---
title: "Creating ADRS with Prostate Cancer Working Group 3 (PCWG3) Criteria"
output:
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Creating ADRS with Prostate Cancer Working Group 3 (PCWG3) Criteria}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r setup, include = FALSE}
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](https://doi.org/10.1200/JCO.2015.64.2702). Most of the endpoints are derived by calling `admiral::derive_extreme_event()`.

RECIST 1.1 criteria alone are insufficient to fully characterize response in
metastatic prostate cancer. The PCWG3 guidelines address this limitation by
defining a composite response that applies RECIST 1.1 to soft‑tissue lesions
while using PCWG3 rules for 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)](adrs.html).

# 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 assessments. 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 and PSA tumor marker assessments.

## Prostate-specific antigen (PSA)

Prostate-specific antigen (PSA) tumor marker assessments are a key component of the PCWG3 criteria for evaluating prostate cancer progression and response. For this vignette, the focus is on \>=50% decline in PSA from baseline as a secondary endpoint and \>=90% decline in PSA from baseline as an exploratory endpoint, confirmed at least 3 weeks later. While this matches PCWG3 principles, please apply the confirmation timing specified in your protocol and any company-specific requirements.

## 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)

-   At least 2 new bone lesions have appeared _within the flare window_ compared to baseline _or_
-   At least 2 new bone lesions have appeared _outside the flare window_ compared to the first post-treatment scan.

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

### Progressive Disease (PD)

-   At least 2 new bone lesions had appeared _within the flare window_ compared to baseline (PDu), and at least 2 additional new bone lesions have been found on the next scan confirming progression (PD). The date of progression is the date of the scan showing the first 2 lesions. _or_
-   At least 2 new bone lesions had appeared _outside the flare window_ compared to the first post-treatment scan (PDu), and the 2 lesions are persistent on the next scan confirming progression (PD). The date of progression is the date of the scan that first documents the second lesion.

See [**Bone Lesion Confirmation Guidance (The 2+2 Rule)**](#22rule) 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 bone scan compared to the baseline scan, and then at a subsequent bone scan (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 that first post-baseline bone scan. This process is referred to as the "2+2 rule."

_After Flare Period:_ The first post-baseline bone scan performed within the flare period becomes the new reference point for subsequent scans, regardless of whether progression (including PDu) is detected at that time. If two or more new lesions appear relative to this new reference 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 bone 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 bone scan. At the first post-baseline bone 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 bone 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 bone scan response is re-assigned from PDu to NON-PD.

_After Flare Period Progression:_ A patient has a baseline scan showing 8 lesions. The first post-baseline bone 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)](https://4870646.fs1.hubspotusercontent-na1.net/hubfs/4870646/KEO149_Info_PCWG3_03.2.pdf) for more details.

# Programming Workflow

-   [Read in Data](#readdata)
-   [Pre-processing of Input Records](#input)
-   [Derive Best Overall Response (BOR)](#bor)
-   [Derive Confirmed Best Overall Response (CBOR)](#cbor)
-   [Derive Unconfirmed PSA50 and PSA90 (PSAURS)](#psaurs)
-   [Derive Confirmed PSA50 and PSA90 (PSACRS)](#psacrs)
-   [Other Endpoints](#other)

## Required Packages

The examples of this vignette require the following packages.

```{r, warning=FALSE, message=FALSE}
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`,`RS` and `LB`.

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

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

-   Soft tissue lesion responses evaluated using RECIST 1.1 criteria at each timepoint.
-   Bone lesion responses assessed across visits using PCWG3 rules.
-   Combined responses derived from both RECIST 1.1 (soft tissue) and PCWG3 (bone lesion) guidelines as collected at source.

```{r message=FALSE}
# PCWG3 SDTM data
rs <- pharmaversesdtm::rs_onco_pcwg3
lb <- pharmaversesdtm::lb_onco_pcwg3

# Filter only PSA records
lb <- lb %>%
  filter(LBTESTCD == "PSA" & !is.na(LBORRES))

# Convert blanks to NA
rs <- convert_blanks_to_na(rs)
lb <- convert_blanks_to_na(lb)

# ADaM data
adsl <- pharmaverseadam::adsl
```

```{r echo=FALSE}
# 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])
)
```

```{r, eval=TRUE, echo=FALSE}
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` and `LB` domains. Only the `ADSL` variables used for derivations are selected at this step.

```{r eval=TRUE}
adsl_vars <- exprs(TRTSDT)
adrs <- derive_vars_merged(
  rs,
  dataset_add = adsl,
  new_vars = adsl_vars,
  by_vars = get_admiral_option("subject_keys")
)

adpsa <- lb %>%
  derive_vars_merged(
    dataset_add = adsl,
    new_vars    = exprs(TRTSDT),
    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.

