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Creating ADRS with Prostate Cancer Working Group 3 (PCWG3) Criteria

Creating ADRS with Prostate Cancer Working Group 3 (PCWG3) Criteria
In admiralonco: Oncology Extension Package for ADaM in 'R' Asset Library 

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(admiraldev)
library(gt)

Introduction

This article describes creating an ADRS ADaM dataset for prostate cancer studies based on Prostate Cancer Working Group 3 (PCWG3) criteria. Most of the endpoints are derived by calling admiral::derive_extreme_event().

Metastatic prostate cancer response cannot be fully captured by RECIST 1.1 criteria alone. Therefore, the PCWG3 guidelines extend these criteria to include composite response combining RECIST 1.1 criteria on soft tissue lesions and PCWG3 rules on bone bone lesions.

Note that only the PCWG3-specific steps are covered in this vignette. For extended guidance on all steps in ADRS creation, refer to the examples in Creating ADRS (Including Non-standard Endpoints).

PCWG3 Guidelines for Prostate Cancer Response

In metastatic prostate cancer clinical trials, efficacy is assessed using PCWG3-modified RECIST 1.1 response and PSA tumor marker results. In this vignette, we have not discussed RECIST 1.1 responses (soft tissue responses), as they are well-documented and widely known, and have focused solely on PCWG3 guidelines for evaluating bone scan responses in prostate cancer.

PSA Exclusion

Prostate-specific antigen (PSA) tumor marker assessments are a key component of the PCWG3 criteria for evaluating prostate cancer progression and response. However, they are excluded in this vignette to focus solely on deriving PCWG3-modified RECIST 1.1 endpoints related to soft tissue and bone lesions.

Bone Response Categories Based on PCWG3 Guidelines:

PCWG3 provides specific criteria for assessing bone lesions, including categories like PDu, NED, and PD, along with the 2+2 rule for confirming progression.

Progressive Disease Unconfirmed (PDu)

Note: if there is no following visit (final visit), the time point remains at PDu.

Progressive Disease (PD)

See Bone Lesion Confirmation Guidance (The 2+2 Rule) for further details.

Not Evaluable (NE)

When imaging is entirely missing or was not done.

No Evidence of Disease (NED)

No bone lesions are present on the scan (whether some were present at baseline and have completely disappeared or whether there were no bone lesions from the start).

Non-Progressive Disease (Non-PD)

Neither PD, PDu, NED or NE.

Bone Lesion Confirmation Guidance (The 2+2 Flare Rule) {#22rule}

Progression on a bone scan is defined based on whether the assessment occurs during the flare period or afterwards. The flare period is generally considered to be the first 8--12 weeks post-baseline, primarily involving the initial post-baseline assessment. Please refer to your study documentation to confirm how the flare period is defined.

Flare Period: If two or more new bone lesions are identified at the first post-baseline assessment compared to the baseline scan, and then at the second assessment (conducted at least 6 weeks later), two or more additional new lesions are observed (resulting in a cumulative total of four or more new lesions since the baseline scan), the progression is confirmed and the progression date is set as the date of the initial scan. This process is referred to as the "2+2 rule."

After Flare Period: If no progression is detected at the first assessment, the initial post-baseline scan (within flare period) becomes the new reference point for subsequent scans. If two or more new lesions appear relative to this new baseline and their persistence or increase in number is confirmed by a follow-up scan at least 6 weeks later, the progression date is recorded as the date of the first scan that noted the appearance of the two new lesions.

Examples of the 2+2 Rule

Flare Period Progression: A patient with metastatic prostate cancer has a baseline bone scan showing 10 lesions. At the first post-baseline scan (within the flare period), 2 new lesions are identified, increasing the total count to 12. This finding is labeled as Progressive Disease Unconfirmed (PDu). A follow-up scan conducted at least 6 weeks later reveals 2 additional new lesions (totaling 4 new lesions compared to baseline). Progression is confirmed based on the 2+2 rule, and the progression date is set as the date of the first post-baseline scan. At the first post-baseline scan response is re-assigned from PDu to PD.

