### Install admiralonco from GitHub

Source: https://github.com/pharmaverse/admiralonco/blob/main/README.md

Install the latest development version of admiralonco directly from GitHub. Ensure the 'pak' package is installed first.

```r
if (!requireNamespace("pak", quietly = TRUE)) {
  install.packages("pak")
}

pak::pkg_install("pharmaverse/admiralonco", dependencies = TRUE)
```

--------------------------------

### Install admiralonco from CRAN

Source: https://github.com/pharmaverse/admiralonco/blob/main/README.md

Use this command to install the stable version of the admiralonco package from CRAN.

```r
install.packages("admiralonco")
```

--------------------------------

### Complete ADRS Workflow Example in R

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

This script demonstrates the full workflow for creating an ADRS dataset, including merging ADSL variables, filtering, deriving analysis dates and values, and generating analysis flags for various oncology parameters like Overall Response, Disease Progression, Response, and Best Overall Response. It utilizes functions from admiral, admiralonco, dplyr, lubridate, pharmaversesdtm, and pharmaverseadam.

```r
library(admiral)
library(admiralonco)
library(dplyr)
library(lubridate)
library(pharmaversesdtm)
library(pharmaverseadam)

# Load source data
data("adsl")
data("rs_onco_recist")
rs <- convert_blanks_to_na(rs_onco_recist)

# Step 1: Merge ADSL variables and set up initial ADRS
adsl_vars <- exprs(RANDDT)
adrs <- derive_vars_merged(rs, dataset_add = adsl, new_vars = adsl_vars,
                           by_vars = get_admiral_option("subject_keys"))

# Step 2: Filter and set parameter details
adrs <- adrs %>%
  filter(RSEVAL == "INVESTIGATOR" & RSTESTCD == "OVRLRESP") %>%
  mutate(
    PARAMCD = "OVR", PARAM = "Overall Response by Investigator",
    PARCAT1 = "Tumor Response", PARCAT2 = "Investigator", PARCAT3 = "RECIST 1.1"
  )

# Step 3: Derive ADT and AVALC/AVAL
adrs <- adrs %>%
  derive_vars_dt(dtc = RSDTC, new_vars_prefix = "A",
                 highest_imputation = "D", date_imputation = "last") %>%
  mutate(AVISIT = VISIT, AVALC = RSSTRESC, AVAL = aval_resp(AVALC))

# Step 4: Derive analysis flags
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
  ) %>%
  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
  )

# Step 5: Create source dataset for parameter derivations
ovr <- filter(adrs, PARAMCD == "OVR" & ANL01FL == "Y" & ANL02FL == "Y")

# Step 6: Derive all oncology parameters
adrs <- adrs %>%
  # Progressive Disease
  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), mode = "first", exist_flag = AVALC, false_value = "N",
    set_values_to = exprs(PARAMCD = "PD", PARAM = "Disease Progression",
                          AVAL = yn_to_numeric(AVALC), ANL01FL = "Y")
  ) %>%
  # Response
  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",
                          AVAL = yn_to_numeric(AVALC), ANL01FL = "Y")
  ) %>%
  # Best Overall Response
  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(bor_cr, bor_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 = "BOR", PARAM = "Best Overall Response",
                          AVAL = aval_resp(AVALC), ANL01FL = "Y")
  ) %>%
  # Assign ASEQ
  derive_var_obs_number(
    by_vars = get_admiral_option("subject_keys"),
    order = exprs(PARAMCD, ADT, VISITNUM), check_type = "error"
  )

# Final result: ADRS with OVR, PD, RSP, BOR parameters

```

--------------------------------

### Install Admiral Onco

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Install the package from CRAN or the development version from GitHub using the pak package.

```r
# Install from CRAN
install.packages("admiralonco")

# Install development version from GitHub
if (!requireNamespace("pak", quietly = TRUE)) {
  install.packages("pak")
}
pak::pkg_install("pharmaverse/admiralonco", dependencies = TRUE)
```

