diff --git a/inst/templates/ad_adee.R b/inst/templates/ad_adee.R
new file mode 100644
index 00000000..8c4a7980
--- /dev/null
+++ b/inst/templates/ad_adee.R
@@ -0,0 +1,249 @@
+# Name: ADEE
+#
+# Label: Exposure Efficacy Response Data
+#
+# Input: adsl, adtte, adlb, advs, adex, adpp
+# Load required packages
+library(admiral)
+library(admiralonco)
+# pharmaverseadam contains example datasets generated from the CDISC pilot
+# project SDTM ran through admiral templates
+library(pharmaverseadam)
+library(dplyr)
+library(lubridate)
+library(stringr)
+
+# Load source datasets ----
+
+# Use e.g. haven::read_sas to read in .sas7bdat, or other suitable functions
+# as needed and assign to the variables below.
+# For illustration purposes read in admiral test data
+
+adsl <- pharmaverseadam::adsl
+adtte <- pharmaverseadam::adtte_onco
+adlb <- pharmaverseadam::adlb
+advs <- pharmaverseadam::advs
+adpp <- pharmaverseadam::adpp
+
+# ---- Prepare adsl - add derived variables
+
+# Ensure TRT01P/TRT01A exist
+if (!"TRT01P" %in% names(adsl)) {
+  adsl <- adsl %>% mutate(TRT01P = ARM)
+}
+if (!"TRT01A" %in% names(adsl)) {
+  adsl <- adsl %>% mutate(TRT01A = ACTARM)
+}
+
+# Create numeric treatment variables
+adsl <- adsl %>%
+  mutate(
+    TRT01PN = case_when(
+      TRT01P == "Placebo" ~ 0,
+      TRT01P == "Xanomeline Low Dose" ~ 1,
+      TRT01P == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    ),
+    TRT01AN = case_when(
+      TRT01A == "Placebo" ~ 0,
+      TRT01A == "Xanomeline Low Dose" ~ 1,
+      TRT01A == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    )
+  )
+
+# Ensure PARAMN exists in ADTTE
+if (!"PARAMN" %in% names(adtte)) {
+  adtte <- adtte %>%
+    mutate(
+      PARAMN = case_when(
+        PARAMCD == "PFS" ~ 1,
+        PARAMCD == "OS" ~ 2,
+        PARAMCD == "TTP" ~ 3,
+        PARAMCD == "TTNT" ~ 4,
+        TRUE ~ 99
+      )
+    )
+}
+
+# ---- Derive Baseline Covariates
+
+## Numeric identifiers and demographics ----
+
+adsl_cov <- adsl %>%
+  mutate(
+    # Study identifiers (numeric)
+    STUDYIDN = as.numeric(word(USUBJID, 1, sep = fixed("-"))),
+    SITEIDN = as.numeric(word(USUBJID, 2, sep = fixed("-"))),
+    USUBJIDN = as.numeric(word(USUBJID, 3, sep = fixed("-"))),
+    SUBJIDN = as.numeric(SUBJID),
+
+    # Demographics (numeric)
+    SEXN = case_when(SEX == "M" ~ 1, SEX == "F" ~ 2, TRUE ~ 3),
+    RACEN = case_when(
+      RACE == "AMERICAN INDIAN OR ALASKA NATIVE" ~ 1,
+      RACE == "ASIAN" ~ 2,
+      RACE == "BLACK OR AFRICAN AMERICAN" ~ 3,
+      RACE == "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER" ~ 4,
+      RACE == "WHITE" ~ 5,
+      TRUE ~ 6
+    ),
+    ETHNICN = case_when(
+      ETHNIC == "HISPANIC OR LATINO" ~ 1,
+      ETHNIC == "NOT HISPANIC OR LATINO" ~ 2,
+      TRUE ~ 3
+    ),
+
+    # Age groups
+    AGEGR1 = case_when(
+      AGE < 65 ~ "<65",
+      AGE >= 65 & AGE < 75 ~ "65-75",
+      AGE >= 75 ~ ">75",
+      TRUE ~ NA_character_
+    ),
+    AGEGR1N = case_when(
+      AGE < 65 ~ 1,
+      AGE >= 65 & AGE < 75 ~ 2,
+      AGE >= 75 ~ 3,
+      TRUE ~ NA_real_
+    ),
+
+    # Treatment (numeric)
+    ARMN = case_when(