Flare Phenomenon: In another scenario, the same patient has a baseline bone scan showing 10 lesions. The first post-baseline scan during the flare period identifies 2 new lesions, increasing the count to 12. However, a follow-up scan conducted 6 weeks later reveals no additional new lesions. According to the 2+2 rule, this is classified as a flare phenomenon, not true progression. At the first post-baseline scan response is re-assigned from PDu to Non-PD.

After Flare Period Progression: A patient has a baseline scan showing 8 lesions. The initial post-baseline scan, conducted 12 weeks after baseline (within the flare period), shows no new lesions, meaning progression is not detected. This scan becomes the new reference point for subsequent assessments.

At a later scan conducted 18 weeks post-baseline (6 weeks after the new reference point, outside the flare period), 2 new lesions are observed relative to the new reference point. A follow-up scan performed 24 weeks post-baseline (6 weeks later) confirms the persistence of these 2 new lesions. The progression date is recorded as 18 weeks post-baseline, which is the date of the first scan noting the appearance of the 2 new lesions.

Please check Responses & Bone Lesion Confirmation Guidance (The 2+2 Flare Rule) for more details.

Programming Workflow

Required Packages

The examples of this vignette require the following packages.

library(admiral)
library(admiralonco)
library(pharmaversesdtm)
library(pharmaverseadam)
library(dplyr)
library(tibble)

Read in Data {#readdata}

To begin, 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 are ADSL and RS.

For demonstration purpose, the SDTM and ADaM datasets (based on CDISC Pilot test data) from {pharmaversesdtm}[^1] and {pharmaverseadam} are used.

[^1]: Currently the RS domain contains some data issues, which will be resolved in the next {pharmaversesdtm} release. For the purposes of this vignette, these issues have been manually corrected using the following code:

rs <- rs%>% mutate(
  RSSTRESC = case_when(
    USUBJID == "01-701-1275" & VISIT == "WEEK 8" & RSTESTCD %in% c("BONERESP", "OVRLRESP") ~ "PD",
    USUBJID == "01-701-1097" & VISIT == "WEEK 16" & RSTESTCD %in% c("BONERESP", "OVRLRESP") ~ "PD",
    TRUE ~ RSSTRESC
  )
)

In this vignette, the RS SDTM dataset is expected to contain:

# PCWG3 SDTM data
rs <- pharmaversesdtm::rs_onco_pcwg3
rs <- convert_blanks_to_na(rs)

# Exclude PSA records
rs <- rs %>% filter(RSTEST != "Tumor Marker Response")

# ADaM data
adsl <- pharmaverseadam::adsl

# select subjects from adsl such that there is one subject without RS data
rs_subjects <- unique(rs$USUBJID)
adsl_subjects <- unique(adsl$USUBJID)
adsl <- filter(
  adsl,
  USUBJID %in% union(rs_subjects, setdiff(adsl_subjects, rs_subjects)[1])
)

################### To be Removed in next release ######
rs <- rs %>%
  mutate(
    RSSTRESC = case_when(
      USUBJID == "01-701-1275" & VISIT == "WEEK 8" & RSTESTCD %in% c("BONERESP", "OVRLRESP") ~ "PD",
      USUBJID == "01-701-1097" & VISIT == "WEEK 16" & RSTESTCD %in% c("BONERESP", "OVRLRESP") ~ "PD",
      TRUE ~ RSSTRESC
    )
  )
########################

dataset_vignette(
  rs,
  display_vars = exprs(USUBJID, RSCAT, RSTESTCD, RSSTRESC, VISIT, VISITNUM, RSDTC)
)

Pre-processing of Input Records {#input}

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.

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

Partial Date Imputation and Deriving ADT, ADTF, AVISIT, AVISITN 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_dtm(
    dtc = RSDTC,
    new_vars_prefix = "A",
    highest_imputation = "D",
    date_imputation = "last"
  ) %>%
  derive_vars_dtm_to_dt(exprs(ADTM)) %>%
  derive_vars_dy(
    reference_date = TRTSDT,
    source_vars = exprs(ADT)
  ) %>%
  mutate(
    AVISIT = VISIT,
    AVISITN = VISITNUM
  )

Derive PARAMCD, PARAM, PARAMN

The next step is to assign parameter level values such as PARAMCD, PARAM, PARAMN to values collected from source, etc. For this, a lookup can be created based on the SDTM RSTESTCD values.

