During the drug development process, clinical trials are often required to assess for the potential that the experimental drug can cause severe liver injury, known as a drug induced liver injury (DILI). There are multiple criteria that need to be evaluated to determine and classify a DILI “Event”. Hy’s Law, a common rule of thumb for a DILI Event , is usually comprised of three parts:
The examples of this vignette require the following packages.
library(admiral)
library(dplyr, warn.conflicts = FALSE)
CRITy
, CRITyFL
)LBTESTCD
and Joining by Potential EventsWe assume that an ADLB
dataset is available 1.
First we read in the ADLB parameters required for the Hy’s Law parameters:
data(admiral_adlb)
<- admiral_adlb %>%
adlb filter(PARAMCD %in% c("AST", "ALT", "BILI") & is.na(DTYPE))
STUDYID | DOMAIN | USUBJID | LBSEQ | LBTESTCD | LBTEST | LBCAT | LBORRES | LBORRESU | LBORNRLO | LBORNRHI | LBSTRESC | LBSTRESN | LBSTRESU | LBSTNRLO | LBSTNRHI | LBNRIND | LBBLFL | VISITNUM | VISIT | VISITDY | LBDTC | LBDY | TRTSDT | TRTEDT | TRT01A | TRT01P | ADT | ADY | PARAMCD | PARAM | PARAMN | PARCAT1 | AVAL | AVALC | ANRLO | ANRHI | DTYPE | AVISIT | AVISITN | ONTRTFL | ANRIND | BASETYPE | ABLFL | BASE | BASEC | BNRIND | CHG | PCHG | ATOXDSCL | ATOXDSCH | ATOXGRL | ATOXGRH | ATOXGR | BTOXGRL | BTOXGRH | BTOXGR | R2BASE | R2ANRLO | R2ANRHI | SHIFT1 | SHIFT2 | ANL01FL | LVOTFL | TRTP | TRTA | ASEQ | SUBJID | RFSTDTC | RFENDTC | RFXSTDTC | RFXENDTC | RFICDTC | RFPENDTC | DTHDTC | DTHFL | SITEID | AGE | AGEU | SEX | RACE | ETHNIC | ARMCD | ARM | ACTARMCD | ACTARM | COUNTRY | DMDTC | DMDY | TRTSDTM | TRTSTMF | TRTEDTM | TRTETMF | TRTDURD | SCRFDT | EOSDT | EOSSTT | FRVDT | RANDDT | DTHDT | DTHADY | LDDTHELD | LSTALVDT | AGEGR1 | SAFFL | RACEGR1 | REGION1 | LDDTHGR1 | DTH30FL | DTHA30FL | DTHB30FL |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CDISCPILOT01 | LB | 01-701-1015 | 3 | ALT | Alanine Aminotransferase | CHEMISTRY | 27 | U/L | 6 | 34 | 27 | 27 | U/L | 6 | 34 | NORMAL | Y | 1 | SCREENING 1 | -7 | 2013-12-26T14:45 | -7 | 2014-01-02 | 2014-07-02 | Placebo | Placebo | 2013-12-26 | -7 | ALT | Alanine Aminotransferase (U/L) | 3 | CHEMISTRY | 27 | 27 | 6 | 34 | NA | Baseline | 0 | NA | NORMAL | LAST | Y | 27 | 27 | NORMAL | 0 | 0.000000 | NA | Alanine aminotransferase increased | NA | 0 | 0 | NA | 0 | 0 | 1.0000000 | 4.500000 | 0.7941176 | NORMAL to NORMAL | 0 to 0 | NA | NA | Placebo | Placebo | 27 | 1015 | 2014-01-02 | 2014-07-02 | 2014-01-02 | 2014-07-02 | NA | 2014-07-02T11:45 | NA | NA | 701 | 63 | YEARS | F | WHITE | HISPANIC OR LATINO | Pbo | Placebo | Pbo | Placebo | USA | 2013-12-26 | -7 | 2014-01-02 | H | 2014-07-02 23:59:59 | H | 182 | NA | 2014-07-02 | COMPLETED | NA | 2014-01-02 | NA | NA | NA | 2014-07-02 | 18-64 | Y | White | NA | NA | NA | NA | NA |
