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Creating ADRS (Including Non-standard Endpoints)

Source: vignettes/adrs.Rmd
adrs.Rmd

Introduction

This article describes creating an ADRS ADaM with oncology endpoint parameters based on RECIST v1.1. It shows an alternative way of deriving the endpoints presented in Creating a Basic ADRS and additionally modified versions of the endpoints (see Derive Non-standard Parameters) which cannot be derived by the admiralonco functions. Most of the endpoints are derived by calling admiral::derive_extreme_event(). It is very flexible. Thus the examples in this vignette can also be used as a starting point for implementing other response criteria than RECIST 1.1, e.g., iRECIST or International Myeloma Working Group (IMWG) criteria for the diagnosis of multiple myeloma.

Examples are currently presented and tested using ADSL (ADaM) and RS, TU (SDTM) inputs. However, other domains could be used. The functions and workflow could similarly be used to create an intermediary ADEVENT ADaM.

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 ADRS should be read into the environment. This will be a company specific process. Some of the data frames needed may be ADSL, RS and TU.

For example purpose, the SDTM and ADaM datasets (based on CDISC Pilot test data)—which are included in pharmaversesdtm and pharmaverseadam—are used.

library(admiral)
library(admiralonco)
library(dplyr)
library(pharmaversesdtm)
library(pharmaverseadam)
library(lubridate)
library(stringr)
data("adsl")
data("rs_onco_recist")
data("tu_onco_recist")

rs <- rs_onco_recist
tu <- tu_onco_recist

rs <- convert_blanks_to_na(rs)
tu <- convert_blanks_to_na(tu)

At this step, it may be useful to join ADSL to your RS domain. Only the ADSL variables used for derivations are selected at this step. The rest of the relevant ADSL would be added later.

adsl_vars <- exprs(RANDDT)
adrs <- derive_vars_merged(
  rs,
  dataset_add = adsl,
  new_vars = adsl_vars,
  by_vars = get_admiral_option("subject_keys")
)

Pre-processing of Input Records

The first step involves company-specific pre-processing of records for the required input to the downstream parameter functions. Note that this could be needed multiple times (e.g. once for investigator and once for Independent Review Facility (IRF)/Blinded Independent Central Review (BICR) records). It could even involve merging input data from other sources besides RS, such as ADTR.

This step would include any required selection/derivation of ADT or applying any necessary partial date imputations, updating AVAL (e.g. this should be ordered from best to worst response), and setting analysis flag ANL01FL. Common options for ANL01FL would be to set null for invalid assessments or those occurring after new anti-cancer therapy, or to only flag assessments on or after after date of first treatment/randomization, or rules to cover the case when a patient has multiple observations per visit (e.g. by selecting worst value). Another consideration could be extra potential protocol-specific sources of Progressive Disease such as radiological assessments, which could be pre-processed here to create a PD record to feed downstream derivations.

For the derivation of the parameters it is expected that the subject identifier variables (usually STUDYID and USUBJID) and ADT are a unique key. This can be achieved by deriving an analysis flag (ANLzzFL). See Derive ANL01FL for an example.

The below shows an example of a possible company-specific implementation of this step.

Select Overall Response Records and Set Parameter Details

In this case we use the overall response records from RS from the investigator as our starting point. The parameter details such as PARAMCD, PARAM etc will always be company-specific, but an example is shown below so that you can trace through how these records feed into the other parameter derivations.

adrs <- adrs %>%
  filter(RSEVAL == "INVESTIGATOR" & RSTESTCD == "OVRLRESP") %>%
  mutate(
    PARAMCD = "OVR",
    PARAM = "Overall Response by Investigator",
    PARCAT1 = "Tumor Response",
    PARCAT2 = "Investigator",
    PARCAT3 = "RECIST 1.1"
  )

Partial Date Imputation and Deriving ADT, ADTF, AVISIT etc

If your data collection allows for partial dates, you could apply a company-specific imputation rule at this stage when deriving ADT. For this example, here we impute missing day to last possible date.

