object containing the following data.frame (400 rows and 56 columns):
#> STUDYID USUBJID SUBJID SITEID AGE AGEU SEX
#> 1 AB12345 AB12345-CHN-3-id-128 id-128 CHN-3 32 YEARS M
#> 2 AB12345 AB12345-CHN-15-id-262 id-262 CHN-15 35 YEARS M
#> 3 AB12345 AB12345-RUS-3-id-378 id-378 RUS-3 30 YEARS F
#> 4 AB12345 AB12345-CHN-11-id-220 id-220 CHN-11 26 YEARS F
#> 5 AB12345 AB12345-CHN-7-id-267 id-267 CHN-7 40 YEARS M
#> 6 AB12345 AB12345-CHN-15-id-201 id-201 CHN-15 49 YEARS M
#> RACE ETHNIC COUNTRY DTHFL INVID
#> 1 ASIAN HISPANIC OR LATINO CHN Y INV ID CHN-3
#> 2 BLACK OR AFRICAN AMERICAN NOT HISPANIC OR LATINO CHN N INV ID CHN-15
#> 3 ASIAN NOT HISPANIC OR LATINO RUS N INV ID RUS-3
#> 4 ASIAN NOT HISPANIC OR LATINO CHN N INV ID CHN-11
#> 5 ASIAN NOT HISPANIC OR LATINO CHN N INV ID CHN-7
#> 6 ASIAN NOT HISPANIC OR LATINO CHN Y INV ID CHN-15
#> INVNAM ARM ARMCD ACTARM ACTARMCD TRT01P
#> 1 Dr. CHN-3 Doe A: Drug X ARM A A: Drug X ARM A A: Drug X
#> 2 Dr. CHN-15 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> 3 Dr. RUS-3 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> 4 Dr. CHN-11 Doe B: Placebo ARM B B: Placebo ARM B B: Placebo
#> 5 Dr. CHN-7 Doe B: Placebo ARM B B: Placebo ARM B B: Placebo
#> 6 Dr. CHN-15 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> TRT01A TRT02P TRT02A REGION1 STRATA1 STRATA2
#> 1 A: Drug X B: Placebo A: Drug X Asia C S2
#> 2 C: Combination B: Placebo C: Combination Asia C S1
#> 3 C: Combination A: Drug X B: Placebo Eurasia A S1
#> 4 B: Placebo B: Placebo B: Placebo Asia B S2
#> 5 B: Placebo C: Combination A: Drug X Asia C S1
#> 6 C: Combination B: Placebo C: Combination Asia C S2
#> BMRKR1 BMRKR2 ITTFL SAFFL BMEASIFL BEP01FL AEWITHFL RANDDT
#> 1 14.424934 MEDIUM Y Y Y Y N 2019-02-22
#> 2 4.055463 LOW Y Y N N Y 2019-02-26
#> 3 2.803240 HIGH Y Y Y N N 2019-02-24
#> 4 10.262734 MEDIUM Y Y Y Y N 2019-02-27
#> 5 6.206763 LOW Y Y N N N 2019-03-01
#> 6 6.906799 MEDIUM Y Y Y N N 2019-03-05
#> TRTSDTM TRTEDTM TRT01SDTM
#> 1 2019-02-24 11:09:18 2022-02-12 03:55:58 2019-02-24 11:09:18
#> 2 2019-02-26 09:05:00 2022-02-26 02:32:36 2019-02-26 09:05:00
#> 3 2019-02-28 03:19:08 2022-02-27 20:46:44 2019-02-28 03:19:08
#> 4 2019-03-01 13:33:03 2022-03-01 07:00:39 2019-03-01 13:33:03
#> 5 2019-03-02 00:09:16 2022-03-01 17:36:52 2019-03-02 00:09:16
