library(admiral)library(dplyr,warn.conflicts =FALSE)library(pharmaversesdtm)library(lubridate)library(stringr)dm<- pharmaversesdtm::dmds<- pharmaversesdtm::dsex<- pharmaversesdtm::exae<- pharmaversesdtm::aelb<- pharmaversesdtm::lbdm<-convert_blanks_to_na(dm)ds<-convert_blanks_to_na(ds)ex<-convert_blanks_to_na(ex)ae<-convert_blanks_to_na(ae)lb<-convert_blanks_to_na(lb)

adsl<- dm%>%select(-DOMAIN)

adsl<- dm%>%mutate(TRT01P = ARM,TRT01A = ACTARM)

# impute start and end time of exposure to first and last respectively,# do not impute dateex_ext<- ex%>%derive_vars_dtm(dtc = EXSTDTC,new_vars_prefix ="EXST"  )%>%derive_vars_dtm(dtc = EXENDTC,new_vars_prefix ="EXEN",time_imputation ="last"  )#> The default value of `ignore_seconds_flag` will change to "TRUE" in admiral#> 1.4.0.adsl<- adsl%>%derive_vars_merged(dataset_add = ex_ext,filter_add = (EXDOSE>0|      (EXDOSE==0&str_detect(EXTRT,"PLACEBO")))&!is.na(EXSTDTM),new_vars =exprs(TRTSDTM = EXSTDTM,TRTSTMF = EXSTTMF),order =exprs(EXSTDTM, EXSEQ),mode ="first",by_vars =exprs(STUDYID, USUBJID)  )%>%derive_vars_merged(dataset_add = ex_ext,filter_add = (EXDOSE>0|      (EXDOSE==0&str_detect(EXTRT,"PLACEBO")))&!is.na(EXENDTM),new_vars =exprs(TRTEDTM = EXENDTM,TRTETMF = EXENTMF),order =exprs(EXENDTM, EXSEQ),mode ="last",by_vars =exprs(STUDYID, USUBJID)  )

adsl<- adsl%>%derive_vars_dtm_to_dt(source_vars =exprs(TRTSDTM, TRTEDTM))

adsl<- adsl%>%derive_var_trtdurd()

Disposition Dates (e.g. EOSDT)

The functionsderive_vars_dt() andderive_vars_merged() can be used to derive a dispositiondate. First the character disposition date (DS.DSSTDTC) isconverted to a numeric date (DSSTDT) callingderive_vars_dt(). TheDS dataset is extendedby theDSSTDT variable because the date is required byother derivations, e.g.,RANDDT as well. Then the relevantdisposition date is selected by adjusting thefilter_addargument.

To add the End of Study date (EOSDT) to the inputdataset, a call could be:

# convert character date to numeric date without imputationds_ext<-derive_vars_dt(  ds,dtc = DSSTDTC,new_vars_prefix ="DSST")adsl<- adsl%>%derive_vars_merged(dataset_add = ds_ext,by_vars =exprs(STUDYID, USUBJID),new_vars =exprs(EOSDT = DSSTDT),filter_add = DSCAT=="DISPOSITION EVENT"& DSDECOD!="SCREEN FAILURE"  )

Theds_ext dataset:

Theadsl dataset:

Thederive_vars_dt() function allows to impute partialdates as well. If imputation is needed and missing days are to beimputed to the first of the month and missing months to the first monthof the year, sethighest_imputation = "M".

Disposition Status (e.g. EOSSTT)

The functionderive_vars_merged() can be used to derivethe End of Study status (EOSSTT) based onDSCAT andDSDECOD fromDS. Therelevant observations are selected by adjusting thefilter_add argument. A function mappingDSDECOD values toEOSSTT values can be definedand used in thenew_vars argument. The mapping for the callbelow is

• "COMPLETED" ifDSDECOD == "COMPLETED"
• NA_character_ ifDSDECOD is"SCREEN FAILURE"
• "DISCONTINUED" otherwise

Example functionformat_eosstt():

format_eosstt<-function(x) {case_when(    x%in%c("COMPLETED")~"COMPLETED",    x%in%c("SCREEN FAILURE")~NA_character_,TRUE~"DISCONTINUED"  )}

The customized mapping functionformat_eosstt() can nowbe passed to the main function. For subjects without a disposition eventthe end of study status is set to"ONGOING" by specifyingthemissing_values argument.

adsl<- adsl%>%derive_vars_merged(dataset_add = ds,by_vars =exprs(STUDYID, USUBJID),filter_add = DSCAT=="DISPOSITION EVENT",new_vars =exprs(EOSSTT =format_eosstt(DSDECOD)),missing_values =exprs(EOSSTT ="ONGOING")  )

This call would return the input dataset with the columnEOSSTT added.