# Prepare param_lookup for SDTM RSTESTCD to add metadata
param_lookup <- tibble::tribble(
  ~RSTESTCD,  ~PARAMCD,   ~PARAM,                                   ~PARAMN,
  "SFTSRESP", "SFTSRESP", "Soft Tissue Response by Investigator",         1,
  "BONERESP", "BONERESP", "Bone Response by Investigator",                2,
  "OVRLRESP", "OVRLRESP", "Overall Tumor Response by Investigator",       3
)

adrs <- adrs %>%
  derive_vars_merged_lookup(
    dataset_add = param_lookup,
    by_vars = exprs(RSTESTCD)
  ) %>%
  mutate(
    PARCAT1 = RSCAT,
    AVALC = RSSTRESC
  )

Derive Combined Overall Time Point Response by Investigator (OVRLRESC) Parameter

Although OVRLRESP, representing the Overall Tumor Response by Investigator is available in the source data, we have re-derived the combined overall response by Investigator (OVRLRESC). This derivation follows the rules from the PCWG3 and RECIST 1.1 combined response interpretation, as described in the PharmaSUG 2024 publication on metastatic prostate cancer response criteria (PharmaSUG 2024, DS-287).

Table : Combined Overall Time Point Response as per summarized PCWG3 guidelines

overall_tpr_table <- tribble(
  ~`Soft Tissue (RECIST 1.1) TPR`, ~`Bone Lesion (PCWG3) TPR`, ~`Overall PCWG TPR`,
  "PD",                            "Any",                      "PD",
  "Any",                           "PD",                       "PD",
  "NE",                            "Non-PD, PDu, NED or NE",   "NE",
  "NED",                           "Non-PD",                   "Non-CR/Non-PD",
  "NED",                           "PDu",                      "PDu",
  "NED",                           "NED",                      "NE",
  "NED",                           "NE",                       "NE",
  "SD",                            "Non-PD, PDu, NED or NE",   "SD",
  "Non-CR/Non-PD",                 "Non-PD, PDu, NED or NE",   "Non-CR/Non-PD",
  "PR",                            "Non-PD, PDu, NED or NE",   "PR",
  "CR",                            "Non-PD, PDu, or NE",       "PR (1)",
  "CR",                            "Non-PD, PDu, or NE",       "Non-CR/Non-PD (2)",
  "CR",                            "NED",                      "CR"
)

overall_tpr_table %>%
  gt() %>%
  tab_header(
    title = "Table 1: Overall Time Point Response",
    subtitle = "Soft Tissue (RECIST 1.1) TPR, Bone Lesion (PCWG3) TPR, and PCWG Combined TPR"
  ) %>%
  cols_label(
    `Soft Tissue (RECIST 1.1) TPR` = "Soft Tissue (RECIST 1.1)",
    `Bone Lesion (PCWG3) TPR` = "Bone Lesion (PCWG3)",
    `Overall PCWG TPR` = "Overall PCWG"
  ) %>%
  tab_footnote(
    footnote = "* When no target and non-target lesions are identified at baseline, and no new lesions are identified on-study, the response will be No Evidence of Disease (NED)."
  ) %>%
  tab_footnote(
    footnote = "** Progressive Disease Unconfirmed (PDu): Temporary marker of possible PD where at least 2 new bone lesions are present, but an additional scan is required for confirmation. To be updated to PD or Non-PD once a subsequent scan is available. If this is the final visit, the response remains as PDu."
  ) %>%
  tab_footnote(
    footnote = "(1) The overall TPR will be PR if target lesions were present at screening."
  ) %>%
  tab_footnote(
    footnote = "(2) The overall TPR will be Non-CR/Non-PD if no target lesions were present at screening."
  )

Derive Combined Overall Time Point Response by Investigator (OVRLRESC) Records referenced from above table.