CDISCPILOT01 | LB | 01-701-1015 | 41 | ALT | Alanine Aminotransferase | CHEMISTRY | 41 | U/L | 6 | 34 | 41 | 41 | U/L | 6 | 34 | HIGH | NA | 4 | WEEK 2 | 14 | 2014-01-16T13:17 | 15 | 2014-01-02 | 2014-07-02 | Placebo | Placebo | 2014-01-16 | 15 | ALT | Alanine Aminotransferase (U/L) | 3 | CHEMISTRY | 41 | 41 | 6 | 34 | NA | Week 2 | 4 | Y | HIGH | LAST | NA | 27 | 27 | NORMAL | 14 | 51.851852 | NA | Alanine aminotransferase increased | NA | 1 | 1 | NA | 0 | 0 | 1.5185185 | 6.833333 | 1.2058824 | NORMAL to HIGH | 0 to 1 | Y | NA | Placebo | Placebo | 28 | 1015 | 2014-01-02 | 2014-07-02 | 2014-01-02 | 2014-07-02 | NA | 2014-07-02T11:45 | NA | NA | 701 | 63 | YEARS | F | WHITE | HISPANIC OR LATINO | Pbo | Placebo | Pbo | Placebo | USA | 2013-12-26 | -7 | 2014-01-02 | H | 2014-07-02 23:59:59 | H | 182 | NA | 2014-07-02 | COMPLETED | NA | 2014-01-02 | NA | NA | NA | 2014-07-02 | 18-64 | Y | White | NA | NA | NA | NA | NA |
CDISCPILOT01 | LB | 01-701-1015 | 76 | ALT | Alanine Aminotransferase | CHEMISTRY | 18 | U/L | 6 | 34 | 18 | 18 | U/L | 6 | 34 | NORMAL | NA | 5 | WEEK 4 | 28 | 2014-01-30T08:50 | 29 | 2014-01-02 | 2014-07-02 | Placebo | Placebo | 2014-01-30 | 29 | ALT | Alanine Aminotransferase (U/L) | 3 | CHEMISTRY | 18 | 18 | 6 | 34 | NA | Week 4 | 5 | Y | NORMAL | LAST | NA | 27 | 27 | NORMAL | -9 | -33.333333 | NA | Alanine aminotransferase increased | NA | 0 | 0 | NA | 0 | 0 | 0.6666667 | 3.000000 | 0.5294118 | NORMAL to NORMAL | 0 to 0 | Y | NA | Placebo | Placebo | 30 | 1015 | 2014-01-02 | 2014-07-02 | 2014-01-02 | 2014-07-02 | NA | 2014-07-02T11:45 | NA | NA | 701 | 63 | YEARS | F | WHITE | HISPANIC OR LATINO | Pbo | Placebo | Pbo | Placebo | USA | 2013-12-26 | -7 | 2014-01-02 | H | 2014-07-02 23:59:59 | H | 182 | NA | 2014-07-02 | COMPLETED | NA | 2014-01-02 | NA | NA | NA | 2014-07-02 | 18-64 | Y | White | NA | NA | NA | NA | NA |
CDISCPILOT01 | LB | 01-701-1015 | 106 | ALT | Alanine Aminotransferase | CHEMISTRY | 26 | U/L | 6 | 34 | 26 | 26 | U/L | 6 | 34 | NORMAL | NA | 7 | WEEK 6 | 42 | 2014-02-12T12:56 | 42 | 2014-01-02 | 2014-07-02 | Placebo | Placebo | 2014-02-12 | 42 | ALT | Alanine Aminotransferase (U/L) | 3 | CHEMISTRY | 26 | 26 | 6 | 34 | NA | Week 6 | 7 | Y | NORMAL | LAST | NA | 27 | 27 | NORMAL | -1 | -3.703704 | NA | Alanine aminotransferase increased | NA | 0 | 0 | NA | 0 | 0 | 0.9629630 | 4.333333 | 0.7647059 | NORMAL to NORMAL | 0 to 0 | Y | NA | Placebo | Placebo | 31 | 1015 | 2014-01-02 | 2014-07-02 | 2014-01-02 | 2014-07-02 | NA | 2014-07-02T11:45 | NA | NA | 701 | 63 | YEARS | F | WHITE | HISPANIC OR LATINO | Pbo | Placebo | Pbo | Placebo | USA | 2013-12-26 | -7 | 2014-01-02 | H | 2014-07-02 23:59:59 | H | 182 | NA | 2014-07-02 | COMPLETED | NA | 2014-01-02 | NA | NA | NA | 2014-07-02 | 18-64 | Y | White | NA | NA | NA | NA | NA |