--------------------------------

### Derive Time-to-Event Parameters

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Use pre-defined event_source and censor_source objects with derive_param_tte() to create OS and PFS parameters.

```r
library(admiralonco)
library(admiral)

# Available TTE source objects:
# Event sources: death_event, pd_event
# Censor sources: lastalive_censor, lasta_censor, rand_censor, trts_censor

# Print definition of death_event
print(death_event)
# event_source(
#   dataset_name = "adrs",
#   filter = PARAMCD == "DEATH" & AVALC == "Y" & ANL01FL == "Y",
#   date = ADT,
#   set_values_to = exprs(
#     EVNTDESC = "Death",
#     SRCDOM = "ADRS",
#     SRCVAR = "ADT",
#     SRCSEQ = ASEQ
#   )
# )

# Derive Overall Survival parameter using pre-defined objects
adtte <- derive_param_tte(
  dataset_adsl = adsl,
  source_datasets = list(adsl = adsl, adrs = adrs),
  start_date = RANDDT,
  event_conditions = list(death_event),
  censor_conditions = list(lastalive_censor),
  set_values_to = exprs(
    PARAMCD = "OS",
    PARAM = "Overall Survival"
  )
)

# Derive Progression-Free Survival using multiple events
adtte <- derive_param_tte(
  dataset_adsl = adsl,
  source_datasets = list(adsl = adsl, adrs = adrs),
  start_date = RANDDT,
  event_conditions = list(pd_event, death_event),
  censor_conditions = list(lasta_censor, rand_censor),
  set_values_to = exprs(
    PARAMCD = "PFS",
    PARAM = "Progression-Free Survival"
  )
)
```

--------------------------------

### Derive Confirmed Response Parameters

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Defines custom confirmation events and derives the confirmed response parameter using event_joined and derive_extreme_event.

```r
library(admiralonco)
library(admiral)
library(dplyr)

# Define study-specific confirmation period (default is 28 days)
confirmation_period <- 21

# Create custom confirmed response events with study-specific confirmation window
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")
)

# Derive Confirmed Response parameter
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"
    )
  )
```

--------------------------------

### Handle deprecation warning for derive_param_confirmed_resp

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/derive_param_confirmed_resp.md

Demonstrates the deprecation message issued when calling the function, advising migration to admiral::derive_extreme_event.

```R
suppress_warning(actual <- derive_param_confirmed_resp(adrs, dataset_adsl = adsl,
        filter_source = PARAMCD == "OVR", source_pd = pd_date, source_datasets = list(
          adrs = adrs), ref_confirm = 28, set_values_to = exprs(AVAL = yn_to_numeric(
          AVALC), PARAMCD = "CRSP", PARAM = "Confirmed Response by Investigator")),
      "Dataset contains CR records followed by PR")
```

--------------------------------

### Derive Best Overall Response

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Utilize pre-defined event objects with derive_extreme_event() to calculate BOR parameters based on RECIST 1.1 criteria.

```r
library(admiralonco)
library(admiral)

# View available pre-defined event objects
# Response events: rsp_y, no_data_n, cb_y
# BOR events: bor_cr, bor_pr, bor_sd, bor_non_crpd, bor_pd, bor_ne, no_data_missing
# Confirmed response events: crsp_y_cr, crsp_y_pr, cbor_cr, cbor_pr

# Print definition of an event object
print(bor_cr)
# event(
#   description = "Define complete response (CR) for best overall response (BOR)",
#   dataset_name = "ovr",
#   condition = AVALC == "CR",
#   set_values_to = exprs(AVALC = "CR")
# )

# Use events to derive Best Overall Response parameter
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",
      PARCAT1 = "Tumor Response",
      AVAL = aval_resp(AVALC),
      ANL01FL = "Y"
    )
  )
```

--------------------------------

### Handle deprecation warning for derive_param_confirmed_bor

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/derive_param_confirmed_bor.md

Shows the usage of the function while suppressing warnings, highlighting the deprecation notice for future migration to admiral::derive_extreme_event.

```R
suppress_warning(actual <- derive_param_confirmed_bor(adrs, dataset_adsl = adsl,
        filter_source = PARAMCD == "OVR", source_pd = pd_date, source_datasets = list(
          adrs = adrs), reference_date = TRTSDT, ref_start_window = 28, ref_confirm = 28,
        set_values_to = exprs(AVAL = aval_resp(AVALC), PARAMCD = "CBOR", PARAM = "Best Confirmed Overall Response by Investigator")),
      "Dataset contains CR records followed by PR")
```