adrs <- adrs %>%
  derive_vars_dt(
    dtc = RSDTC,
    new_vars_prefix = "A",
    highest_imputation = "D",
    date_imputation = "last"
  ) %>%
  mutate(AVISIT = VISIT)

Derive AVALC and AVAL

Here we populate AVALC and create the numeric version as AVAL (ordered from best to worst response). The AVAL values are not considered in the parameter derivations below, and so changing AVAL here would not change the result of those derivations.

adrs <- adrs %>%
  mutate(
    AVALC = RSSTRESC,
    AVAL = aval_resp(AVALC)
  )

Flag Worst Assessment at Each Date (ANL01FL)

When deriving ANL01FL this is an opportunity to exclude any records that should not contribute to any downstream parameter derivations. In the below example this includes only selecting valid assessments and those occurring on or after randomization date. If there is more than one assessment at a date, the worst one is flagged.

worst_resp <- function(arg) {
  case_when(
    arg == "NE" ~ 1,
    arg == "CR" ~ 2,
    arg == "PR" ~ 3,
    arg == "SD" ~ 4,
    arg == "NON-CR/NON-PD" ~ 5,
    arg == "PD" ~ 6,
    TRUE ~ 0
  )
}

adrs <- adrs %>%
  restrict_derivation(
    derivation = derive_var_extreme_flag,
    args = params(
      by_vars = c(get_admiral_option("subject_keys"), exprs(ADT)),
      order = exprs(worst_resp(AVALC), RSSEQ),
      new_var = ANL01FL,
      mode = "last"
    ),
    filter = !is.na(AVAL) & ADT >= RANDDT
  )

Here is an alternative example where those records occurring after new anti-cancer therapy are additionally excluded (where NACTDT would be pre-derived as first date of new anti-cancer therapy. See admiralonco Creating and Using New Anti-Cancer Start Date for deriving this variable).

adrs <- adrs %>%
  mutate(
    ANL01FL = case_when(
      !is.na(AVAL) & ADT >= RANDDT & ADT < NACTDT ~ "Y",
      TRUE ~ NA_character_
    )
  )

Flag Assessments up to First PD (ANL02FL)

To restrict response data up to and including first reported progressive disease ANL02FL flag could be created by using admiral function admiral::derive_var_relative_flag().

adrs <- adrs %>%
  derive_var_relative_flag(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(ADT, RSSEQ),
    new_var = ANL02FL,
    condition = AVALC == "PD",
    mode = "first",
    selection = "before",
    inclusive = TRUE
  )

Select Source Assessments for Parameter derivations

For most parameter derivations the post-baseline overall response assessments up to and including first PD are considered.

ovr <- filter(adrs, PARAMCD == "OVR" & ANL01FL == "Y" & ANL02FL == "Y")

Define Events

The building blocks for the events that contribute to deriving common endpoints like what constitutes a responder, or a Best Overall Response of complete response (CR), … are predefined in admiralonco (see Pre-Defined Response Event Objects). Some may need to be adjusted for study-specific needs, e.g., minimum time between response and confirmation assessment. Here the confirmation period and the keep_source_vars argument is updated.

confirmation_period <- 21

crsp_y_cr <- event_joined(
  description = paste(
    "Define confirmed response as CR followed by CR at least",
    confirmation_period,
    "days later and at most one NE in between"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  order = exprs(ADT),
  first_cond_upper = AVALC.join == "CR" &
    ADT.join >= ADT + days(confirmation_period),
  condition = AVALC == "CR" &
    all(AVALC.join %in% c("CR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1,
  set_values_to = exprs(AVALC = "Y")
)

