![[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
library(magrittr)
# TealData --------
adsl <- cdisc_dataset(
dataname = "ADSL",
x = teal.data::example_cdisc_data("ADSL"), ,
code = "library(teal.data)\nADSL <- teal.data::example_cdisc_data(\"ADSL\")"
)
adae <- cdisc_dataset(
dataname = "ADAE",
x = teal.data::example_cdisc_data("ADAE"),
code = "library(teal.data)\nADAE <- teal.data::example_cdisc_data(\"ADAE\")"
)
rd <- cdisc_data(adsl, adae)
get_datasets(rd)
#> $ADSL
#> A CDISCTealDataset object containing the following data.frame (400 rows and 55 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 11:09:25 2022-02-12 04:28:08 2019-02-24 11:09:25
#> 2 2019-02-26 09:05:10 2022-02-26 03:05:10 2019-02-26 09:05:10
#> 3 2019-02-28 03:19:22 2022-02-27 21:19:22 2019-02-28 03:19:22
#> 4 2019-03-01 13:33:19 2022-03-01 07:33:19 2019-03-01 13:33:19
#> 5 2019-03-02 00:09:33 2022-03-01 18:09:33 2019-03-02 00:09:33
#> 6 2019-03-05 15:24:07 2022-02-19 04:06:48 2019-03-05 15:24:07
#> TRT01EDTM TRT02SDTM TRT02EDTM
#> 1 2021-02-11 22:28:08 2021-02-11 22:28:08 2022-02-12 04:28:08
#> 2 2021-02-25 21:05:10 2021-02-25 21:05:10 2022-02-26 03:05:10
#> 3 2021-02-27 15:19:22 2021-02-27 15:19:22 2022-02-27 21:19:22
#> 4 2021-03-01 01:33:19 2021-03-01 01:33:19 2022-03-01 07:33:19
#> 5 2021-03-01 12:09:33 2021-03-01 12:09:33 2022-03-01 18:09:33
#> 6 2021-02-18 22:06:48 2021-02-18 22:06:48 2022-02-19 04:06:48
#> AP01SDTM AP01EDTM AP02SDTM
#> 1 2019-02-24 11:09:25 2021-02-11 22:28:08 2021-02-11 22:28:08
#> 2 2019-02-26 09:05:10 2021-02-25 21:05:10 2021-02-25 21:05:10
#> 3 2019-02-28 03:19:22 2021-02-27 15:19:22 2021-02-27 15:19:22
#> 4 2019-03-01 13:33:19 2021-03-01 01:33:19 2021-03-01 01:33:19
#> 5 2019-03-02 00:09:33 2021-03-01 12:09:33 2021-03-01 12:09:33
#> 6 2019-03-05 15:24:07 2021-02-18 22:06:48 2021-02-18 22:06:48
#> AP02EDTM EOSSTT EOTSTT EOSDT EOSDY DCSREAS
#> 1 2022-02-12 04:28:08 DISCONTINUED DISCONTINUED 2022-02-12 1084 DEATH
#> 2 2022-02-26 03:05:10 COMPLETED COMPLETED 2022-02-26 1096 <NA>
#> 3 2022-02-27 21:19:22 COMPLETED COMPLETED 2022-02-27 1096 <NA>
#> 4 2022-03-01 07:33:19 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 5 2022-03-01 18:09:33 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 6 2022-02-19 04:06:48 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 1105
#> 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 1084
#> ADTHAUT
#> 1 Yes
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 Yes
#> ...