CDISCPILOT01 | LB | 01-701-1015 | 136 | ALT | Alanine Aminotransferase | CHEMISTRY | 22 | U/L | 6 | 34 | 22 | 22 | U/L | 6 | 34 | NORMAL | NA | 8 | WEEK 8 | 56 | 2014-03-05T12:25 | 63 | 2014-01-02 | 2014-07-02 | Placebo | Placebo | 2014-03-05 | 63 | ALT | Alanine Aminotransferase (U/L) | 3 | CHEMISTRY | 22 | 22 | 6 | 34 | NA | Week 8 | 8 | Y | NORMAL | LAST | NA | 27 | 27 | NORMAL | -5 | -18.518518 | NA | Alanine aminotransferase increased | NA | 0 | 0 | NA | 0 | 0 | 0.8148148 | 3.666667 | 0.6470588 | NORMAL to NORMAL | 0 to 0 | Y | NA | Placebo | Placebo | 32 | 1015 | 2014-01-02 | 2014-07-02 | 2014-01-02 | 2014-07-02 | NA | 2014-07-02T11:45 | NA | NA | 701 | 63 | YEARS | F | WHITE | HISPANIC OR LATINO | Pbo | Placebo | Pbo | Placebo | USA | 2013-12-26 | -7 | 2014-01-02 | H | 2014-07-02 23:59:59 | H | 182 | NA | 2014-07-02 | COMPLETED | NA | 2014-01-02 | NA | NA | NA | 2014-07-02 | 18-64 | Y | White | NA | NA | NA | NA | NA |
CDISCPILOT01 | LB | 01-701-1015 | 166 | ALT | Alanine Aminotransferase | CHEMISTRY | 27 | U/L | 6 | 34 | 27 | 27 | U/L | 6 | 34 | NORMAL | NA | 9 | WEEK 12 | 84 | 2014-03-26T15:15 | 84 | 2014-01-02 | 2014-07-02 | Placebo | Placebo | 2014-03-26 | 84 | ALT | Alanine Aminotransferase (U/L) | 3 | CHEMISTRY | 27 | 27 | 6 | 34 | NA | Week 12 | 9 | Y | NORMAL | LAST | NA | 27 | 27 | NORMAL | 0 | 0.000000 | NA | Alanine aminotransferase increased | NA | 0 | 0 | NA | 0 | 0 | 1.0000000 | 4.500000 | 0.7941176 | NORMAL to NORMAL | 0 to 0 | Y | NA | Placebo | Placebo | 33 | 1015 | 2014-01-02 | 2014-07-02 | 2014-01-02 | 2014-07-02 | NA | 2014-07-02T11:45 | NA | NA | 701 | 63 | YEARS | F | WHITE | HISPANIC OR LATINO | Pbo | Placebo | Pbo | Placebo | USA | 2013-12-26 | -7 | 2014-01-02 | H | 2014-07-02 23:59:59 | H | 182 | NA | 2014-07-02 | COMPLETED | NA | 2014-01-02 | NA | NA | NA | 2014-07-02 | 18-64 | Y | White | NA | NA | NA | NA | NA |
CRITy
, CRITyFL
)A standard convention of ADLBHY datasets, are various CRITy
and CRITyFL
columns to describe the conditions necessary to reach that particular criterion of Hy’s Law and the actual flag itself to indicate whether or not the condition was reached.
Using mutate()
, call_derivation()
and derive_var_merged_exist_flag()
, we can create these columns that indicate the the 3-fold or greater than upper limit of normal of ALT/AST and the 2-fold or greater than upper limit of normal of BILI.
To increase visibility and for simplicity, we will retain only columns that are relevant to a Hy’s Law analysis for now.