Note:Non-PD is changed to NON-PD at programming level to match with existing data.

For Scenario 11, in this vignette, it is assumed that all subjects have target lesions identified at screening. If there are subjects without target lesions identified at screening, the overall response must be evaluated differently, as described in Scenario 11 and referenced in the commented code. Please review your study data to verify whether screening lesions are categorized as target or non-target as this classification impacts the derivation of the overall response.

adrs <- derive_param_computed(
  dataset = adrs,
  by_vars = exprs(
    !!!get_admiral_option("subject_keys"), !!!adsl_vars, DOMAIN, RSEVAL, ADT,
    ADY, ADTM, ADTF, VISIT, VISITNUM, AVISIT, AVISITN
  ),
  parameters = c("SFTSRESP", "BONERESP"),
  set_values_to = exprs(
    AVALC = case_when(
      # Scenario 1 & 2: Soft Tissue PD or Bone Lesion PD -> Overall response = PD
      AVALC.SFTSRESP == "PD" | AVALC.BONERESP == "PD" ~ "PD",

      # Scenario 3: Soft Tissue = NE + Bone Lesion = NON-PD, PDu, NED, or NE -> Overall response = NE
      AVALC.SFTSRESP == "NE" & AVALC.BONERESP %in% c("NON-PD", "PDu", "NED", "NE") ~ "NE",

      # Scenario 4: Soft Tissue = NED + Bone Lesion = NON-PD -> Overall response = Non-CR/NON-PD
      AVALC.SFTSRESP == "NED" & AVALC.BONERESP == "NON-PD" ~ "Non-CR/NON-PD",

      # Scenario 5: Soft Tissue = NED + Bone Lesion = PDu -> Overall response = PDu
      AVALC.SFTSRESP == "NED" & AVALC.BONERESP == "PDu" ~ "PDu",

      # Scenario 6: Soft Tissue = NED + Bone Lesion = NED -> Overall response = NE
      AVALC.SFTSRESP == "NED" & AVALC.BONERESP == "NED" ~ "NE",

      # Scenario 7: Soft Tissue = NED + Bone Lesion = NE -> Overall response = NE
      AVALC.SFTSRESP == "NED" & AVALC.BONERESP == "NE" ~ "NE",

      # Scenario 8: Soft Tissue = SD + Bone Lesion = NON-PD, PDu, NED, or NE -> Overall response = SD
      AVALC.SFTSRESP == "SD" & AVALC.BONERESP %in% c("NON-PD", "PDu", "NED", "NE") ~ "SD",

      # Scenario 9: Soft Tissue = Non-CR/NON-PD + Bone Lesion = NON-PD, PDu, NED, or NE -> Overall response = Non-CR/NON-PD
      AVALC.SFTSRESP == "Non-CR/NON-PD" & AVALC.BONERESP %in% c("NON-PD", "PDu", "NED", "NE") ~ "Non-CR/NON-PD",

      # Scenario 10: Soft Tissue = PR + Bone Lesion = NON-PD, PDu, NED, or NE -> Overall response = PR
      AVALC.SFTSRESP == "PR" & AVALC.BONERESP %in% c("NON-PD", "PDu", "NED", "NE") ~ "PR",

      # Scenario 11: Soft Tissue = CR + Bone Lesion = NON-PD, PDu, NE -> Overall response = PR
      # ((1) The overall TPR will be PR if target lesions were present at screening.)
      AVALC.SFTSRESP == "CR" & AVALC.BONERESP %in% c("NON-PD", "PDu", "NE") ~ "PR",

      # Soft Tissue = CR + Bone Lesion = NON-PD, PDu, NE -> Overall response =Non-CR/NON-PD
      # (2) The overall TPR will be Non-CR/NON-PD if no target lesions were present at screening.)
      # AVALC.SFTSRESP == "CR" & AVALC.BONERESP %in% c("NON-PD", "PDu", "NE") ~ "Non-CR/NON-PD",