crsp_y_pr <- event_joined(
  description = paste(
    "Define confirmed response as PR followed by CR or PR at least",
    confirmation_period,
    "days later, at most one NE in between, and no PR after CR"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  order = exprs(ADT),
  first_cond_upper = AVALC.join %in% c("CR", "PR") &
    ADT.join >= ADT + days(confirmation_period),
  condition = AVALC == "PR" &
    all(AVALC.join %in% c("CR", "PR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1 &
    (
      min_cond(
        var = ADT.join,
        cond = AVALC.join == "CR"
      ) > max_cond(var = ADT.join, cond = AVALC.join == "PR") |
        count_vals(var = AVALC.join, val = "CR") == 0 |
        count_vals(var = AVALC.join, val = "PR") == 0
    ),
  set_values_to = exprs(AVALC = "Y")
)

cbor_cr <- event_joined(
  description = paste(
    "Define complete response (CR) for confirmed best overall response (CBOR) as",
    "CR followed by CR at least",
    confirmation_period,
    "days later and at most one NE in between"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  first_cond_upper = AVALC.join == "CR" &
    ADT.join >= ADT + confirmation_period,
  condition = AVALC == "CR" &
    all(AVALC.join %in% c("CR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1,
  set_values_to = exprs(AVALC = "CR")
)

cbor_pr <- event_joined(
  description = paste(
    "Define partial response (PR) for confirmed best overall response (CBOR) as",
    "PR followed by CR or PR at least",
    confirmation_period,
    "28 days later, at most one NE in between, and no PR after CR"
  ),
  dataset_name = "ovr",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  first_cond_upper = AVALC.join %in% c("CR", "PR") &
    ADT.join >= ADT + confirmation_period,
  condition = AVALC == "PR" &
    all(AVALC.join %in% c("CR", "PR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1 &
    (
      min_cond(
        var = ADT.join,
        cond = AVALC.join == "CR"
      ) > max_cond(var = ADT.join, cond = AVALC.join == "PR") |
        count_vals(var = AVALC.join, val = "CR") == 0 |
        count_vals(var = AVALC.join, val = "PR") == 0
    ),
  set_values_to = exprs(AVALC = "PR")
)

no_data_n <- event(
  description = "Define no response for all patients in adsl (should be used as last event)",
  dataset_name = "adsl",
  condition = TRUE,
  set_values_to = exprs(AVALC = "N"),
  keep_source_vars = adsl_vars
)

no_data_missing <- event(
  description = paste(
    "Define missing response (MISSING) for all patients in adsl (should be used",
    "as last event)"
  ),
  dataset_name = "adsl",
  condition = TRUE,
  set_values_to = exprs(AVALC = "MISSING"),
  keep_source_vars = adsl_vars
)

Derive Progressive Disease Parameter

Now that we have the input records prepared above with any company-specific requirements, we can start to derive new parameter records. For the parameter derivations, all values except those overwritten by set_values_to argument are kept from the earliest occurring input record fulfilling the required criteria.

The function admiral::derive_extreme_records() can be used to find the date of first PD.

adrs <- adrs %>%
  derive_extreme_records(
    dataset_ref = adsl,
    dataset_add = adrs,
    by_vars = get_admiral_option("subject_keys"),
    filter_add = PARAMCD == "OVR" & AVALC == "PD" & ANL01FL == "Y",
    order = exprs(ADT, RSSEQ),
    mode = "first",
    exist_flag = AVALC,
    false_value = "N",
    set_values_to = exprs(
      PARAMCD = "PD",
      PARAM = "Disease Progression by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

For progressive disease, response and death parameters shown in steps here and below, in our examples we show these as ADRS parameters, but they could equally be achieved via ADSL dates or ADEVENT parameters. If you prefer to store as an ADSL date, then the function admiral::derive_var_extreme_dt() could be used to find the date of first PD as a variable, rather than as a new parameter record.

Derive Response Parameter

The function admiral::derive_extreme_event() can then be used to find the date of first response. In the below example, the response condition has been defined as CR or PR via the rsp_y1 event.