+      ARM == "Placebo" ~ 0,
+      ARM == "Xanomeline Low Dose" ~ 1,
+      ARM == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    ),
+    ACTARMN = case_when(
+      ACTARM == "Placebo" ~ 0,
+      ACTARM == "Xanomeline Low Dose" ~ 1,
+      ACTARM == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    )
+  )
+
+## Add baseline vitals ----
+
+adsl_vs <- adsl_cov %>%
+  derive_vars_merged(
+    dataset_add = advs,
+    filter_add = PARAMCD == "HEIGHT" & ABLFL == "Y",
+    by_vars = exprs(STUDYID, USUBJID),
+    new_vars = exprs(HTBL = AVAL)
+  ) %>%
+  derive_vars_merged(
+    dataset_add = advs,
+    filter_add = PARAMCD == "WEIGHT" & ABLFL == "Y",
+    by_vars = exprs(STUDYID, USUBJID),
+    new_vars = exprs(WTBL = AVAL)
+  ) %>%
+  mutate(
+    BMIBL = compute_bmi(height = HTBL, weight = WTBL),
+    BSABL = compute_bsa(height = HTBL, weight = WTBL, method = "Mosteller"),
+    WTBLGR1 = case_when(
+      WTBL < 70 ~ "<70 kg",
+      WTBL >= 70 ~ ">=70 kg",
+      TRUE ~ NA_character_
+    )
+  )
+
+## Add baseline labs ----
+
+labs_bl <- adlb %>%
+  filter(ABLFL == "Y" & PARAMCD %in% c("CREAT", "ALT", "AST", "BILI", "ALB")) %>%
+  mutate(PARAMCDB = paste0(PARAMCD, "BL")) %>%
+  select(STUDYID, USUBJID, PARAMCDB, AVAL)
+
+adsl_vslb <- adsl_vs %>%
+  derive_vars_transposed(
+    dataset_merge = labs_bl,
+    by_vars = exprs(STUDYID, USUBJID),
+    key_var = PARAMCDB,
+    value_var = AVAL
+  ) %>%
+  mutate(
+    TBILBL = BILIBL,
+    CRCLBL = compute_egfr(
+      creat = CREATBL, creatu = "SI", age = AGE,
+      weight = WTBL, sex = SEX, method = "CRCL"
+    ),
+    EGFRBL = compute_egfr(
+      creat = CREATBL, creatu = "SI", age = AGE,
+      weight = WTBL, sex = SEX, method = "CKD-EPI"
+    )
+  ) %>%
+  select(-BILIBL)
+
+# ---- Derive Exposure Metrics
+
+exposure_final <- adsl_vslb %>%
+  derive_vars_transposed(
+    dataset_merge = adpp,
+    filter = PARAMCD %in% c("AUCLST", "CMAX"),
+    by_vars = get_admiral_option("subject_keys"),
+    key_var = PARAMCD,
+    value_var = AVAL
+  ) %>%
+  rename(AUCSS = AUCLST, CMAXSS = CMAX)
+
+# ---- Create adee base dataset
+
+# Get variable names from both datasets
+adsl_vars <- names(exposure_final)
+adtte_vars <- names(adtte)
+
+# Find common variables
+common_vars <- intersect(adsl_vars, adtte_vars)
+
+# Remove key variables to get variables to drop
+vars_to_drop <- setdiff(common_vars, c("STUDYID", "USUBJID"))
+
+adee_base <- adtte %>%
+  # Filter to efficacy endpoints
+  filter(PARAMCD %in% c("OS", "PFS", "TTP", "TTNT")) %>%
+  # Add derived variables
+  mutate(
+    EVENT = 1 - CNSR,
+    AVALU = if_else(!is.na(AVAL), "DAYS", NA_character_),
+  ) %>%
+  # Remove overlapping variables (use clean method)
+  select(-any_of(vars_to_drop)) %>%
+  # Merge exposure and covariates
+  derive_vars_merged(
+    dataset_add = exposure_final,
+    by_vars = exprs(STUDYID, USUBJID)
+  )
+
+# ---- Add Anaylsis variables
+
+adee <- adee_base %>%
+  # Analysis flags
+  mutate(
+    ANL01FL = if_else(PARAMCD == "PFS", "Y", ""),
+    ANL02FL = if_else(PARAMCD == "OS", "Y", ""),
+    ANL03FL = if_else(PARAMCD == "TTP", "Y", ""),