```{r}
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
  )

adpsa <- adpsa %>%
  derive_vars_dtm(
    dtc                = LBDTC,
    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 `RS`, a lookup can be created
based on the SDTM `RSTESTCD` values and for `ADPSA` (`LB`) they are directly
applied.

```{r, eval=TRUE, include=TRUE, message=FALSE}
# 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
  )

adpsa <- adpsa %>%
  mutate(
    PARAMCD = LBTESTCD,
    PARAM = LBTEST,
    PARCAT1 = "PSA Response",
    AVAL = LBSTRESN
  )
```

## RECIST 1.1 and Bone Response Combined Based on PCWG3 Guidelines

### 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](https://www.lexjansen.com/pharmasug/2024/DS/PharmaSUG-2024-DS-287.pdf)).

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

```{r, eval=TRUE, include=TRUE, message=FALSE,echo=FALSE}
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.

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.

```{r eval=TRUE, message=FALSE, include=TRUE}
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"
  )
)
```

```{r, eval=TRUE, echo=FALSE}
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.

```{r}
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
    )
  )
```

```{r, echo=FALSE}
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:

-   Some of these events are already defined in `{admiralonco}` (see [Pre-Defined Response Event Objects](../reference/event_objects.html)). The definitions are repeated here to show the complete picture.
-   Some of these events are also used for deriving confirmed best overall response.

```{r, eval=TRUE, include=TRUE, message=FALSE}
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.

```{r, eval=TRUE, include=TRUE, message=FALSE}
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"
    )
  )
```

```{r, echo=FALSE}
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.

```{r, eval=TRUE, include=TRUE, message=FALSE}
# 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"
    )
  )
```

```{r, echo=FALSE}
dataset_vignette(
  adrs %>%
    filter(PARAMCD == "CBOR"),
  display_vars = exprs(USUBJID, PARAM, PARAMCD, AVISIT, AVISITN, ADT, AVALC, AVAL)
)
```

## PSA Analysis

In this section, we derive PSA50 (\>=50% decline from baseline) and PSA90 (\>=90% decline) endpoints, both unconfirmed and confirmed, using `derive_extreme_event()` applied to PSA percent change from baseline (`PCHG`) derived from the LB domain (ADPSA). For all PSA endpoints, response (`AVALC = "Y"`) is prioritized over no response (`AVALC = "N"`), and subjects without any PSA measurements are assigned `AVALC = "MISSING"` for this vignette.

### Derive `BASE`, `CHG`, `PCHG` for PSA (ADPSA)

As the `LB` domain is used in this vignette, the baseline variables need to be derived. Alternatively, the `ADLB` dataset could be used. Then this step could be skipped.

```{r}
adpsa <- adpsa %>%
  # Baseline: last non-missing PSA on/ before TRTSDT
  restrict_derivation(
    derivation = derive_var_extreme_flag,
    args = params(
      by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD),
      order   = exprs(ADTM),
      new_var = ABLFL,
      mode    = "last"
    ),
    filter = !is.na(AVAL) & ADT <= TRTSDT
  ) %>%
  derive_var_base(
    by_vars    = exprs(!!!get_admiral_option("subject_keys"), PARAMCD),
    source_var = AVAL,
    new_var    = BASE
  ) %>%
  restrict_derivation(
    derivation = derive_var_chg,
    filter     = ADT > TRTSDT
  ) %>%
  restrict_derivation(
    derivation = derive_var_pchg,
    filter     = ADT > TRTSDT & !is.na(BASE) & !is.na(CHG)
  )
```

```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
  adpsa %>%
    arrange(!!!get_admiral_option("subject_keys"), PARAMCD, ADT, ADTM),
  display_vars = exprs(USUBJID, AVISIT, AVISITN, ADT, AVAL, BASE, CHG, PCHG, ABLFL)
)
```

### Define Events for all PSA endpoints

For responder the first assessment of response is selected. For non-responder
the last PSA assessment is selected. Subjects without baseline but with a
post-baseline PSA assessment are considered as non-responder.