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(event_nr, ADT),
    tmp_event_nr_var = event_nr,
    mode = "first",
    events = list(rsp_y, no_data_n),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "RSP",
      PARAM = "Response by Investigator (confirmation not required)",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

Derive Clinical Benefit Parameter

The function admiral::derive_extreme_event() can then be used to derive the clinical benefit parameter, which we define as a patient having had a response or a sustained period of time before first PD. This could also be known as disease control. In this example the “sustained period” has been defined as 42 days after randomization date via the cb_y2 event.

Please note that the result AVALC = "Y" is defined by the first two events specified for events. For subjects with observations fulfilling both events the one with the earlier date should be selected (and not the first one in the list). Thus ignore_event_order = TRUE is specified.

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(desc(AVALC), ADT, event_nr),
    tmp_event_nr_var = event_nr,
    mode = "first",
    events = list(rsp_y, cb_y, no_data_n),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "CB",
      PARAM = "Clinical Benefit by Investigator (confirmation for response not required)",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

Derive Best Overall Response Parameter

The function admiral::derive_extreme_event() can be used to derive the best overall response (without confirmation required) parameter. Similar to the above function you can optionally decide what period would you consider a SD or NON-CR/NON-PD as being eligible from. In this example, 42 days after randomization date has been used again.

Please note that the order of the events specified for events is important. For example, a subject with PR, PR, CR qualifies for both bor_cr and bor_pr. As bor_cr is listed before bor_pr, CR is selected as best overall response for this subject.

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(event_nr, ADT),
    tmp_event_nr_var = event_nr,
    mode = "first",
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    events = list(bor_cr, bor_pr, bor_sd, bor_non_crpd, bor_pd, bor_ne, no_data_missing),
    set_values_to = exprs(
      PARAMCD = "BOR",
      PARAM = "Best Overall Response by Investigator (confirmation not required)",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = aval_resp(AVALC),
      ANL01FL = "Y"
    )
  )

Note that the above gives pre-defined AVAL values (defined by aval_resp()) of: "CR" ~ 1, "PR" ~ 2, "SD" ~ 3, "NON-CR/NON-PD" ~ 4, "PD" ~ 5, "NE" ~ 6, "MISSING" ~ 7.

If you’d like to provide your own company-specific ordering here you could do this as follows:

aval_resp_new <- function(arg) {
  case_when(
    arg == "CR" ~ 7,
    arg == "PR" ~ 6,
    arg == "SD" ~ 5,
    arg == "NON-CR/NON-PD" ~ 4,
    arg == "PD" ~ 3,
    arg == "NE" ~ 2,
    arg == "MISSING" ~ 1,
    TRUE ~ NA_real_
  )
}

Then update the definition of AVAL in the set_values_to argument of the above admiral::derive_extreme_event() call. Be aware that this will only impact the AVAL mapping, not the derivation of BOR in any way - as the function derivation relies only on the events and their order specified for the events argument here.

Derive Best Overall Response of CR/PR Parameter

The function admiral::derive_extreme_records() can be used to check if a patient had a response for BOR.

adrs <- adrs %>%
  derive_extreme_records(
    dataset_ref = adsl,
    dataset_add = adrs,
    by_vars = get_admiral_option("subject_keys"),
    filter_add = PARAMCD == "BOR" & AVALC %in% c("CR", "PR"),
    exist_flag = AVALC,
    false_value = "N",
    set_values_to = exprs(
      PARAMCD = "BCP",
      PARAM = "Best Overall Response of CR/PR by Investigator (confirmation not required)",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

Derive Response Parameters requiring Confirmation

Any of the above response parameters can be repeated for “confirmed” responses only. For these the function admiral::derive_extreme_event() can be used with different events. Some of the other functions from above can then be re-used passing in these confirmed response records. See the examples below of derived parameters requiring confirmation. The assessment and the confirmatory assessment here need to occur at least 28 days apart (without any +1 applied to this calculation of days between visits), using the crsp_y_cr3, crsp_y_pr4, cbor_cr5, and cbor_pr6 event.

Please note that the result AVALC = "Y" for confirmed clinical benefit is defined by the first two events specified for events. For subjects with observations fulfilling both events the one with the earlier date should be selected (and not the first one in the list). Thus ignore_event_order = TRUE is specified.

