Required:

• Serum CA-125 results as per protocol and mapped in LB SDTM domain.
• RECIST 1.1 tumor size and response as per protocol and mapped in RS/TR/TU SDTM domain.

Note: collection of CA-125 and RECIST 1.1 tumor assessment may not be always same visit. CA-125 can be collected more frequently than tumor assessment.

May be Collected:

• CA-125 progression
• CA-125 response
• Combined response of CA-125 and RECIST 1.1 based on GCIG criteria

Assumptions:

For this vignette we made assumptions that following information is collected on the CRF:

1. CA-125 confirmed responses are collected on the CRF and mapped to SDTM RS domain. Please note that we are not programmatically confirming the CA-125 response.

2. The date of CA-125 response is assigned to the date of the first measurement when the CA-125 level is reduced by 50%. The date of CA-125 progression is assigned to the date of the first measurement that meets progression criteria. This is assumed to be collected on the CRF CA-125 response page.

3. CA-125 responses are mapped to CR, PR, SD, PD, NE as shown below.

   CA-125 response per GCIG	CA-125 response mapped
   Response within Normal Range	CR
   Response but not within Normal Range	PR
   Non-Response/Non-PD	SD
   PD	PD
   NE	NE

4. Combined CA-125 and RECIST 1.1 response per visit where both RECIST 1.1 and CA-125 is evaluated is also collected on the CRF. The investigator should already consider CA-125 confirmed response and each visit RECIST 1.1 response based on GCIG document.

5. In SUPPRS there are records with below QNAM and QLABEL values:

   QNAM	QLABEL	QVAL	Purpose	Use case
   CA125EFL	CA-125 response evaluable	Y/N	Indicates population evaluable for CA-125 response (baseline CA-125 >= 2 * ULRR and no mouse antibodies)	CA125EFL variable
   CAELEPRE	Elevated pre-treatment CA-125	Y/N	Indicates CA-125 level at baseline (Y - elevated, N - not elevated)	Derivation of PD category (MCRIT1/MCRIT1ML/MCRIT1MN)
   MOUSEANT	Received mouse antibodies	Y	Indicates if a prohibited therapy was received	Derivation of ANL02FL
   CA50RED	>=50% reduction from baseline	Y	Indicates response, but does not distinguish between CR and PR	Not used in further derivations
   CANORM2X	CA125 normal, lab increased >=2x ULRR	Y	Indicates PD category A or C	Derivation of PD category (MCRIT1/MCRIT1ML/MCRIT1MN)
   CNOTNORM	CA125 not norm, lab increased >=2x nadir	Y	Indicates PD category B	Derivation of PD category (MCRIT1/MCRIT1ML/MCRIT1MN)

The above SUPPRS records refer only to records with RS.RSCAT = "CA125". The exception is QNAM = "MOUSEANT", which appears for both RS.RSCAT = "CA125" and RS.RSCAT = "RECIST 1.1 - CA125" (for the same visits).

If the data are collected by other ways and similar information from SUPPRS dataset are not available, they should be derived in advance from LB CA-125 measurements records. Information on whether the patient has received mouse antibodies should also be taken into account.

Select Overall Response Records per Response Criteria Category

The next step is to assign parameter level values such as PARAMCD, PARAM,PARAMN, PARCAT1, etc. For this, a lookup can be created based on the SDTM RSCAT, RSTESTCD and RSEVAL values to join to the source data.

param_lookup <- tribble(
  ~RSCAT, ~RSTESTCD, ~RSEVAL,
  ~PARAMCD, ~PARAM, ~PARAMN,
  ~PARCAT1, ~PARCAT1N, ~PARCAT2, ~PARCAT2N,

  # CA-125
  "CA125", "OVRLRESP", "INVESTIGATOR",
  "OVRCA125", "CA-125 Overall Response by Investigator", 1,
  "CA-125", 1, "Investigator", 1,

  # RECIST 1.1
  "RECIST 1.1", "OVRLRESP", "INVESTIGATOR",
  "OVRR11", "RECIST 1.1 Overall Response by Investigator", 2,
  "RECIST 1.1", 2, "Investigator", 1,