<- adlb %>%
adlb_annotated mutate(
CRIT1 = case_when(
== "AST" ~ "AST >=3xULN",
PARAMCD == "ALT" ~ "ALT >=3xULN",
PARAMCD == "BILI" ~ "BILI >=2xULN"
PARAMCD
),CRIT1FL = if_else(
/ ANRHI >= 3 & PARAMCD %in% c("AST", "ALT")) |
(AVAL / ANRHI >= 2 & PARAMCD == "BILI"),
(AVAL "Y",
NA_character_
)%>%
) select(STUDYID, USUBJID, TRT01A, PARAMCD, LBSEQ, ADT, AVISIT, ADY, AVAL, ANRHI, CRIT1, CRIT1FL)
STUDYID | USUBJID | TRT01A | PARAMCD | LBSEQ | ADT | AVISIT | ADY | AVAL | ANRHI | CRIT1 | CRIT1FL |
---|---|---|---|---|---|---|---|---|---|---|---|
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 3 | 2013-12-26 | Baseline | -7 | 27 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 41 | 2014-01-16 | Week 2 | 15 | 41 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 76 | 2014-01-30 | Week 4 | 29 | 18 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 106 | 2014-02-12 | Week 6 | 42 | 26 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 136 | 2014-03-05 | Week 8 | 63 | 22 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 166 | 2014-03-26 | Week 12 | 84 | 27 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 201 | 2014-05-07 | Week 16 | 126 | 17 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 231 | 2014-05-21 | Week 20 | 140 | 21 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 261 | 2014-06-18 | Week 24 | 168 | 23 | 34 | ALT >=3xULN | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 296 | 2014-07-02 | Week 26 | 182 | 23 | 34 | ALT >=3xULN | NA |
LBTESTCD
and Joining by Potential EventsIf an elevated ALT/AST event reaches the threshold for Hy’s Law, we need to search for any elevated BILI events within a certain time-window, usually up to 14 days after the elevated ALT/AST event (this window may vary by organization). By,
derive_vars_joined()
while using the filter_join
argument to only join together the relevant flagged BILI records that have a corresponding flagged ALT/AST record (prior up to 14 days but may vary for trial/organization) that would indicate a potential Hy’s Law event,the resulting dataset is helpful for deriving additional parameters. The dataset may also prove useful for a listing where you have to display the two lab-records in one row to showcase the potential event.
<- adlb_annotated %>%
altast_records filter(PARAMCD %in% c("AST", "ALT"))
<- adlb_annotated %>%
bili_records filter(PARAMCD %in% c("BILI"))
<- derive_vars_joined(
hylaw_records dataset = altast_records,
dataset_add = bili_records,
by_vars = exprs(STUDYID, USUBJID),
order = exprs(ADY),
filter_join = 0 <= ADT.join - ADT & ADT.join - ADT <= 14 & CRIT1FL == "Y" & CRIT1FL.join == "Y",
new_vars = exprs(BILI_DT = ADT, BILI_CRITFL = CRIT1FL),
mode = "first"
)
STUDYID | USUBJID | TRT01A | PARAMCD | LBSEQ | ADT | AVISIT | ADY | AVAL | ANRHI | CRIT1 | CRIT1FL | BILI_DT | BILI_CRITFL |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CDISCPILOT01 | 01-705-1186 | Placebo | ALT | 40 | 2014-01-23 | Week 2 | 16 | 104 | 32 | ALT >=3xULN | Y | 2014-01-23 | Y |
CDISCPILOT01 | 01-705-1186 | Placebo | ALT | 127 | 2014-01-29 | Week 4 | 22 | 107 | 32 | ALT >=3xULN | Y | 2014-01-29 | Y |
CDISCPILOT01 | 01-705-1186 | Placebo | AST | 41 | 2014-01-23 | Week 2 | 16 | 118 | 34 | AST >=3xULN | Y | 2014-01-23 | Y |
CDISCPILOT01 | 01-705-1186 | Placebo | AST | 77 | 2014-01-26 | Unscheduled 4.1 | 19 | 115 | 34 | AST >=3xULN | Y | 2014-01-26 | Y |
CDISCPILOT01 | 01-705-1186 | Placebo | AST | 128 | 2014-01-29 | Week 4 | 22 | 135 | 34 | AST >=3xULN | Y | 2014-01-29 | Y |
CDISCPILOT01 | 01-705-1186 | Placebo | AST | 113 | 2014-02-01 | Unscheduled 4.2 | 25 | 114 | 34 | AST >=3xULN | Y | 2014-02-01 | Y |
CDISCPILOT01 | 01-705-1292 | Xanomeline Low Dose | AST | 180 | 2014-02-07 | Week 12 | 117 | 125 | 34 | AST >=3xULN | Y | NA | NA |
CDISCPILOT01 | 01-705-1310 | Xanomeline High Dose | ALT | 135 | 2013-12-26 | Week 8 | 55 | 129 | 32 | ALT >=3xULN | Y | NA | NA |
CDISCPILOT01 | 01-705-1310 | Xanomeline High Dose | AST | 136 | 2013-12-26 | Week 8 | 55 | 114 | 34 | AST >=3xULN | Y | NA | NA |
CDISCPILOT01 | 01-708-1286 | Placebo | ALT | 207 | 2014-02-23 | Week 24 | 167 | 124 | 32 | ALT >=3xULN | Y | NA | NA |
Using derive_param_exist_flag()
you can create a variety of parameters for your final dataset with AVAL = 1/0
for your specific Hy’s Law analysis. Below is an example of how to indicate a potential Hy’s Law event, with PARAMCD
set as "HYSLAW"
and PARAM
set to "ALT/AST >= 3xULN and BILI >= 2xULN"
for each patient using the flags from the prior dataset. This method allows for flexibility as well, if parameters for each visit was desired, you would add AVISIT
and ADT
to the select()
and subject_keys
lines as denoted from the following code.