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(desc(AVALC), ADT, event_nr),
    tmp_event_nr_var = event_nr,
    mode = "first",
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    events = list(crsp_y_cr, crsp_y_pr, no_data_n),
    set_values_to = exprs(
      PARAMCD = "CRSP",
      PARAM = "Confirmed Response by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(desc(AVALC), ADT, event_nr),
    tmp_event_nr_var = event_nr,
    mode = "first",
    events = list(crsp_y_cr, crsp_y_pr, cb_y, no_data_n),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "CCB",
      PARAM = "Confirmed Clinical Benefit by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(event_nr, ADT),
    tmp_event_nr_var = event_nr,
    mode = "first",
    events = list(cbor_cr, cbor_pr, bor_sd, bor_non_crpd, bor_pd, bor_ne, no_data_missing),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "CBOR",
      PARAM = "Best Confirmed Overall Response by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = aval_resp(AVALC),
      ANL01FL = "Y"
    )
  ) %>%
  derive_extreme_records(
    dataset_ref = adsl,
    dataset_add = adrs,
    by_vars = get_admiral_option("subject_keys"),
    filter_add = PARAMCD == "CBOR" & AVALC %in% c("CR", "PR"),
    exist_flag = AVALC,
    false_value = "N",
    set_values_to = exprs(
      PARAMCD = "CBCP",
      PARAM = "Best Confirmed Overall Response of CR/PR by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

Derive Non-standard Parameters

As admiral::derive_extreme_event() is very flexible, it is easy to implement non-standard parameters. Below two examples for modified RECIST 1.1 parameters are shown.

Adding a Criterion for Confirmed Clinical Benefit

Confirmed clinical benefit was defined before as confirmed response or CR, PR, SD, or NON-CR/NON-PD at least 42 days after randomization. Here an alternative definition is implemented which considers PD more than 42 days after randomization as an additional criterion for clinical benefit.

cb_y_pd <- event(
  description = paste(
    "Define PD occuring more than 42 days after",
    "randomization as clinical benefit"
  ),
  dataset_name = "ovr",
  condition = AVALC == "PD" & ADT > RANDDT + 42,
  set_values_to = exprs(AVALC = "Y")
)

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(desc(AVALC), ADT, event_nr),
    tmp_event_nr_var = event_nr,
    mode = "first",
    events = list(crsp_y_cr, crsp_y_pr, cb_y, cb_y_pd, no_data_n),
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    set_values_to = exprs(
      PARAMCD = "ACCB",
      PARAM = "Alternative Confirmed Clinical Benefit by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

Considering Non-Standard Response Values

Assume no evidence of disease (NED) is a valid value collected for overall response. A new event (bor_ned) can be defined for this response value and be added to the list of events (events) in the admiral::derive_extreme_event() call.

bor_ned <- event(
  description = paste(
    "Define no evidence of disease (NED) for best overall response (BOR) as NED",
    "occuring at least 42 days after randomization"
  ),
  dataset_name = "ovr",
  condition = AVALC == "NED" & ADT >= RANDDT + 42,
  set_values_to = exprs(AVALC = "NED")
)

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(event_nr, ADT),
    tmp_event_nr_var = event_nr,
    mode = "first",
    source_datasets = list(
      ovr = ovr,
      adsl = adsl
    ),
    events = list(bor_cr, bor_pr, bor_sd, bor_non_crpd, bor_ned, bor_pd, bor_ne, no_data_missing),
    set_values_to = exprs(
      PARAMCD = "A1BOR",
      PARAM = paste(
        "Best Overall Response by Investigator (confirmation not required)",
        "- RECIST 1.1 adjusted for NED at Baseline"
      ),
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1 adjusted for NED at Baseline",
      AVAL = aval_resp(AVALC),
      ANL01FL = "Y"
    )
  )

Derive Parameters using Independent Review Facility (IRF)/Blinded Independent Central Review (BICR) responses