  # Combined
  "RECIST 1.1 - CA125", "OVRLRESP", "INVESTIGATOR",
  "OVRR11CA", "Combined Overall Response by Investigator", 3,
  "Combined", 3, "Investigator", 1
)

This lookup may now be joined to the source data and this is how the parameters will look like:

adrs <- derive_vars_merged_lookup(
  adrs,
  dataset_add = param_lookup,
  by_vars = exprs(RSCAT, RSTESTCD, RSEVAL)
)

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"
  ) %>%
  derive_vars_dy(
    reference_date = TRTSDT,
    source_vars = exprs(ADT)
  ) %>%
  derive_vars_dtm(
    dtc = RSDTC,
    new_vars_prefix = "A",
    highest_imputation = "D",
    date_imputation = "last",
    flag_imputation = "time"
  ) %>%
  mutate(AVISIT = VISIT)

Derive AVALC and AVAL

Since the set of CA-125 response categories is a subset of the RECIST 1.1 response categories and the set of combined response categories overlaps with the set of RECIST 1.1 response categories, we can use the admiralonco::aval_resp() function to assign AVAL (ordered from best to worst response).

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

Flag One Assessment at Each Analysis Date (ANL01FL)

In the below example:

• we consider only valid assessments and those occurring on or after randomization date,
• subject identifier variables (usually STUDYID and USUBJID), PARAMCD and ADT are a unique key,
• if there is more than one assessment at a date, the worst one is flagged (ensure that the appropriate mode is being set in the admiral::derive_var_extreme_flag()),
• to get the correct ordering, we will define the worst_resp() function.

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(PARAMCD, ADT)),
      order = exprs(worst_resp(AVALC), RSSEQ),
      new_var = ANL01FL,
      mode = "last"
    ),
    filter = !is.na(AVAL) & ADT >= RANDDT
  )

Flag Assessments up to First PD or After Receiving Mouse Antibodies (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().

According to GCIG guidelines, assessments after patients received mouse antibodies or if there has been medical and/or surgical interference with their peritoneum or pleura during the previous 28 days should not be considered.

Note: In our vignette, we will use the variable MOUSEANT, which indicates whether mouse antibodies have been received. The user can similarly include a variable that indicates whether there has been medical and/or surgical interference with the patient’s peritoneum or pleura in the past 28 days.

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

CA-125 Response Evaluable Flag

Patients can only be evaluable for a CA-125 response if they have pre-treatment sample that is at least twice the upper limit of the reference range and within 2 weeks before starting the treatment. In our case, this information is collected only for CA-125 records while a flag is needed at the patient level.

CA-125 Response Evaluable Flag can easily be derived using derive_var_merged_exist_flag() function.

adrs <- adrs %>%
  select(-CA125EFL) %>%
  derive_var_merged_exist_flag(
    dataset_add = adrs,
    by_vars = get_admiral_option("subject_keys"),
    new_var = CA125EFL,
    condition = (CA125EFL == "Y")
  )

Select Source Assessments for Parameter Derivations

For next parameter derivations we consider:

• one assessment at each analysis date (ANL01FL = "Y"),
• assessments up to and including first PD or mouse antibodies whatever comes first (ANL02FL = "Y"),
• CA-125 Response Evaluable population (not for derivation of CA-125 progression),

# used for derivation of CA-125 PD
ovr_pd <- filter(adrs, PARAMCD == "OVRCA125" & ANL01FL == "Y" & ANL02FL == "Y")

# used for derivation of CA-125 response parameters
ovr_ca125 <- filter(adrs, PARAMCD == "OVRCA125" & CA125EFL == "Y" & ANL01FL == "Y" & ANL02FL == "Y")

# used for derivation of unconfirmed best overall response from RECIST 1.1 and confirmed CA-125 together
ovr_ubor <- filter(adrs, PARAMCD == "OVRR11CA" & CA125EFL == "Y" & ANL01FL == "Y" & ANL02FL == "Y")

# used for derivation of confirmed best overall response from RECIST 1.1 and confirmed CA-125 together
ovr_r11 <- filter(adrs, PARAMCD == "OVRR11" & CA125EFL == "Y" & ANL01FL == "Y" & ANL02FL == "Y")

Now that we have the input records prepared above with any company-specific requirements, we can start to derive new parameter records.