#> 6 2019-03-05 15:23:44 2022-02-19 03:34:25 2019-03-05 15:23:44
#> TRT01EDTM TRT02SDTM TRT02EDTM
#> 1 2021-02-11 22:06:46 2021-02-11 22:06:46 2022-02-12 03:55:58
#> 2 2021-02-25 20:43:24 2021-02-25 20:43:24 2022-02-26 02:32:36
#> 3 2021-02-27 14:57:32 2021-02-27 14:57:32 2022-02-27 20:46:44
#> 4 2021-03-01 01:11:27 2021-03-01 01:11:27 2022-03-01 07:00:39
#> 5 2021-03-01 11:47:40 2021-03-01 11:47:40 2022-03-01 17:36:52
#> 6 2021-02-18 21:45:13 2021-02-18 21:45:13 2022-02-19 03:34:25
#> AP01SDTM AP01EDTM AP02SDTM
#> 1 2019-02-24 11:09:18 2021-02-11 22:06:46 2021-02-11 22:06:46
#> 2 2019-02-26 09:05:00 2021-02-25 20:43:24 2021-02-25 20:43:24
#> 3 2019-02-28 03:19:08 2021-02-27 14:57:32 2021-02-27 14:57:32
#> 4 2019-03-01 13:33:03 2021-03-01 01:11:27 2021-03-01 01:11:27
#> 5 2019-03-02 00:09:16 2021-03-01 11:47:40 2021-03-01 11:47:40
#> 6 2019-03-05 15:23:44 2021-02-18 21:45:13 2021-02-18 21:45:13
#> AP02EDTM EOSSTT EOTSTT EOSDT EOSDY DCSREAS
#> 1 2022-02-12 03:55:58 DISCONTINUED DISCONTINUED 2022-02-12 1084 DEATH
#> 2 2022-02-26 02:32:36 COMPLETED COMPLETED 2022-02-26 1096 <NA>
#> 3 2022-02-27 20:46:44 COMPLETED COMPLETED 2022-02-27 1096 <NA>
#> 4 2022-03-01 07:00:39 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 5 2022-03-01 17:36:52 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 6 2022-02-19 03:34:25 DISCONTINUED DISCONTINUED 2022-02-19 1082 DEATH
#> DTHDT DTHCAUS DTHCAT LDDTHELD LDDTHGR1 LSTALVDT DTHADY
#> 1 2022-03-06 ADVERSE EVENT ADVERSE EVENT 22 <=30 2022-03-06 1106
#> 2 <NA> <NA> <NA> NA <NA> 2022-03-17 NA
#> 3 <NA> <NA> <NA> NA <NA> 2022-03-11 NA
#> 4 <NA> <NA> <NA> NA <NA> 2022-03-26 NA
#> 5 <NA> <NA> <NA> NA <NA> 2022-03-15 NA
#> 6 2022-02-22 ADVERSE EVENT ADVERSE EVENT 3 <=30 2022-02-22 1085
#> ADTHAUT study_duration_secs
#> 1 Yes 63113904
#> 2 <NA> 63113904
#> 3 <NA> 63113904
#> 4 <NA> 63113904
#> 5 <NA> 63113904
#> 6 Yes 63113904
#>
#> ...
#> # A tibble: 6 × 56
#> STUDYID USUBJID SUBJID SITEID AGE AGEU SEX RACE ETHNIC COUNTRY DTHFL
#> <chr> <chr> <chr> <chr> <int> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 AB12345 AB12345-CH… id-11 CHN-9 28 YEARS F NATI… HISPA… CHN Y
#> 2 AB12345 AB12345-CH… id-352 CHN-16 28 YEARS M ASIAN UNKNO… CHN N
#> 3 AB12345 AB12345-CH… id-186 CHN-1 27 YEARS M ASIAN NOT H… CHN N
#> 4 AB12345 AB12345-CH… id-371 CHN-1 28 YEARS F ASIAN NOT H… CHN N