If the derivation must be changed, the user can create his/her ownfunction to mapDSDECOD to a suitableEOSSTTvalue.

Disposition Reason(s) (e.g. DCSREAS,DCSREASP)

The main reason for discontinuation is usually stored inDSDECOD whileDSTERM provides additionaldetails regarding subject’s discontinuation (e.g., description of"OTHER").

The functionderive_vars_merged() can be used to derivea disposition reason (along with the details, if required) at a specifictimepoint. The relevant observations are selected by adjusting thefilter_add argument.

To derive the End of Study reason(s) (DCSREAS andDCSREASP), the function will mapDCSREAS asDSDECOD, andDCSREASP asDSTERMifDSDECOD is not"COMPLETED","SCREEN FAILURE", orNA,NAotherwise.

adsl<- adsl%>%derive_vars_merged(dataset_add = ds,by_vars =exprs(USUBJID),new_vars =exprs(DCSREAS = DSDECOD,DCSREASP = DSTERM),filter_add = DSCAT=="DISPOSITION EVENT"&!(DSDECOD%in%c("SCREEN FAILURE","COMPLETED",NA))  )

This call would return the input dataset with the columnDCSREAS andDCSREASP added.

If the derivation must be changed, the user can define thatderivation in thefilter_add argument of the function tomapDSDECOD andDSTERM to a suitableDCSREAS/DCSREASP value.

The call below mapsDCSREAS andDCREASP asfollows:

• DCSREAS asDSDECOD ifDSDECODis not"COMPLETED" orNA,NAotherwise
• DCSREASP asDSTERM ifDSDECODis equal toOTHER,NA otherwise

adsl<- adsl%>%derive_vars_merged(dataset_add = ds,by_vars =exprs(USUBJID),new_vars =exprs(DCSREAS = DSDECOD),filter_add = DSCAT=="DISPOSITION EVENT"&      DSDECOD%notin%c("SCREEN FAILURE","COMPLETED",NA)  )%>%derive_vars_merged(dataset_add = ds,by_vars =exprs(USUBJID),new_vars =exprs(DCSREASP = DSTERM),filter_add = DSCAT=="DISPOSITION EVENT"& DSDECOD%in%"OTHER"  )

Randomization Date (RANDDT)

The functionderive_vars_merged() can be used to deriverandomization date variable. To map Randomization Date(RANDDT), the call would be:

adsl<- adsl%>%derive_vars_merged(dataset_add = ds_ext,filter_add = DSDECOD=="RANDOMIZED",by_vars =exprs(STUDYID, USUBJID),new_vars =exprs(RANDDT = DSSTDT)  )

This call would return the input dataset with the columnRANDDT is added.

Death Date (DTHDT)

The functionderive_vars_dt() can be used to deriveDTHDT. This function allows the user to impute the date aswell.

Example calls:

adsl<- adsl%>%derive_vars_dt(new_vars_prefix ="DTH",dtc = DTHDTC  )

This call would return the input dataset with the columnsDTHDT added and, by default, the associated date imputationflag (DTHDTF) populated with the controlled terminologyoutlined in the ADaM IG for date imputations. If the imputation flag isnot required, the user must set the argumentflag_imputation to"none".

If imputation is needed and the date is to be imputed to the firstday of the month/year the call would be:

adsl<- adsl%>%derive_vars_dt(new_vars_prefix ="DTH",dtc = DTHDTC,date_imputation ="first"  )

See alsoDate and Time Imputation.

Cause of Death (DTHCAUS)

The cause of deathDTHCAUS can be derived using thefunctionderive_vars_extreme_event().

Since the cause of death could be collected/mapped in differentdomains (e.g. DS,AE,DD), it isimportant the user specifies the right source(s) to derive the cause ofdeath from.