#>
#> $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 04:08:58
#> 2 Y Y Y N N 2020-11-03 2020-11-04 04:08:58
#> 3 Y Y Y N N 2020-11-03 2020-11-04 04:08:58
#> 4 Y Y Y N N 2020-11-03 2020-11-04 04:08:58
#> 5 Y Y Y Y N 2020-07-22 2020-07-25 14:26:21
#> 6 Y Y Y Y N 2020-07-22 2020-07-25 14:26:21
#> TRTEDTM TRT01SDTM TRT01EDTM
#> 1 2022-02-20 03:33:55 2020-11-04 04:08:58 2021-02-19 21:33:55
#> 2 2022-02-20 03:33:55 2020-11-04 04:08:58 2021-02-19 21:33:55
#> 3 2022-02-20 03:33:55 2020-11-04 04:08:58 2021-02-19 21:33:55
#> 4 2022-02-20 03:33:55 2020-11-04 04:08:58 2021-02-19 21:33:55
#> 5 2023-07-26 08:26:21 2020-07-25 14:26:21 2022-07-26 02:26:21
#> 6 2023-07-26 08:26:21 2020-07-25 14:26:21 2022-07-26 02:26:21
#> TRT02SDTM TRT02EDTM AP01SDTM
#> 1 2021-02-19 21:33:55 2022-02-20 03:33:55 2020-11-04 04:08:58
#> 2 2021-02-19 21:33:55 2022-02-20 03:33:55 2020-11-04 04:08:58
#> 3 2021-02-19 21:33:55 2022-02-20 03:33:55 2020-11-04 04:08:58
#> 4 2021-02-19 21:33:55 2022-02-20 03:33:55 2020-11-04 04:08:58
#> 5 2022-07-26 02:26:21 2023-07-26 08:26:21 2020-07-25 14:26:21
#> 6 2022-07-26 02:26:21 2023-07-26 08:26:21 2020-07-25 14:26:21
#> AP01EDTM AP02SDTM AP02EDTM EOSSTT
#> 1 2021-02-19 21:33:55 2021-02-19 21:33:55 2022-02-20 03:33:55 DISCONTINUED
#> 2 2021-02-19 21:33:55 2021-02-19 21:33:55 2022-02-20 03:33:55 DISCONTINUED
#> 3 2021-02-19 21:33:55 2021-02-19 21:33:55 2022-02-20 03:33:55 DISCONTINUED
#> 4 2021-02-19 21:33:55 2021-02-19 21:33:55 2022-02-20 03:33:55 DISCONTINUED
#> 5 2022-07-26 02:26:21 2022-07-26 02:26:21 2023-07-26 08:26:21 COMPLETED
#> 6 2022-07-26 02:26:21 2022-07-26 02:26:21 2023-07-26 08:26:21 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 ASEQ AESEQ AETERM
#> 1 24 <=30 2022-03-16 496 Yes 1 1 trm B.2.1.2.1
#> 2 24 <=30 2022-03-16 496 Yes 2 2 trm D.1.1.4.2
#> 3 24 <=30 2022-03-16 496 Yes 3 3 trm A.1.1.1.2
#> 4 24 <=30 2022-03-16 496 Yes 4 4 trm A.1.1.1.2
#> 5 NA <NA> 2023-08-08 NA <NA> 1 1 trm B.2.1.2.1
#> 6 NA <NA> 2023-08-08 NA <NA> 2 2 trm D.2.1.5.3
#> AELLT AEDECOD AEHLT AEHLGT AEBODSYS AESOC AESEV
#> 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 cl B MODERATE
#> 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 cl D MODERATE
#> 3 llt A.1.1.1.2 dcd A.1.1.1.2 hlt A.1.1.1 hlgt A.1.1 cl A.1 cl A MODERATE
#> 4 llt A.1.1.1.2 dcd A.1.1.1.2 hlt A.1.1.1 hlgt A.1.1 cl A.1 cl A MODERATE
#> 5 llt B.2.1.2.1 dcd B.2.1.2.1 hlt B.2.1.2 hlgt B.2.1 cl B.2 cl B MODERATE
#> 6 llt D.2.1.5.3 dcd D.2.1.5.3 hlt D.2.1.5 hlgt D.2.1 cl D.2 cl D MILD
#> AESER AEACN AEREL AEOUT AESDTH AESCONG AESDISAB
#> 1 N DOSE NOT CHANGED N RECOVERING/RESOLVING N N Y
#> 2 N DOSE NOT CHANGED N RECOVERING/RESOLVING N N Y
#> 3 Y DOSE NOT CHANGED N RECOVERING/RESOLVING N N Y
#> 4 Y DRUG WITHDRAWN N RECOVERING/RESOLVING N Y N
#> 5 N DOSE INCREASED N RECOVERED/RESOLVED N N N
#> 6 N DOSE INCREASED Y RECOVERED/RESOLVED N N Y
#> AESHOSP AESLIFE AESMIE TRTEMFL AECONTRT ASTDTM
#> 1 N N N Y Y 2021-04-15 04:08:58
#> 2 N N N Y N 2021-05-19 04:08:58
#> 3 N N N Y Y 2021-09-21 04:08:58
#> 4 N N N Y N 2021-12-02 04:08:58
#> 5 Y N N Y Y 2021-08-16 14:26:21
#> 6 N N N Y N 2021-10-23 14:26:21
#> AENDTM ASTDY AENDY LDOSEDTM LDRELTM AETOXGR
#> 1 2021-10-04 04:08:58 162 334 2020-11-07 09:05:04 228663.896 3
#> 2 2021-10-31 04:08:58 196 361 2021-05-17 07:21:09 2687.814 3
#> 3 2022-02-16 04:08:58 321 469 2021-05-23 15:14:37 173574.345 2
#> 4 2022-01-13 04:08:58 393 435 2021-03-19 08:52:05 371236.878 2
#> 5 2021-08-26 14:26:21 387 397 2021-07-22 06:04:04 36502.291 3
#> 6 2023-07-12 14:26:21 455 1082 2021-07-16 21:30:39 142135.710 1
#> SMQ01NAM SMQ02NAM SMQ01SC SMQ02SC CQ01NAM ANL01FL
#> 1 <NA> <NA> <NA> <NA> <NA> Y
#> 2 <NA> <NA> <NA> <NA> <NA> Y
#> 3 <NA> <NA> <NA> <NA> <NA> Y
#> 4 <NA> <NA> <NA> <NA> <NA> Y
#> 5 <NA> <NA> <NA> <NA> <NA> Y
#> 6 <NA> <NA> <NA> <NA> D.2.1.5.3/A.1.1.1.1 AESI Y
#> AERELNST AEACNOTH
#> 1 NONE PROCEDURE/SURGERY
#> 2 CONCURRENT ILLNESS MEDICATION
#> 3 CONCURRENT ILLNESS SUBJECT DISCONTINUED FROM STUDY
#> 4 CONCURRENT ILLNESS SUBJECT DISCONTINUED FROM STUDY
#> 5 CONCURRENT ILLNESS PROCEDURE/SURGERY
#> 6 CONCURRENT ILLNESS MEDICATION
#> ...