#> 5 AB12345 AB12345-CH… id-233 CHN-1 36 YEARS F BLAC… NOT H… CHN Y
#> 6 AB12345 AB12345-US… id-131 USA-12 44 YEARS F AMER… NOT H… USA N
#> # … with 45 more variables: INVID <chr>, INVNAM <chr>, ARM <fct>, ARMCD <fct>,
#> # ACTARM <fct>, ACTARMCD <fct>, TRT01P <fct>, TRT01A <fct>, TRT02P <fct>,
#> # TRT02A <fct>, REGION1 <fct>, STRATA1 <fct>, STRATA2 <fct>, BMRKR1 <dbl>,
#> # BMRKR2 <fct>, ITTFL <fct>, SAFFL <fct>, BMEASIFL <fct>, BEP01FL <fct>,
#> # AEWITHFL <fct>, RANDDT <date>, TRTSDTM <dttm>, TRTEDTM <dttm>,
#> # TRT01SDTM <dttm>, TRT01EDTM <dttm>, TRT02SDTM <dttm>, TRT02EDTM <dttm>,
#> # AP01SDTM <dttm>, AP01EDTM <dttm>, AP02SDTM <dttm>, AP02EDTM <dttm>, …
#>
#> $ADAE
#> A CDISCTealDataset object containing the following data.frame (1934 rows and 92 columns):
#> STUDYID USUBJID SUBJID SITEID AGE AGEU SEX RACE
#> 1 AB12345 AB12345-BRA-1-id-134 id-134 BRA-1 47 YEARS M WHITE
#> 2 AB12345 AB12345-BRA-1-id-134 id-134 BRA-1 47 YEARS M WHITE
#> 3 AB12345 AB12345-BRA-1-id-134 id-134 BRA-1 47 YEARS M WHITE
#> 4 AB12345 AB12345-BRA-1-id-134 id-134 BRA-1 47 YEARS M WHITE
#> 5 AB12345 AB12345-BRA-1-id-141 id-141 BRA-1 35 YEARS F WHITE
#> 6 AB12345 AB12345-BRA-1-id-141 id-141 BRA-1 35 YEARS F WHITE
#> ETHNIC COUNTRY DTHFL INVID INVNAM
#> 1 NOT HISPANIC OR LATINO BRA Y INV ID BRA-1 Dr. BRA-1 Doe
#> 2 NOT HISPANIC OR LATINO BRA Y INV ID BRA-1 Dr. BRA-1 Doe
#> 3 NOT HISPANIC OR LATINO BRA Y INV ID BRA-1 Dr. BRA-1 Doe
#> 4 NOT HISPANIC OR LATINO BRA Y INV ID BRA-1 Dr. BRA-1 Doe
#> 5 NOT HISPANIC OR LATINO BRA N INV ID BRA-1 Dr. BRA-1 Doe
#> 6 NOT HISPANIC OR LATINO BRA N INV ID BRA-1 Dr. BRA-1 Doe
#> ARM ARMCD ACTARM ACTARMCD TRT01P TRT01A
#> 1 A: Drug X ARM A A: Drug X ARM A A: Drug X A: Drug X
#> 2 A: Drug X ARM A A: Drug X ARM A A: Drug X A: Drug X
#> 3 A: Drug X ARM A A: Drug X ARM A A: Drug X A: Drug X
#> 4 A: Drug X ARM A A: Drug X ARM A A: Drug X A: Drug X
#> 5 C: Combination ARM C C: Combination ARM C C: Combination C: Combination
#> 6 C: Combination ARM C C: Combination ARM C C: Combination C: Combination
#> TRT02P TRT02A REGION1 STRATA1 STRATA2 BMRKR1 BMRKR2
#> 1 B: Placebo A: Drug X South America B S2 6.462991 LOW
#> 2 B: Placebo A: Drug X South America B S2 6.462991 LOW
#> 3 B: Placebo A: Drug X South America B S2 6.462991 LOW
#> 4 B: Placebo A: Drug X South America B S2 6.462991 LOW