+    ANL04FL = if_else(PARAMCD == "TTNT", "Y", "")
+  ) %>%
+  # Parameter categories
+  mutate(
+    PARCAT1 = "EFFICACY",
+    PARCAT2 = "TIME TO EVENT"
+  ) %>%
+  # Sequence number
+  derive_var_obs_number(
+    by_vars = exprs(STUDYID, USUBJID),
+    order = exprs(PARAMN),
+    new_var = ASEQ,
+    check_type = "error"
+  )
+
+# Save output ----
+
+# Change to whichever directory you want to save the dataset in
+dir <- tools::R_user_dir("admiralonco_templates_data", which = "cache")
+if (!file.exists(dir)) {
+  # Create the folder
+  dir.create(dir, recursive = TRUE, showWarnings = FALSE)
+}
+save(adee, file = file.path(dir, "adee.rda"), compress = "bzip2")
diff --git a/inst/templates/ad_ader.R b/inst/templates/ad_ader.R
new file mode 100644
index 00000000..e85c4ce0
--- /dev/null
+++ b/inst/templates/ad_ader.R
@@ -0,0 +1,206 @@
+# Name: ADER
+#
+# Label: Exposure Response Data
+#
+# Input: adsl, adrs, adtte, adlb, advs, adex
+library(admiral)
+library(admiralonco)
+# pharmaverseadam contains example datasets generated from the CDISC pilot
+# project SDTM ran through admiral templates
+library(pharmaverseadam)
+library(dplyr)
+library(lubridate)
+library(stringr)
+
+# Load source datasets ----
+
+# Use e.g. haven::read_sas to read in .sas7bdat, or other suitable functions
+# as needed and assign to the variables below.
+# For illustration purposes read in data from pharmaverseadam
+
+adsl <- pharmaverseadam::adsl
+adrs <- pharmaverseadam::adrs_onco
+adtte <- pharmaverseadam::adtte_onco
+adlb <- pharmaverseadam::adlb
+advs <- pharmaverseadam::advs
+adex <- pharmaverseadam::adex %>%
+  filter(PARCAT1 == "INDIVIDUAL")
+
+adpp <- pharmaverseadam::adpp
+
+# Derivations ----
+
+# For ADTTE censor variables add "IND" to PARAMCD
+adttei <- adtte %>%
+  mutate(PARAMCD = paste0(PARAMCD, "IND"))
+
+ader_tte <- adsl %>%
+  select(!!!get_admiral_option("subject_keys")) %>%
+  # Create OS and PFS variables from ADTTE
+  derive_vars_transposed(
+    dataset_merge = adtte,
+    by_vars = get_admiral_option("subject_keys"),
+    key_var = PARAMCD,
+    value_var = AVAL
+  ) %>%
+  # Create OS and PFS censor variables
+  derive_vars_transposed(
+    dataset_merge = adttei,
+    by_vars = get_admiral_option("subject_keys"),
+    key_var = PARAMCD,
+    value_var = CNSR
+  )
+
+# Derive Best Overall Response (BOR) variables from ADRS
+ader_bor <- ader_tte %>%
+  derive_vars_merged(
+    dataset_add = adrs,
+    filter_add = PARAMCD == "BOR" & ANL01FL == "Y",
+    by_vars = get_admiral_option("subject_keys"),
+    new_vars = exprs(BOR = AVAL, BORC = AVALC)
+  )
+
+# Add exposure metrics
+
+ader_auc <- ader_bor %>%
+  derive_vars_transposed(
+    dataset_merge = adpp,
+    filter = PARAMCD %in% c("AUCLST", "CMAX"),
+    by_vars = get_admiral_option("subject_keys"),
+    key_var = PARAMCD,
+    value_var = AVAL
+  ) %>%
+  rename(AUCSS = AUCLST, CMAXSS = CMAX)
+
+# Add Sequence number
+
+ader_aseq <- ader_auc %>%
+  derive_var_obs_number(
+    by_vars = get_admiral_option("subject_keys"),
+    check_type = "error"
+  )
+
+# ---- Derive Covariates ----
+# Include numeric values for STUDYIDN, USUBJIDN, SEXN, RACEN etc.