For example, if the date of death is collected in the AE form whenthe AE is Fatal, the cause of death would be set to the preferred term(AEDECOD) of that Fatal AE, while if the date of death iscollected in theDS form, the cause of death would be setto the disposition term (DSTERM). To achieve this, theevent() objects withinderive_vars_extreme_event() must be specified and definedsuch that they fit the study requirement.

An example call toderive_vars_extreme_event() wouldbe:

adsl<- adsl%>%derive_vars_extreme_event(by_vars =exprs(STUDYID, USUBJID),events =list(event(dataset_name ="ae",condition = AEOUT=="FATAL",set_values_to =exprs(DTHCAUS = AEDECOD),      ),event(dataset_name ="ds",condition = DSDECOD=="DEATH"&grepl("DEATH DUE TO", DSTERM),set_values_to =exprs(DTHCAUS = DSTERM),      )    ),source_datasets =list(ae = ae,ds = ds),tmp_event_nr_var = event_nr,order =exprs(event_nr),mode ="first",new_vars =exprs(DTHCAUS)  )

The function also offers the option to add some traceabilityvariables (e.g. DTHDOM would store the domain where thedate of death is collected, andDTHSEQ would store thexxSEQ value of that domain). The traceability variablesshould be added to theevent() calls and included in thenew_vars parameter ofderive_vars_extreme_event().

adsl<- adsl%>%select(-DTHCAUS)%>%# remove it before deriving it againderive_vars_extreme_event(by_vars =exprs(STUDYID, USUBJID),events =list(event(dataset_name ="ae",condition = AEOUT=="FATAL",set_values_to =exprs(DTHCAUS = AEDECOD,DTHDOM ="AE",DTHSEQ = AESEQ),      ),event(dataset_name ="ds",condition = DSDECOD=="DEATH"&grepl("DEATH DUE TO", DSTERM),set_values_to =exprs(DTHCAUS = DSTERM,DTHDOM ="DS",DTHSEQ = DSSEQ),      )    ),source_datasets =list(ae = ae,ds = ds),tmp_event_nr_var = event_nr,order =exprs(event_nr),mode ="first",new_vars =exprs(DTHCAUS, DTHDOM, DTHSEQ)  )

Following the derivation ofDTHCAUS and relatedtraceability variables, it is then possible to derive grouping variablessuch as death categories (DTHCGRx) using standard tidyversecode.

adsl<- adsl%>%mutate(DTHCGR1 =case_when(is.na(DTHDOM)~NA_character_,    DTHDOM=="AE"~"ADVERSE EVENT",str_detect(DTHCAUS,"(PROGRESSIVE DISEASE|DISEASE RELAPSE)")~"PROGRESSIVE DISEASE",TRUE~"OTHER"  ))

Duration Relative to Death

The functionderive_vars_duration() can be used toderive duration relative to death like the Relative Day of Death(DTHADY) or the numbers of days from last dose to death(LDDTHELD).

Example calls:

• Relative Day of Death

adsl<- adsl%>%derive_vars_duration(new_var = DTHADY,start_date = TRTSDT,end_date = DTHDT  )

• Elapsed Days from Last Dose to Death

adsl<- adsl%>%derive_vars_duration(new_var = LDDTHELD,start_date = TRTEDT,end_date = DTHDT,add_one =FALSE  )

adsl<- adsl%>%derive_vars_extreme_event(by_vars =exprs(STUDYID, USUBJID),events =list(event(dataset_name ="ae",order =exprs(AESTDTC, AESEQ),condition =!is.na(AESTDTC),set_values_to =exprs(LSTALVDT =convert_dtc_to_dt(AESTDTC,highest_imputation ="M"),seq = AESEQ        ),      ),event(dataset_name ="ae",order =exprs(AEENDTC, AESEQ),condition =!is.na(AEENDTC),set_values_to =exprs(LSTALVDT =convert_dtc_to_dt(AEENDTC,highest_imputation ="M"),seq = AESEQ        ),      ),event(dataset_name ="lb",order =exprs(LBDTC, LBSEQ),condition =!is.na(LBDTC),set_values_to =exprs(LSTALVDT =convert_dtc_to_dt(LBDTC,highest_imputation ="M"),seq = LBSEQ        ),      ),event(dataset_name ="adsl",condition =!is.na(TRTEDT),set_values_to =exprs(LSTALVDT = TRTEDT,seq =0),      )    ),source_datasets =list(ae = ae,lb = lb,adsl = adsl),tmp_event_nr_var = event_nr,order =exprs(LSTALVDT, seq, event_nr),mode ="last",new_vars =exprs(LSTALVDT)  )