#> 5 C: Combination C: Combination South America B S1 7.516076 HIGH
#> 6 C: Combination C: Combination South America B S1 7.516076 HIGH
#> ITTFL SAFFL BMEASIFL BEP01FL AEWITHFL RANDDT TRTSDTM
#> 1 Y Y Y N N 2020-11-03 2020-11-04 03:50:33
#> 2 Y Y Y N N 2020-11-03 2020-11-04 03:50:33
#> 3 Y Y Y N N 2020-11-03 2020-11-04 03:50:33
#> 4 Y Y Y N N 2020-11-03 2020-11-04 03:50:33
#> 5 Y Y Y Y N 2020-07-22 2020-07-25 14:10:56
#> 6 Y Y Y Y N 2020-07-22 2020-07-25 14:10:56
#> TRTEDTM TRT01SDTM TRT01EDTM
#> 1 2022-02-20 03:01:31 2020-11-04 03:50:33 2021-02-19 21:12:19
#> 2 2022-02-20 03:01:31 2020-11-04 03:50:33 2021-02-19 21:12:19
#> 3 2022-02-20 03:01:31 2020-11-04 03:50:33 2021-02-19 21:12:19
#> 4 2022-02-20 03:01:31 2020-11-04 03:50:33 2021-02-19 21:12:19
#> 5 2023-07-26 07:38:32 2020-07-25 14:10:56 2022-07-26 01:49:20
#> 6 2023-07-26 07:38:32 2020-07-25 14:10:56 2022-07-26 01:49:20
#> TRT02SDTM TRT02EDTM AP01SDTM
#> 1 2021-02-19 21:12:19 2022-02-20 03:01:31 2020-11-04 03:50:33
#> 2 2021-02-19 21:12:19 2022-02-20 03:01:31 2020-11-04 03:50:33
#> 3 2021-02-19 21:12:19 2022-02-20 03:01:31 2020-11-04 03:50:33
#> 4 2021-02-19 21:12:19 2022-02-20 03:01:31 2020-11-04 03:50:33
#> 5 2022-07-26 01:49:20 2023-07-26 07:38:32 2020-07-25 14:10:56
#> 6 2022-07-26 01:49:20 2023-07-26 07:38:32 2020-07-25 14:10:56
#> AP01EDTM AP02SDTM AP02EDTM EOSSTT
#> 1 2021-02-19 21:12:19 2021-02-19 21:12:19 2022-02-20 03:01:31 DISCONTINUED
#> 2 2021-02-19 21:12:19 2021-02-19 21:12:19 2022-02-20 03:01:31 DISCONTINUED
#> 3 2021-02-19 21:12:19 2021-02-19 21:12:19 2022-02-20 03:01:31 DISCONTINUED
#> 4 2021-02-19 21:12:19 2021-02-19 21:12:19 2022-02-20 03:01:31 DISCONTINUED
#> 5 2022-07-26 01:49:20 2022-07-26 01:49:20 2023-07-26 07:38:32 COMPLETED
#> 6 2022-07-26 01:49:20 2022-07-26 01:49:20 2023-07-26 07:38:32 COMPLETED
#> EOTSTT EOSDT EOSDY DCSREAS DTHDT DTHCAUS DTHCAT
#> 1 DISCONTINUED 2022-02-20 473 DEATH 2022-03-16 ADVERSE EVENT ADVERSE EVENT
#> 2 DISCONTINUED 2022-02-20 473 DEATH 2022-03-16 ADVERSE EVENT ADVERSE EVENT
#> 3 DISCONTINUED 2022-02-20 473 DEATH 2022-03-16 ADVERSE EVENT ADVERSE EVENT
#> 4 DISCONTINUED 2022-02-20 473 DEATH 2022-03-16 ADVERSE EVENT ADVERSE EVENT
#> 5 COMPLETED 2023-07-26 1096 <NA> <NA> <NA> <NA>
#> 6 COMPLETED 2023-07-26 1096 <NA> <NA> <NA> <NA>
#> LDDTHELD LDDTHGR1 LSTALVDT DTHADY ADTHAUT study_duration_secs ASEQ AESEQ