All of the above steps can be repeated for different sets of records, such as now using assessments from the IRF/BICR instead of investigator. For this you would just need to replace the first steps with selecting the required records, create the variables AVALC, AVAL, ADT, AVISIT, ANL01FL, ANL02FL and the dataset ovrb (see Pre-processing of Input Records) and then feed these as input to the downstream parameter functions.

adrs_bicr <- rs %>%
  filter(
    RSEVAL == "INDEPENDENT ASSESSOR" & RSACPTFL == "Y" & RSTESTCD == "OVRLRESP"
  ) %>%
  mutate(
    PARAMCD = "OVRB",
    PARAM = "Overall Response by BICR",
    PARCAT1 = "Tumor Response",
    PARCAT2 = "Blinded Independent Central Review",
    PARCAT3 = "RECIST 1.1"
  )

Then in all the calls to the parameter derivation functions you would replace ovr = ovr with ovr == ovrb in the value of the source_datasets argument.

Derive Death Parameter

The function admiral::derive_extreme_records() can be used to create a new death parameter using death date from ADSL. We need to restrict the columns from ADSL as we’ll merge all required variables later across all our ADRS records.

adsldth <- adsl %>%
  select(!!!get_admiral_option("subject_keys"), DTHDT, !!!adsl_vars)

adrs <- adrs %>%
  derive_extreme_records(
    dataset_ref = adsldth,
    dataset_add = adsldth,
    by_vars = get_admiral_option("subject_keys"),
    filter_add = !is.na(DTHDT),
    exist_flag = AVALC,
    false_value = "N",
    set_values_to = exprs(
      PARAMCD = "DEATH",
      PARAM = "Death",
      PARCAT1 = "Reference Event",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y",
      ADT = DTHDT
    )
  ) %>%
  select(-DTHDT)

Derive Last Disease Assessment Parameters

The function admiral::derive_extreme_records() can be used to create a parameter for last disease assessment.

adrs <- adrs %>%
  derive_extreme_records(
    dataset_ref = adsl,
    dataset_add = adrs,
    by_vars = get_admiral_option("subject_keys"),
    filter_add = PARAMCD == "OVR" & ANL01FL == "Y",
    order = exprs(ADT, RSSEQ),
    mode = "last",
    set_values_to = exprs(
      PARAMCD = "LSTA",
      PARAM = "Last Disease Assessment by Investigator",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      ANL01FL = "Y"
    )
  )

Derive Measurable Disease at Baseline Parameter

The function admiral::derive_param_exist_flag() can be used to check whether a patient has measurable disease at baseline, according to a company-specific condition. In this example we check TU for target lesions during the baseline visit. We need to restrict the columns from ADSL as we’ll merge all required variables later across all our ADRS records.

adslmdis <- adsl %>%
  select(!!!get_admiral_option("subject_keys"), !!!adsl_vars)

adrs <- adrs %>%
  derive_param_exist_flag(
    dataset_ref = adslmdis,
    dataset_add = tu,
    condition = TUEVAL == "INVESTIGATOR" & TUSTRESC == "TARGET" & VISIT == "SCREENING",
    false_value = "N",
    missing_value = "N",
    set_values_to = exprs(
      PARAMCD = "MDIS",
      PARAM = "Measurable Disease at Baseline by Investigator",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

Assign ASEQ

The function admiral::derive_var_obs_number() can be used to derive ASEQ. An example call is:

adrs <- adrs %>%
  derive_var_obs_number(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(PARAMCD, ADT, VISITNUM, RSSEQ),
    check_type = "error"
  )

Add ADSL variables

If needed, the other ADSL variables can now be added. List of ADSL variables already merged held in vector adsl_vars.

adrs <- adrs %>%
  derive_vars_merged(
    dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
    by_vars = get_admiral_option("subject_keys")
  )

Example Script

ADaM Sample Code
ADRS admiral::use_ad_template("ADRS", package = "admiralonco")