      # Scenario 12: Soft Tissue = CR + Bone Lesion = NED -> Overall response = CR
      AVALC.SFTSRESP == "CR" & AVALC.BONERESP == "NED" ~ "CR",

      # Default: If conditions are not met, assign NA
      TRUE ~ NA_character_
    ),
    PARAMCD = "OVRLRESC",
    PARAM = "Overall Tumor Response by Investigator - Derived",
    PARAMN = 4,
    PARCAT1 = "PCWG3 and RECIST 1.1"
  )
)

dataset_vignette(
  adrs %>%
    arrange(!!!get_admiral_option("subject_keys"), AVISITN, PARAMN),
  display_vars = exprs(USUBJID, PARAM, PARAMCD, PARCAT1, AVALC, AVISIT, ADT)
)

Derive AVAL (Numeric tumor response from AVALC values)

The AVAL values are not considered in the further parameter derivations below, and so changing AVAL here would not change the result of those derivations.

adrs <- adrs %>%
  mutate(
    AVAL = case_when(
      AVALC == "CR" ~ 1, # Complete Response
      AVALC == "PR" ~ 2, # Partial Response
      AVALC == "SD" ~ 3, # Stable Disease
      AVALC == "PD" ~ 4, # Progressive Disease
      AVALC == "Non-CR/NON-PD" ~ 5, # Neither Complete Response nor Progressive Disease
      AVALC == "NON-PD" ~ 6, # Non-Progressive Disease
      AVALC == "PDu" ~ 7, # Progressive Disease Unconfirmed
      AVALC == "NE" ~ 8, # Not Evaluable
      AVALC == "NED" ~ 9, # No Evidence of Disease
      TRUE ~ NA_real_ # Default for unexpected/missing AVALC values
    )
  )

dataset_vignette(
  adrs %>%
    arrange(!!!get_admiral_option("subject_keys"), AVISITN, PARAMN),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVISIT, ADT, AVALC, AVAL)
)

Best Overall Response (BOR) and Confirmed Best Overall Response (CBOR)

BOR represents the Best Overall Responses observed during the study, reflecting valid tumor responses such as Complete Response (CR), Partial Response (PR), Stable Disease (SD), and Progressive Disease (PD).

CBOR, on the other hand, refers to the Confirmed Best Overall Response, requiring sustained responses like CR and PR to meet confirmation criteria, such as persistence over a predefined confirmation period (e.g., 28 days), based on PCWG3 guidelines.

For both BOR and CBOR, if PDu remains the last recorded assessment without follow-up confirmation, it is classified as SD in this vignette. However, as a more conservative approach, you may choose to classify it as PD. Please refer to your study documentation and protocol requirements to confirm the preferred approach to handling unresolved PDu.

Additionally, for CBOR, if CR or PR cannot be confirmed, it is classified as SD as usually done for RECIST. Please check the event bor_sd defined in the next section.

Define Events for Best Overall Response (BOR)

Please note:

bor_cr <- event(
  description = "Complete Response (CR)",
  dataset_name = "adrs",
  condition = AVALC == "CR",
  set_values_to = exprs(AVALC = "CR")
)

bor_pr <- event(
  description = "Partial Response (PR)",
  dataset_name = "adrs",
  condition = AVALC == "PR",
  set_values_to = exprs(AVALC = "PR")
)

bor_non_crpd <- event(
  description = "Non-CR/Non-PD",
  dataset_name = "adrs",
  condition = AVALC == "Non-CR/NON-PD",
  set_values_to = exprs(AVALC = "Non-CR/Non-PD")
)

bor_sd <- event(
  description = "Stable Disease (SD)",
  dataset_name = "adrs",
  # CR and PR are included for CBOR when CR or PR couldn't be confirmed
  # PDu can occur only as last assessment and is considered as SD
  condition = AVALC %in% c("CR", "PR", "SD", "PDu"),
  set_values_to = exprs(AVALC = "SD")
)

bor_pd <- event(
  description = "Progressive Disease (PD)",
  dataset_name = "adrs",
  condition = AVALC == "PD",
  set_values_to = exprs(AVALC = "PD")
)

bor_ne <- event(
  description = "Not Evaluable (NE)",
  dataset_name = "adrs",
  condition = AVALC == "NE",
  set_values_to = exprs(AVALC = "NE")
)

bor_ned <- event(
  description = "No Evidence of Disease (NED)",
  dataset_name = "adrs",
  condition = AVALC == "NED",
  set_values_to = exprs(AVALC = "NED")
)