#> 1 24 <=30 2022-03-16 497 Yes 63113904 1 1
#> 2 24 <=30 2022-03-16 497 Yes 63113904 2 2
#> 3 24 <=30 2022-03-16 497 Yes 63113904 3 3
#> 4 24 <=30 2022-03-16 497 Yes 63113904 4 4
#> 5 NA <NA> 2023-08-08 NA <NA> 63113904 1 1
#> 6 NA <NA> 2023-08-08 NA <NA> 63113904 2 2
#> AETERM AELLT AEDECOD AEHLT AEHLGT AEBODSYS
#> 1 trm B.2.1.2.1 llt B.2.1.2.1 dcd B.2.1.2.1 hlt B.2.1.2 hlgt B.2.1 cl B.2
#> 2 trm D.1.1.4.2 llt D.1.1.4.2 dcd D.1.1.4.2 hlt D.1.1.4 hlgt D.1.1 cl D.1
#> 3 trm A.1.1.1.2 llt A.1.1.1.2 dcd A.1.1.1.2 hlt A.1.1.1 hlgt A.1.1 cl A.1
#> 4 trm A.1.1.1.2 llt A.1.1.1.2 dcd A.1.1.1.2 hlt A.1.1.1 hlgt A.1.1 cl A.1
#> 5 trm B.2.1.2.1 llt B.2.1.2.1 dcd B.2.1.2.1 hlt B.2.1.2 hlgt B.2.1 cl B.2
#> 6 trm D.2.1.5.3 llt D.2.1.5.3 dcd D.2.1.5.3 hlt D.2.1.5 hlgt D.2.1 cl D.2
#> AESOC AESEV AESER AEACN AEREL AEOUT AESDTH
#> 1 cl B MODERATE N DOSE NOT CHANGED N RECOVERING/RESOLVING N
#> 2 cl D MODERATE N DOSE NOT CHANGED N RECOVERING/RESOLVING N
#> 3 cl A MODERATE Y DOSE NOT CHANGED N RECOVERING/RESOLVING N
#> 4 cl A MODERATE Y DRUG WITHDRAWN N RECOVERING/RESOLVING N
#> 5 cl B MODERATE N DOSE INCREASED N RECOVERED/RESOLVED N
#> 6 cl D MILD N DOSE INCREASED Y RECOVERED/RESOLVED N
#> AESCONG AESDISAB AESHOSP AESLIFE AESMIE TRTEMFL AECONTRT ASTDTM
#> 1 N Y N N N Y Y 2021-04-15
#> 2 N Y N N N Y N 2021-05-20
#> 3 N Y N N N Y Y 2021-09-22
#> 4 Y N N N N Y N 2021-12-03
#> 5 N N Y N N Y Y 2021-08-17
#> 6 N Y N N N Y N 2021-10-23
#> AENDTM ASTDY AENDY LDOSEDTM AETOXGR SMQ01NAM SMQ02NAM SMQ01SC
#> 1 2021-10-05 162 335 2020-11-07 08:42:06 3 <NA> <NA> <NA>
#> 2 2021-11-01 197 362 2021-05-18 03:00:40 3 <NA> <NA> <NA>
#> 3 2022-02-17 322 470 2021-05-24 03:31:30 2 <NA> <NA> <NA>
#> 4 2022-01-14 394 436 2021-03-19 15:29:44 2 <NA> <NA> <NA>
#> 5 2021-08-27 388 398 2021-07-22 14:59:07 3 <NA> <NA> <NA>
#> 6 2023-07-13 455 1083 2021-07-16 10:08:53 1 <NA> <NA> <NA>
#> SMQ02SC CQ01NAM ANL01FL AERELNST
#> 1 <NA> <NA> Y NONE
#> 2 <NA> <NA> Y CONCURRENT ILLNESS
#> 3 <NA> <NA> Y CONCURRENT ILLNESS
#> 4 <NA> <NA> Y CONCURRENT ILLNESS
#> 5 <NA> <NA> Y CONCURRENT ILLNESS
#> 6 <NA> D.2.1.5.3/A.1.1.1.1 AESI Y CONCURRENT ILLNESS
#> AEACNOTH
#> 1 PROCEDURE/SURGERY
#> 2 MEDICATION
#> 3 SUBJECT DISCONTINUED FROM STUDY
#> 4 SUBJECT DISCONTINUED FROM STUDY
#> 5 PROCEDURE/SURGERY
#> 6 MEDICATION
#>
#> ...