Additional modifications can be made such as:
<- hylaw_records %>%
hylaw_records_pts_visits select(STUDYID, USUBJID, TRT01A) %>% # add AVISIT, ADT for by visit
distinct()
<- hylaw_records %>%
hylaw_records_fls select(STUDYID, USUBJID, TRT01A, CRIT1FL, BILI_CRITFL) %>% # add AVISIT, ADT for by visit
distinct()
<- derive_param_exist_flag(
hylaw_params dataset_adsl = hylaw_records_pts_visits,
dataset_add = hylaw_records_fls,
condition = CRIT1FL == "Y" & BILI_CRITFL == "Y",
false_value = "N",
missing_value = "N",
subject_keys = exprs(STUDYID, USUBJID, TRT01A), # add AVISIT, ADT for by visit
set_values_to = exprs(
PARAMCD = "HYSLAW",
PARAM = "ALT/AST >= 3xULN and BILI >= 2xULN"
) )
STUDYID | USUBJID | TRT01A | AVALC | AVAL | PARAMCD | PARAM |
---|---|---|---|---|---|---|
CDISCPILOT01 | 01-705-1186 | Placebo | Y | 1 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-701-1015 | Placebo | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-701-1023 | Placebo | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-701-1028 | Xanomeline High Dose | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-701-1033 | Xanomeline Low Dose | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-701-1034 | Xanomeline High Dose | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-701-1047 | Placebo | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-701-1097 | Xanomeline Low Dose | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-705-1292 | Xanomeline Low Dose | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
CDISCPILOT01 | 01-705-1310 | Xanomeline High Dose | N | 0 | HYSLAW | ALT/AST >= 3xULN and BILI >= 2xULN |
The last step would be binding these rows back to whatever previous dataset is appropriate based on your data specifications, in this case, it would be best suited to bind back to our adlb_annotated
object.
<- adlb_annotated %>%
adlbhy bind_rows(hylaw_params)
STUDYID | USUBJID | TRT01A | PARAMCD | LBSEQ | ADT | AVISIT | ADY | AVAL | ANRHI | CRIT1 | CRIT1FL | AVALC | PARAM |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 3 | 2013-12-26 | Baseline | -7 | 27 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 41 | 2014-01-16 | Week 2 | 15 | 41 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 76 | 2014-01-30 | Week 4 | 29 | 18 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 106 | 2014-02-12 | Week 6 | 42 | 26 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 136 | 2014-03-05 | Week 8 | 63 | 22 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 166 | 2014-03-26 | Week 12 | 84 | 27 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 201 | 2014-05-07 | Week 16 | 126 | 17 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 231 | 2014-05-21 | Week 20 | 140 | 21 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 261 | 2014-06-18 | Week 24 | 168 | 23 | 34 | ALT >=3xULN | NA | NA | NA |
CDISCPILOT01 | 01-701-1015 | Placebo | ALT | 296 | 2014-07-02 | Week 26 | 182 | 23 | 34 | ALT >=3xULN | NA | NA | NA |
Here we demonstrated what is the base-case that may be asked of as a trial programmer. The reality is that Hy’s Law and assessing potential DILI events can get rather complex quite quickly. Differences in assessment across organizations and specific trials might require modifications, which may include:
CRITy
and CRITyFL
columns for different cutoffs like 5xULN, 10xULN, 20xULNWe hope by demonstrating the flexibility of admiral
functions and using a general workflow to create the necessary parameters for an ADLBHY, that creating this final dataset becomes simplified and easily scalable. Ideally, this is ready for your organization’s standard macros or previous code for TLFs and outputs as well. This is our first attempt at breaking down and summarizing this topic. We welcome feedback and ideas to improve this guide!
ADaM | Sample Code |
---|---|
ADLBHY | ad_adlbhy.R |
In the walk through below we will use the ADLB dataset created from the call use_ad_template("adlb")
. Due to the size of the dataset, we only included the following USUBJIDs:
01-701-1015
, 01-701-1023
, 01-701-1028
, 01-701-1033
, 01-701-1034
, 01-701-1047
, 01-701-1097
, 01-705-1186
, 01-705-1292
, 01-705-1310
, 01-708-1286
.