--------------------------------

### Call deprecated derive_param_response

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/derive_param_response.md

This snippet demonstrates calling the deprecated `derive_param_response` function. It will trigger a deprecation message and is recommended to be replaced with `admiral::derive_extreme_event`.

```R
adrs %>% derive_param_response(dataset_adsl = adsl, filter_source = PARAMCD ==
  "OVR" & AVALC %in% c("CR", "PR"), source_pd = NULL, source_datasets = NULL,
  set_values_to = exprs(AVAL = admiral::yn_to_numeric(AVALC), PARAMCD = "RSP",
  PARAM = "Response by investigator"), subject_keys = get_admiral_option(
  "subject_keys"))
```

--------------------------------

### Map Response Values to Numeric

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Use aval_resp() to convert character response values to numeric for AVAL variable creation in ADRS datasets.

```r
library(admiralonco)

# Map character response values to numeric AVAL
response_values <- c("CR", "PR", "SD", "NON-CR/NON-PD", "PD", "NE", "MISSING", "ND", NA_character_)
aval_resp(response_values)
# Returns: 1, 2, 3, 4, 5, 6, 7, NA, NA

# Use in ADRS dataset creation
library(dplyr)
adrs <- adrs %>%
  mutate(
    AVALC = RSSTRESC,
    AVAL = aval_resp(AVALC)
  )
```

--------------------------------

### Derive Progressive Disease Parameter

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Identifies the date of first PD using extreme record derivation.

```r
library(admiralonco)
library(admiral)
library(dplyr)

# Derive Progressive Disease parameter
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"
    )
  )
```

--------------------------------

### Test derive_param_bor deprecation notice

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/derive_param_bor.md

Demonstrates the deprecation warning issued when calling the function, advising migration to admiral::derive_extreme_event().

```R
actual_01 <- derive_param_bor(dataset = adrs, dataset_adsl = adsl,
  filter_source = PARAMCD == "OVR", source_pd = NULL, source_datasets = NULL,
  reference_date = TRTSDT, ref_start_window = 28, set_values_to = exprs(AVAL = {{
    aval_fun_pass }}(AVALC), PARAMCD = "BOR", PARAM = "Best Overall Response"))
```

--------------------------------

### Call deprecated derive_param_clinbenefit function

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/derive_param_clinbenefit.md

Call the derive_param_clinbenefit function to trigger the deprecation message. This function is deprecated and users should use admiral::derive_extreme_event instead. The deprecation message will become a warning in 2027.

```r
actual_output <- derive_param_clinbenefit(dataset = adrs, dataset_adsl = adsl,
  filter_source = PARAMCD == "OVR", source_resp = resp, source_pd = pd,
  source_datasets = list(adrs = adrs), reference_date = TRTSDT,
  ref_start_window = 28, clinben_vals = c("CR", "PR", "SD"), set_values_to = exprs(
    AVAL = yn_to_numeric(AVALC), PARAMCD = "CBR", ANL01FL = "Y"))
```

--------------------------------

### Derive Confirmed Best Overall Response

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Placeholder for deriving the confirmed best overall response parameter.

```r
library(admiralonco)
library(admiral)
library(dplyr)

# Derive Confirmed Best Overall Response parameter
```

--------------------------------

### Handle filter_pd deprecation

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/filter_pd.md

Demonstrates the deprecation message triggered when calling filter_pd and date_source, which are deprecated as of admiralonco 1.4.

```R
actual_output <- filter_pd(dataset = adrs, filter = PARAMCD == "OVR",
      source_pd = date_source(dataset_name = "adevent", date = ADT, filter = PARAMCD ==
        "PD", ), source_datasets = list(adevent = adevent))
```

--------------------------------

### Test derive_param_bor missing records error

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/derive_param_bor.md

Triggers an error when the filter_source argument results in zero records from the input dataset.

```R
derive_param_bor(dataset = adrs, dataset_adsl = adsl, filter_source = PARAMCD ==
  "MISSING RECORDS", source_pd = NULL, source_datasets = NULL, reference_date = TRTSDT,
ref_start_window = 28, set_values_to = exprs(PARAMCD = "BOR", PARAM = "Best Overall Response"))
```