#> # A tibble: 6 × 92
#> STUDYID USUBJID SUBJID SITEID AGE AGEU SEX RACE ETHNIC COUNTRY DTHFL
#> <chr> <chr> <chr> <chr> <int> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 AB12345 AB12345-US… id-206 USA-8 34 YEARS F ASIAN NOT H… USA N
#> 2 AB12345 AB12345-US… id-206 USA-8 34 YEARS F ASIAN NOT H… USA N
#> 3 AB12345 AB12345-US… id-206 USA-8 34 YEARS F ASIAN NOT H… USA N
#> 4 AB12345 AB12345-US… id-130 USA-9 40 YEARS M ASIAN NOT H… USA N
#> 5 AB12345 AB12345-US… id-130 USA-9 40 YEARS M ASIAN NOT H… USA N
#> 6 AB12345 AB12345-US… id-130 USA-9 40 YEARS M ASIAN NOT H… USA N
#> # … with 81 more variables: INVID <chr>, INVNAM <chr>, ARM <fct>, ARMCD <fct>,
#> # ACTARM <fct>, ACTARMCD <fct>, TRT01P <fct>, TRT01A <fct>, TRT02P <fct>,
#> # TRT02A <fct>, REGION1 <fct>, STRATA1 <fct>, STRATA2 <fct>, BMRKR1 <dbl>,
#> # BMRKR2 <fct>, ITTFL <fct>, SAFFL <fct>, BMEASIFL <fct>, BEP01FL <fct>,
#> # AEWITHFL <fct>, RANDDT <date>, TRTSDTM <dttm>, TRTEDTM <dttm>,
#> # TRT01SDTM <dttm>, TRT01EDTM <dttm>, TRT02SDTM <dttm>, TRT02EDTM <dttm>,
#> # AP01SDTM <dttm>, AP01EDTM <dttm>, AP02SDTM <dttm>, AP02EDTM <dttm>, …
#>
# TealDataConnector --------
adsl_cf <- callable_function(function() synthetic_cdisc_data("latest")$adsl)
adsl <- cdisc_dataset_connector(
dataname = "ADSL",
pull_callable = adsl_cf,
keys = get_cdisc_keys("ADSL")
)
adlb_cf <- callable_function(function() synthetic_cdisc_data("latest")$adlb)
adlb <- cdisc_dataset_connector(
dataname = "ADLB",
pull_callable = adlb_cf,
keys = get_cdisc_keys("ADLB")
)
rdc <- relational_data_connector(
connection = data_connection(),
connectors = list(adsl, adlb)
)
rdc$set_ui(function(id, connection, connectors) p("Example UI"))
rdc$set_server(
function(id, connection, connectors) {
moduleServer(
id = id,
module = function(input, output, session) {
# Note this is simplified as we are not opening a real connection here
for (connector in connectors) {
set_args(connector, args = list(name = input$name))
# pull each dataset
connector$get_server()(id = connector$get_dataname())
if (connector$is_failed()) {
break
}
}
}
)
}
)
if (FALSE) {
load_datasets(rdc)
get_datasets(rdc)
}
# TealData --------
if (FALSE) {
drc <- cdisc_data(rdc, adae)
get_datasets(drc)
}
# TealDatasetConnector --------
library(scda)
adsl_cf <- callable_function(
function() {
synthetic_cdisc_data("latest")$adsl
}
)
rdc <- cdisc_dataset_connector("ADSL", adsl_cf, keys = get_cdisc_keys("ADSL"))
if (FALSE) {
load_datasets(rdc)
get_datasets(rdc)
}
# TealDataset --------
library(scda)
adsl <- cdisc_dataset(
dataname = "ADSL",
x = synthetic_cdisc_data("latest")$adsl,
code = "library(scda)\nADSL <- synthetic_cdisc_data(\"latest\")$adsl"
)