adsl<- adsl%>%select(-LSTALVDT)%>%# created in the previous callderive_vars_extreme_event(by_vars =exprs(STUDYID, USUBJID),events =list(event(dataset_name ="ae",order =exprs(AESTDTC, AESEQ),condition =!is.na(AESTDTC),set_values_to =exprs(LSTALVDT =convert_dtc_to_dt(AESTDTC,highest_imputation ="M"),LALVSEQ = AESEQ,LALVDOM ="AE",LALVVAR ="AESTDTC"        ),      ),event(dataset_name ="ae",order =exprs(AEENDTC, AESEQ),condition =!is.na(AEENDTC),set_values_to =exprs(LSTALVDT =convert_dtc_to_dt(AEENDTC,highest_imputation ="M"),LALVSEQ = AESEQ,LALVDOM ="AE",LALVVAR ="AEENDTC"        ),      ),event(dataset_name ="lb",order =exprs(LBDTC, LBSEQ),condition =!is.na(LBDTC),set_values_to =exprs(LSTALVDT =convert_dtc_to_dt(LBDTC,highest_imputation ="M"),LALVSEQ = LBSEQ,LALVDOM ="LB",LALVVAR ="LBDTC"        ),      ),event(dataset_name ="adsl",condition =!is.na(TRTEDT),set_values_to =exprs(LSTALVDT = TRTEDT,LALVSEQ =NA_integer_,LALVDOM ="ADSL",LALVVAR ="TRTEDTM"),      )    ),source_datasets =list(ae = ae,lb = lb,adsl = adsl),tmp_event_nr_var = event_nr,order =exprs(LSTALVDT, LALVSEQ, event_nr),mode ="last",new_vars =exprs(LSTALVDT, LALVSEQ, LALVDOM, LALVVAR)  )

Grouping (e.g. AGEGR1 orREGION1)

Numeric and categorical variables (AGE,RACE,COUNTRY, etc.) may need to be grouped toperform the required analysis.{admiral} provides thederive_vars_cat() function to create such groups. Thisfunction is especially useful if more than one variable needs to becreated for each condition, e.g.,AGEGR1 andAGEGR1N.

Additionally, one needs to be careful when considering the order ofthe conditions in the lookup table. The category is assigned based onthe first match. That meanscatch-all conditions must comeafter specific conditions, e.g. !is.na(COUNTRY) must comeafterCOUNTRY %in% c("CAN", "USA").

# create lookup tablesagegr1_lookup<-exprs(~condition,~AGEGR1,  AGE<18,"<18",between(AGE,18,64),"18-64",  AGE>64,">64",is.na(AGE),"Missing")region1_lookup<-exprs(~condition,~REGION1,  COUNTRY%in%c("CAN","USA"),"North America",!is.na(COUNTRY),"Rest of the World",is.na(COUNTRY),"Missing")

adsl<- adsl%>%derive_vars_cat(definition = agegr1_lookup  )%>%derive_vars_cat(definition = region1_lookup  )

Alternatively, you can also solve this task with customfunctions:

format_agegr1<-function(var_input) {case_when(    var_input<18~"<18",between(var_input,18,64)~"18-64",    var_input>64~">64",TRUE~"Missing"  )}format_region1<-function(var_input) {case_when(    var_input%in%c("CAN","USA")~"North America",!is.na(var_input)~"Rest of the World",TRUE~"Missing"  )}adsl%>%mutate(AGEGR1 =format_agegr1(AGE),REGION1 =format_region1(COUNTRY)  )

Population Flags (e.g. SAFFL)

Since the populations flags are mainly company/study specific nodedicated functions are provided, but in most cases they can easily bederived usingderive_var_merged_exist_flag.

An example of an implementation could be:

adsl<- adsl%>%derive_var_merged_exist_flag(dataset_add = ex,by_vars =exprs(STUDYID, USUBJID),new_var = SAFFL,false_value ="N",missing_value ="N",condition = (EXDOSE>0| (EXDOSE==0&str_detect(EXTRT,"PLACEBO")))  )