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 Best Overall Response (BOR) {#bor}

Use the defined events to derive BOR based on the first occurrence in the adrs dataset, prioritizing responses hierarchically (CR > PR > SD > Non-CR/NON-PD > PD > NE > NED > MISSING).

In this part of the vignette, we will derive Best Overall Response based on combined response (PARAMCD = "OVRLRESC") as derived above.

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    events = list(
      bor_cr, bor_pr, bor_sd, bor_non_crpd, bor_pd, bor_ne, bor_ned, no_data_missing
    ),
    source_datasets = list(
      adsl = adsl,
      adrs = adrs %>% filter(PARAMCD == "OVRLRESC") # Use derived responses (OVRLRESC)
    ),
    order = exprs(event_nr, ADT), # Prioritize earliest valid event
    tmp_event_nr_var = event_nr,
    mode = "first", # Retain the best response observed at the first occurrence
    set_values_to = exprs(
      PARAMCD = "BOR",
      PARAM = "Best Overall Response",
      PARAMN = 5,
      PARCAT1 = "PCWG3 and RECIST 1.1"
    )
  )

dataset_vignette(
  adrs %>%
    filter(PARAMCD == "BOR"),
  display_vars = exprs(USUBJID, PARAM, PARAMCD, AVISIT, AVISITN, ADT, AVALC, AVAL)
)

Derive Confirmed BOR (CBOR) {#cbor}

As per RECIST 1.1 and PCWG3 guidelines, Complete Response (CR) and Partial Response (PR) require confirmation within a 28-day period to ensure their validity.

# Confirmed CR Event with 28-day persistence
cbor_cr <- event_joined(
  description = "Confirmed Complete Response (CR)",
  dataset_name = "adrs",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  first_cond_upper = AVALC.join == "CR" & ADT.join >= ADT + 28, # Follow-up within 28-day window
  condition = AVALC == "CR" & all(AVALC.join == "CR"), # All linked records must also be CR
  set_values_to = exprs(AVALC = "CR") # Set response as Confirmed CR
)

# Confirmed PR Event with 28-day persistence
cbor_pr <- event_joined(
  description = "Confirmed Partial Response (PR)",
  dataset_name = "adrs",
  join_vars = exprs(AVALC, ADT),
  join_type = "after",
  first_cond_upper = AVALC.join %in% c("CR", "PR") & ADT.join >= ADT + 28, # Include CR as confirmation
  condition = AVALC == "PR" & all(AVALC.join %in% c("CR", "PR")), # Ensure no events other than CR or PR in between
  set_values_to = exprs(AVALC = "PR")
)

adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    events = list(
      cbor_cr, cbor_pr, bor_sd, bor_non_crpd, bor_pd, bor_ne, bor_ned, no_data_missing
    ),
    source_datasets = list(
      adsl = adsl,
      adrs = adrs %>% filter(PARAMCD == "OVRLRESC")
    ),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr, ADT),
    mode = "first",
    set_values_to = exprs(
      PARAMCD = "CBOR",
      PARAM = "Confirmed Best Overall Response",
      PARAMN = 6,
      PARCAT1 = "PCWG3 and RECIST 1.1"
    )
  )

dataset_vignette(
  adrs %>%
    filter(PARAMCD == "CBOR"),
  display_vars = exprs(USUBJID, PARAM, PARAMCD, AVISIT, AVISITN, ADT, AVALC, AVAL)
)

Other Endpoints {#other}

For examples on the additional endpoints, please see Creating ADRS (Including Non-standard Endpoints).



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admiralonco documentation built on Sept. 1, 2025, 5:10 p.m.

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