get_datasets(adsl)
#> A CDISCTealDataset object containing the following data.frame (400 rows and 56 columns):
#> STUDYID USUBJID SUBJID SITEID AGE AGEU SEX
#> 1 AB12345 AB12345-CHN-3-id-128 id-128 CHN-3 32 YEARS M
#> 2 AB12345 AB12345-CHN-15-id-262 id-262 CHN-15 35 YEARS M
#> 3 AB12345 AB12345-RUS-3-id-378 id-378 RUS-3 30 YEARS F
#> 4 AB12345 AB12345-CHN-11-id-220 id-220 CHN-11 26 YEARS F
#> 5 AB12345 AB12345-CHN-7-id-267 id-267 CHN-7 40 YEARS M
#> 6 AB12345 AB12345-CHN-15-id-201 id-201 CHN-15 49 YEARS M
#> RACE ETHNIC COUNTRY DTHFL INVID
#> 1 ASIAN HISPANIC OR LATINO CHN Y INV ID CHN-3
#> 2 BLACK OR AFRICAN AMERICAN NOT HISPANIC OR LATINO CHN N INV ID CHN-15
#> 3 ASIAN NOT HISPANIC OR LATINO RUS N INV ID RUS-3
#> 4 ASIAN NOT HISPANIC OR LATINO CHN N INV ID CHN-11
#> 5 ASIAN NOT HISPANIC OR LATINO CHN N INV ID CHN-7
#> 6 ASIAN NOT HISPANIC OR LATINO CHN Y INV ID CHN-15
#> INVNAM ARM ARMCD ACTARM ACTARMCD TRT01P
#> 1 Dr. CHN-3 Doe A: Drug X ARM A A: Drug X ARM A A: Drug X
#> 2 Dr. CHN-15 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> 3 Dr. RUS-3 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> 4 Dr. CHN-11 Doe B: Placebo ARM B B: Placebo ARM B B: Placebo
#> 5 Dr. CHN-7 Doe B: Placebo ARM B B: Placebo ARM B B: Placebo
#> 6 Dr. CHN-15 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> TRT01A TRT02P TRT02A REGION1 STRATA1 STRATA2
#> 1 A: Drug X B: Placebo A: Drug X Asia C S2
#> 2 C: Combination B: Placebo C: Combination Asia C S1
#> 3 C: Combination A: Drug X B: Placebo Eurasia A S1
#> 4 B: Placebo B: Placebo B: Placebo Asia B S2
#> 5 B: Placebo C: Combination A: Drug X Asia C S1
#> 6 C: Combination B: Placebo C: Combination Asia C S2
#> BMRKR1 BMRKR2 ITTFL SAFFL BMEASIFL BEP01FL AEWITHFL RANDDT
#> 1 14.424934 MEDIUM Y Y Y Y N 2019-02-22
#> 2 4.055463 LOW Y Y N N Y 2019-02-26
#> 3 2.803240 HIGH Y Y Y N N 2019-02-24
#> 4 10.262734 MEDIUM Y Y Y Y N 2019-02-27
#> 5 6.206763 LOW Y Y N N N 2019-03-01
#> 6 6.906799 MEDIUM Y Y Y N N 2019-03-05
#> TRTSDTM TRTEDTM TRT01SDTM
#> 1 2019-02-24 11:09:18 2022-02-12 03:55:58 2019-02-24 11:09:18
#> 2 2019-02-26 09:05:00 2022-02-26 02:32:36 2019-02-26 09:05:00
#> 3 2019-02-28 03:19:08 2022-02-27 20:46:44 2019-02-28 03:19:08
#> 4 2019-03-01 13:33:03 2022-03-01 07:00:39 2019-03-01 13:33:03
#> 5 2019-03-02 00:09:16 2022-03-01 17:36:52 2019-03-02 00:09:16
#> 6 2019-03-05 15:23:44 2022-02-19 03:34:25 2019-03-05 15:23:44
#> TRT01EDTM TRT02SDTM TRT02EDTM
#> 1 2021-02-11 22:06:46 2021-02-11 22:06:46 2022-02-12 03:55:58
#> 2 2021-02-25 20:43:24 2021-02-25 20:43:24 2022-02-26 02:32:36
#> 3 2021-02-27 14:57:32 2021-02-27 14:57:32 2022-02-27 20:46:44
#> 4 2021-03-01 01:11:27 2021-03-01 01:11:27 2022-03-01 07:00:39