--------------------------------

### Validate response values in derive_param_confirmed_bor

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/derive_param_confirmed_bor.md

Demonstrates the error triggered when invalid response values are provided to the function.

```R
derive_param_confirmed_bor(adrs, dataset_adsl = adsl, filter_source = PARAMCD ==
        "OVR", source_pd = pd_date, source_datasets = list(adrs = adrs),
      reference_date = TRTSDT, ref_start_window = 28, ref_confirm = 28,
      set_values_to = exprs(PARAMCD = "CBOR", PARAM = "Best Confirmed Overall Response by Investigator"))
```

--------------------------------

### Flag Assessments up to First PD

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Creates an analysis flag (ANL02FL) to restrict assessments to those occurring on or before the first PD date.

```r
library(admiralonco)
library(admiral)
library(dplyr)

# Flag assessments up to first PD (ANL02FL)
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
# Post-baseline overall response assessments up to and including first PD
ovr <- filter(adrs, PARAMCD == "OVR" & ANL01FL == "Y" & ANL02FL == "Y")
```

--------------------------------

### Create Custom Non-standard Events

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Defines custom event criteria for study-specific endpoints or adjusted response values.

```r
library(admiralonco)
library(admiral)
library(dplyr)

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

# Derive alternative confirmed clinical benefit
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",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

# Example: Add non-standard response value (No Evidence of Disease)
bor_ned <- event(
  description = "Define NED for best overall response occurring at least 42 days after randomization",
  dataset_name = "ovr",
  condition = AVALC == "NED" & ADT >= RANDDT + 42,
  set_values_to = exprs(AVALC = "NED")
)

# Use custom event in BOR derivation
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 = "Best Overall Response (adjusted for NED at Baseline)",
      AVAL = aval_resp(AVALC),
      ANL01FL = "Y"
    )
  )
```

--------------------------------

### Derive Response Parameter

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Uses derive_extreme_event to identify subjects with a CR or PR response based on the rsp_y event object.

```r
library(admiralonco)
library(admiral)
library(dplyr)

# Prepare overall response source dataset
ovr <- filter(adrs, PARAMCD == "OVR" & ANL01FL == "Y" & ANL02FL == "Y")

# Derive Response parameter (CR or PR = Y, otherwise N)
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"
    )
  )

# Result: Each subject gets one RSP record with AVALC = "Y" or "N"
```

--------------------------------

### Validate response values in derive_param_confirmed_resp

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/derive_param_confirmed_resp.md

Triggers an error when invalid response values are provided to the function.

```R
derive_param_confirmed_resp(adrs, dataset_adsl = adsl, filter_source = PARAMCD ==
        "OVR", source_pd = pd_date, source_datasets = list(adrs = adrs), ref_confirm = 28,
      set_values_to = exprs(PARAMCD = "CRSP", PARAM = "Confirmed Response by Investigator"))
```

--------------------------------

### Derive Best Confirmed Overall Response

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Derives the CBOR parameter using a specific hierarchy of events and extreme record selection.

```r
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"
    )
  )
```

```r
adrs <- adrs %>%
  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",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )
```

--------------------------------

### Derive Clinical Benefit Parameter

Source: https://context7.com/pharmaverse/admiralonco/llms.txt

Derives a clinical benefit parameter by combining response and stable disease events using rsp_y and cb_y.

```r
library(admiralonco)
library(admiral)
library(dplyr)

# Clinical benefit: response (CR/PR) OR SD/NON-CR/NON-PD at least 42 days after randomization
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 not required)",
      PARCAT1 = "Tumor Response",
      PARCAT2 = "Investigator",
      PARCAT3 = "RECIST 1.1",
      AVAL = yn_to_numeric(AVALC),
      ANL01FL = "Y"
    )
  )

# ADT is set to earliest date of response or qualifying SD/NON-CR/NON-PD
```

--------------------------------

### Validate source_datasets in filter_pd

Source: https://github.com/pharmaverse/admiralonco/blob/main/tests/testthat/_snaps/filter_pd.md

Triggers an error when the dataset name provided in source_pd is missing from the source_datasets list.

```R
filter_pd(dataset = adrs, filter = PARAMCD == "OVR", source_pd = source_pd,
      source_datasets = list(ars = adrs))
```

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