+
+covar <- adsl %>%
+  derive_vars_merged(
+    dataset_add = country_code_lookup,
+    new_vars = exprs(COUNTRYN = country_number, COUNTRYL = country_name),
+    by_vars = exprs(COUNTRY = country_code)
+  ) %>%
+  mutate(
+    STUDYIDN = as.numeric(word(USUBJID, 1, sep = fixed("-"))),
+    SITEIDN = as.numeric(word(USUBJID, 2, sep = fixed("-"))),
+    USUBJIDN = as.numeric(word(USUBJID, 3, sep = fixed("-"))),
+    SUBJIDN = as.numeric(SUBJID),
+    SEXN = case_when(
+      SEX == "M" ~ 1,
+      SEX == "F" ~ 2,
+      TRUE ~ 3
+    ),
+    RACEN = case_when(
+      RACE == "AMERICAN INDIAN OR ALASKA NATIVE" ~ 1,
+      RACE == "ASIAN" ~ 2,
+      RACE == "BLACK OR AFRICAN AMERICAN" ~ 3,
+      RACE == "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER" ~ 4,
+      RACE == "WHITE" ~ 5,
+      TRUE ~ 6
+    ),
+    ETHNICN = case_when(
+      ETHNIC == "HISPANIC OR LATINO" ~ 1,
+      ETHNIC == "NOT HISPANIC OR LATINO" ~ 2,
+      TRUE ~ 3
+    ),
+    ARMN = case_when(
+      ARM == "Placebo" ~ 0,
+      ARM == "Xanomeline Low Dose" ~ 1,
+      ARM == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    ),
+    ACTARMN = case_when(
+      ACTARM == "Placebo" ~ 0,
+      ACTARM == "Xanomeline Low Dose" ~ 1,
+      ACTARM == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    ),
+    COHORT = ARMN,
+    COHORTC = ARM,
+    ROUTE = unique(adex$EXROUTE)[1],
+    ROUTEN = case_when(
+      ROUTE == "TRANSDERMAL" ~ 3,
+      TRUE ~ NA_real_
+    ),
+    FORM = unique(adex$EXDOSFRM)[1],
+    FORMN = case_when(
+      FORM == "PATCH" ~ 3,
+      TRUE ~ 4
+    )
+  ) %>%
+  select(
+    STUDYID, STUDYIDN, SITEID, SITEIDN, USUBJID, USUBJIDN,
+    SUBJID, SUBJIDN, AGE, SEX, SEXN, COHORT, COHORTC, ROUTE, ROUTEN,
+    RACE, RACEN, ETHNIC, ETHNICN, FORM, FORMN, COUNTRY, COUNTRYN, COUNTRYL
+  )
+
+# ---- Derive additional baselines from ADVS and ADLB ----
+
+labsbl <- adlb %>%
+  filter(ABLFL == "Y" & PARAMCD %in% c("CREAT", "ALT", "AST", "BILI")) %>%
+  mutate(PARAMCDB = paste0(PARAMCD, "BL")) %>%
+  select(STUDYID, USUBJID, PARAMCDB, AVAL)
+
+covar_vslb <- covar %>%
+  derive_vars_merged(
+    dataset_add = advs,