#> 5 2021-03-01 11:47:40 2021-03-01 11:47:40 2022-03-01 17:36:52
#> 6 2021-02-18 21:45:13 2021-02-18 21:45:13 2022-02-19 03:34:25
#> AP01SDTM AP01EDTM AP02SDTM
#> 1 2019-02-24 11:09:18 2021-02-11 22:06:46 2021-02-11 22:06:46
#> 2 2019-02-26 09:05:00 2021-02-25 20:43:24 2021-02-25 20:43:24
#> 3 2019-02-28 03:19:08 2021-02-27 14:57:32 2021-02-27 14:57:32
#> 4 2019-03-01 13:33:03 2021-03-01 01:11:27 2021-03-01 01:11:27
#> 5 2019-03-02 00:09:16 2021-03-01 11:47:40 2021-03-01 11:47:40
#> 6 2019-03-05 15:23:44 2021-02-18 21:45:13 2021-02-18 21:45:13
#> AP02EDTM EOSSTT EOTSTT EOSDT EOSDY DCSREAS
#> 1 2022-02-12 03:55:58 DISCONTINUED DISCONTINUED 2022-02-12 1084 DEATH
#> 2 2022-02-26 02:32:36 COMPLETED COMPLETED 2022-02-26 1096 <NA>
#> 3 2022-02-27 20:46:44 COMPLETED COMPLETED 2022-02-27 1096 <NA>
#> 4 2022-03-01 07:00:39 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 5 2022-03-01 17:36:52 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 6 2022-02-19 03:34:25 DISCONTINUED DISCONTINUED 2022-02-19 1082 DEATH
#> DTHDT DTHCAUS DTHCAT LDDTHELD LDDTHGR1 LSTALVDT DTHADY
#> 1 2022-03-06 ADVERSE EVENT ADVERSE EVENT 22 <=30 2022-03-06 1106
#> 2 <NA> <NA> <NA> NA <NA> 2022-03-17 NA
#> 3 <NA> <NA> <NA> NA <NA> 2022-03-11 NA
#> 4 <NA> <NA> <NA> NA <NA> 2022-03-26 NA
#> 5 <NA> <NA> <NA> NA <NA> 2022-03-15 NA
#> 6 2022-02-22 ADVERSE EVENT ADVERSE EVENT 3 <=30 2022-02-22 1085
#> ADTHAUT study_duration_secs
#> 1 Yes 63113904
#> 2 <NA> 63113904
#> 3 <NA> 63113904
#> 4 <NA> 63113904
#> 5 <NA> 63113904
#> 6 Yes 63113904
#>
#> ...
#> # A tibble: 6 × 56
#> STUDYID USUBJID SUBJID SITEID AGE AGEU SEX RACE ETHNIC COUNTRY DTHFL
#> <chr> <chr> <chr> <chr> <int> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 AB12345 AB12345-CH… id-11 CHN-9 28 YEARS F NATI… HISPA… CHN Y
#> 2 AB12345 AB12345-CH… id-352 CHN-16 28 YEARS M ASIAN UNKNO… CHN N
#> 3 AB12345 AB12345-CH… id-186 CHN-1 27 YEARS M ASIAN NOT H… CHN N
#> 4 AB12345 AB12345-CH… id-371 CHN-1 28 YEARS F ASIAN NOT H… CHN N
#> 5 AB12345 AB12345-CH… id-233 CHN-1 36 YEARS F BLAC… NOT H… CHN Y
#> 6 AB12345 AB12345-US… id-131 USA-12 44 YEARS F AMER… NOT H… USA N
#> # … with 45 more variables: INVID <chr>, INVNAM <chr>, ARM <fct>, ARMCD <fct>,
#> # ACTARM <fct>, ACTARMCD <fct>, TRT01P <fct>, TRT01A <fct>, TRT02P <fct>,
#> # TRT02A <fct>, REGION1 <fct>, STRATA1 <fct>, STRATA2 <fct>, BMRKR1 <dbl>,
#> # BMRKR2 <fct>, ITTFL <fct>, SAFFL <fct>, BMEASIFL <fct>, BEP01FL <fct>,
#> # AEWITHFL <fct>, RANDDT <date>, TRTSDTM <dttm>, TRTEDTM <dttm>,
#> # TRT01SDTM <dttm>, TRT01EDTM <dttm>, TRT02SDTM <dttm>, TRT02EDTM <dttm>,
#> # AP01SDTM <dttm>, AP01EDTM <dttm>, AP02SDTM <dttm>, AP02EDTM <dttm>, …