#>
# TealDataConnector --------
random_data_connector <- function(dataname) {
fun_dataset_connector(
dataname = dataname,
fun = teal.data::example_cdisc_data,
fun_args = list(dataname = dataname),
)
}
adsl_cf <- callable_function(teal.data::example_cdisc_data) %>%
set_args(list(dataname = "ADSL"))
adsl <- cdisc_dataset_connector(
dataname = "ADSL",
pull_callable = adsl_cf,
keys = get_cdisc_keys("ADSL")
)
adlb_cf <- callable_function(teal.data::example_cdisc_data) %>%
set_args(list(dataname = "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 --------
adsl_cf <- callable_function(teal.data::example_cdisc_data) %>%
set_args(list(dataname = "ADSL"))
rdc <- cdisc_dataset_connector("ADSL", adsl_cf, keys = get_cdisc_keys("ADSL"))
if (FALSE) {
load_datasets(rdc)
get_datasets(rdc)
}
# TealDataset --------
adsl <- cdisc_dataset(
dataname = "ADSL",
x = teal.data::example_cdisc_data("ADSL"),
code = "library(teal.data)\nADSL <- example_cdisc_data(\"ADSL\")"
)
get_datasets(adsl)
#> A CDISCTealDataset object containing the following data.frame (400 rows and 55 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 11:09:25 2022-02-12 04:28:08 2019-02-24 11:09:25
#> 2 2019-02-26 09:05:10 2022-02-26 03:05:10 2019-02-26 09:05:10
#> 3 2019-02-28 03:19:22 2022-02-27 21:19:22 2019-02-28 03:19:22
#> 4 2019-03-01 13:33:19 2022-03-01 07:33:19 2019-03-01 13:33:19
#> 5 2019-03-02 00:09:33 2022-03-01 18:09:33 2019-03-02 00:09:33
#> 6 2019-03-05 15:24:07 2022-02-19 04:06:48 2019-03-05 15:24:07
#> TRT01EDTM TRT02SDTM TRT02EDTM
#> 1 2021-02-11 22:28:08 2021-02-11 22:28:08 2022-02-12 04:28:08
#> 2 2021-02-25 21:05:10 2021-02-25 21:05:10 2022-02-26 03:05:10
#> 3 2021-02-27 15:19:22 2021-02-27 15:19:22 2022-02-27 21:19:22
#> 4 2021-03-01 01:33:19 2021-03-01 01:33:19 2022-03-01 07:33:19
#> 5 2021-03-01 12:09:33 2021-03-01 12:09:33 2022-03-01 18:09:33
#> 6 2021-02-18 22:06:48 2021-02-18 22:06:48 2022-02-19 04:06:48
#> AP01SDTM AP01EDTM AP02SDTM
#> 1 2019-02-24 11:09:25 2021-02-11 22:28:08 2021-02-11 22:28:08
#> 2 2019-02-26 09:05:10 2021-02-25 21:05:10 2021-02-25 21:05:10
#> 3 2019-02-28 03:19:22 2021-02-27 15:19:22 2021-02-27 15:19:22
#> 4 2019-03-01 13:33:19 2021-03-01 01:33:19 2021-03-01 01:33:19
#> 5 2019-03-02 00:09:33 2021-03-01 12:09:33 2021-03-01 12:09:33
#> 6 2019-03-05 15:24:07 2021-02-18 22:06:48 2021-02-18 22:06:48
#> AP02EDTM EOSSTT EOTSTT EOSDT EOSDY DCSREAS
#> 1 2022-02-12 04:28:08 DISCONTINUED DISCONTINUED 2022-02-12 1084 DEATH
#> 2 2022-02-26 03:05:10 COMPLETED COMPLETED 2022-02-26 1096 <NA>
#> 3 2022-02-27 21:19:22 COMPLETED COMPLETED 2022-02-27 1096 <NA>
#> 4 2022-03-01 07:33:19 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 5 2022-03-01 18:09:33 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 6 2022-02-19 04:06:48 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 1105
#> 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 1084
#> ADTHAUT
#> 1 Yes
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 Yes
#> ...