+    filter_add = PARAMCD == "HEIGHT" & ABLFL == "Y",
+    by_vars = exprs(STUDYID, USUBJID),
+    new_vars = exprs(HTBL = AVAL)
+  ) %>%
+  derive_vars_merged(
+    dataset_add = advs,
+    filter_add = PARAMCD == "WEIGHT" & ABLFL == "Y",
+    by_vars = exprs(STUDYID, USUBJID),
+    new_vars = exprs(WTBL = AVAL)
+  ) %>%
+  derive_vars_transposed(
+    dataset_merge = labsbl,
+    by_vars = exprs(STUDYID, USUBJID),
+    key_var = PARAMCDB,
+    value_var = AVAL
+  ) %>%
+  mutate(
+    BMIBL = compute_bmi(height = HTBL, weight = WTBL),
+    BSABL = compute_bsa(
+      height = HTBL,
+      weight = WTBL,
+      method = "Mosteller"
+    ),
+    CRCLBL = compute_egfr(
+      creat = CREATBL, creatu = "SI", age = AGE, weight = WTBL, sex = SEX,
+      method = "CRCL"
+    ),
+    EGFRBL = compute_egfr(
+      creat = CREATBL, creatu = "SI", age = AGE, weight = WTBL, sex = SEX,
+      method = "CKD-EPI"
+    )
+  ) %>%
+  rename(TBILBL = BILIBL)
+
+# Combine covariates with ADER data
+
+ader <- ader_aseq %>%
+  derive_vars_merged(
+    dataset_add = covar_vslb,
+    by_vars = exprs(STUDYID, USUBJID)
+  )
+
+# Save output ----
+
+# Change to whichever directory you want to save the dataset in
+dir <- tools::R_user_dir("admiralonco_templates_data", which = "cache")
+if (!file.exists(dir)) {
+  # Create the folder
+  dir.create(dir, recursive = TRUE, showWarnings = FALSE)
+}
+save(ader, file = file.path(dir, "ader.rda"), compress = "bzip2")
diff --git a/inst/templates/ad_adtrr.R b/inst/templates/ad_adtrr.R
new file mode 100644
index 00000000..703f84ab
--- /dev/null
+++ b/inst/templates/ad_adtrr.R
@@ -0,0 +1,326 @@
+# Name: ADTRR
+#
+# Label: Exposure Tumor Response Data
+#
+# Input: adsl, adtr, adlb, advs, adex, adpp
+# Load required packages
+library(admiral)
+library(admiralonco)
+# pharmaverseadam contains example datasets generated from the CDISC pilot
+# project SDTM ran through admiral templates
+library(pharmaverseadam)
+library(dplyr)
+library(lubridate)
+library(stringr)
+
+# Load source datasets ----
+
+# Use e.g. haven::read_sas to read in .sas7bdat, or other suitable functions
+# as needed and assign to the variables below.