```{r}
# PSA50-Y – first time PCHG <= -50
psa50_y <- event(
  description = "First time PSA50 (PCHG <= -50)",
  dataset_name = "adpsa",
  condition = PCHG <= -50,
  set_values_to = exprs(
    AVALC = "Y",
    AVAL  = 1
  )
)

# Confirmed PSA50 (Y)
psa50_confirmed <- event_joined(
  description = "Confirmed PSA50 (PCHG <= -50, confirmed >=3 weeks later)",
  dataset_name = "adpsa",
  join_vars = exprs(PCHG, ADT),
  join_type = "after",
  first_cond_upper = PCHG.join <= -50 & ADT.join >= ADT + 21,
  condition = PCHG <= -50,
  set_values_to = exprs(
    AVALC = "Y",
    AVAL  = 1
  )
)

# PSA90-Y – first time PCHG <= -90
psa90_y <- event(
  description = "First time PSA90 (PCHG <= -90)",
  dataset_name = "adpsa",
  condition = PCHG <= -90,
  set_values_to = exprs(
    AVALC = "Y",
    AVAL  = 1
  )
)

# Confirmed PSA90 (Y)
psa90_confirmed <- event_joined(
  description = "Confirmed PSA90 (PCHG <= -90, confirmed >=3 weeks later)",
  dataset_name = "adpsa",
  join_vars = exprs(PCHG, ADT),
  join_type = "after",
  first_cond_upper = PCHG.join <= -90 & ADT.join >= ADT + 21,
  condition = PCHG <= -90,
  set_values_to = exprs(
    AVALC = "Y",
    AVAL  = 1
  )
)

# PSA-N – no PSA response
# mode = "last" ensures that, for non-responders (subjects who never reach PCHG <= -xx),
# the "no response" event is anchored to their last available PSA assessment
psa_n <- event(
  description = "No PSA response",
  dataset_name = "adpsa",
  condition = TRUE,
  mode = "last",
  set_values_to = exprs(
    AVALC = "N",
    AVAL  = 0
  )
)

# No PSA data -> MISSING
psa_missing <- event(
  description = "No PSA measurements available",
  dataset_name = "adsl",
  condition = TRUE,
  set_values_to = exprs(
    AVALC = "MISSING",
    AVAL  = NA_real_
  )
)
```

### Derive Unconfirmed PSA50 PSA90 (PSAURS) {#psaurs}

```{r}
# PSA50URS (unconfirmed >=50% decline)
adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    # 1) Y: response, 2) N: no response (with PSA data), 3) MISSING: no PSA data at all
    events = list(psa50_y, psa_n, psa_missing),
    source_datasets = list(
      adpsa = adpsa,
      adsl  = adsl
    ),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr, ADTM),
    mode = "first",
    set_values_to = exprs(
      PARAMCD = "PSA50URS",
      PARAM   = "PSA50 unconfirmed (>=50% decline)",
      PARCAT1 = "PSA Response",
      PARAMN  = 10
    )
  )

# PSA90URS (unconfirmed >=90% decline)
adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    events = list(psa90_y, psa_n, psa_missing),
    source_datasets = list(
      adpsa = adpsa,
      adsl  = adsl
    ),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr, ADTM),
    mode = "first",
    set_values_to = exprs(
      PARAMCD = "PSA90URS",
      PARAM   = "PSA90 unconfirmed (>=90% decline)",
      PARCAT1 = "PSA Response",
      PARAMN  = 12
    )
  )
```

### Derive Confirmed PSA50 PSA90 (PSACRS) {#psacrs}

```{r}
# PSA50CRS (confirmed >=50% decline)
adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    # confirmed response, then no response (with PSA), then MISSING (no PSA)
    events = list(psa50_confirmed, psa_n, psa_missing),
    source_datasets = list(
      adpsa = adpsa,
      adsl  = adsl
    ),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr, ADT),
    mode = "first",
    set_values_to = exprs(
      PARAMCD = "PSA50CRS",
      PARAM   = "PSA50 confirmed (>=50% decline)",
      PARCAT1 = "PSA Response",
      PARAMN  = 11
    )
  )

# PSA90CRS (confirmed >=90% decline)
adrs <- adrs %>%
  derive_extreme_event(
    by_vars = get_admiral_option("subject_keys"),
    events = list(psa90_confirmed, psa_n, psa_missing),
    source_datasets = list(
      adpsa = adpsa,
      adsl  = adsl
    ),
    tmp_event_nr_var = event_nr,
    order = exprs(event_nr, ADTM),
    mode = "first",
    set_values_to = exprs(
      PARAMCD = "PSA90CRS",
      PARAM   = "PSA90 confirmed (>=90% decline)",
      PARCAT1 = "PSA Response",
      PARAMN  = 13
    )
  )
```

```{r, eval=TRUE, echo=FALSE}
dataset_vignette(
  adrs %>% filter(PARCAT1 == "PSA Response") %>%
    arrange(USUBJID, PARAMCD),
  display_vars = exprs(USUBJID, PARAMCD, PARAM, AVISIT, AVISITN, ADT, AVALC, AVAL)
)
```

## Other Endpoints {#other}

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