+# For illustration purposes read in data from pharmaverseadam
+
+adsl <- pharmaverseadam::adsl
+adrs <- pharmaverseadam::adrs_onco
+adtr <- pharmaverseadam::adtr_onco
+adlb <- pharmaverseadam::adlb
+advs <- pharmaverseadam::advs
+adpp <- pharmaverseadam::adpp
+
+# ---- Prepare adsl - add derived variables
+
+# Ensure TRT01P/TRT01A exist
+if (!"TRT01P" %in% names(adsl)) {
+  adsl <- adsl %>% mutate(TRT01P = ARM)
+}
+if (!"TRT01A" %in% names(adsl)) {
+  adsl <- adsl %>% mutate(TRT01A = ACTARM)
+}
+
+# Create numeric treatment variables
+adsl <- adsl %>%
+  mutate(
+    TRT01PN = case_when(
+      TRT01P == "Placebo" ~ 0,
+      TRT01P == "Xanomeline Low Dose" ~ 1,
+      TRT01P == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    ),
+    TRT01AN = case_when(
+      TRT01A == "Placebo" ~ 0,
+      TRT01A == "Xanomeline Low Dose" ~ 1,
+      TRT01A == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    )
+  )
+
+# ---- Derive Baseline Covariates
+
+## Numeric identifiers and demographics ----
+
+adsl_cov <- adsl %>%
+  filter(SAFFL == "Y") %>%
+  mutate(
+    # Study identifiers (numeric)
+    STUDYIDN = as.numeric(word(USUBJID, 1, sep = fixed("-"))),
+    SITEIDN = as.numeric(word(USUBJID, 2, sep = fixed("-"))),
+    USUBJIDN = as.numeric(word(USUBJID, 3, sep = fixed("-"))),
+    SUBJIDN = as.numeric(SUBJID),
+
+    # Demographics (numeric)
+    SEXN = case_when(SEX == "M" ~ 1, SEX == "F" ~ 2, TRUE ~ 3),
+    RACEN = case_when(
+      RACE == "AMERICAN INDIAN OR ALASKA NATIVE" ~ 1,
+      RACE == "ASIAN" ~ 2,
+      RACE == "BLACK OR AFRICAN AMERICAN" ~ 3,
+      RACE == "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER" ~ 4,
+      RACE == "WHITE" ~ 5,
+      TRUE ~ 6
+    ),
+    ETHNICN = case_when(
+      ETHNIC == "HISPANIC OR LATINO" ~ 1,
+      ETHNIC == "NOT HISPANIC OR LATINO" ~ 2,
+      TRUE ~ 3
+    ),
+
+    # Age groups
+    AGEGR1 = case_when(
+      AGE < 65 ~ "<65",
+      AGE >= 65 & AGE < 75 ~ "65-75",
+      AGE >= 75 ~ ">75",
+      TRUE ~ NA_character_
+    ),
+    AGEGR1N = case_when(
+      AGE < 65 ~ 1,
+      AGE >= 65 & AGE < 75 ~ 2,
+      AGE >= 75 ~ 3,
+      TRUE ~ NA_real_
+    ),
+
+    # Treatment (numeric)
+    ARMN = case_when(
+      ARM == "Placebo" ~ 0,
+      ARM == "Xanomeline Low Dose" ~ 1,
+      ARM == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    ),
+    ACTARMN = case_when(
+      ACTARM == "Placebo" ~ 0,
+      ACTARM == "Xanomeline Low Dose" ~ 1,
+      ACTARM == "Xanomeline High Dose" ~ 2,
+      TRUE ~ 3
+    )
+  )
+
+## Add baseline vitals ----
+
+adsl_vs <- adsl_cov %>%
+  derive_vars_merged(
+    dataset_add = advs,
+    filter_add = PARAMCD == "HEIGHT" & ABLFL == "Y",
+    by_vars = exprs(STUDYID, USUBJID),
+    new_vars = exprs(HTBL = AVAL)
+  ) %>%
+  derive_vars_merged(
+    dataset_add = advs,
+    filter_add = PARAMCD == "WEIGHT" & ABLFL == "Y",
+    by_vars = exprs(STUDYID, USUBJID),
+    new_vars = exprs(WTBL = AVAL)
+  ) %>%
+  mutate(
+    BMIBL = compute_bmi(height = HTBL, weight = WTBL),
+    BSABL = compute_bsa(height = HTBL, weight = WTBL, method = "Mosteller"),
+    WTBLGR1 = case_when(
+      WTBL < 70 ~ "<70 kg",
+      WTBL >= 70 ~ ">=70 kg",
+      TRUE ~ NA_character_
+    )
+  )
+
+## Add baseline labs ----
+
+labs_bl <- adlb %>%
+  filter(ABLFL == "Y" & PARAMCD %in% c("CREAT", "ALT", "AST", "BILI", "ALB")) %>%
+  mutate(PARAMCDB = paste0(PARAMCD, "BL")) %>%
+  select(STUDYID, USUBJID, PARAMCDB, AVAL)
+
+adsl_vslb <- adsl_vs %>%
+  derive_vars_transposed(
+    dataset_merge = labs_bl,
+    by_vars = exprs(STUDYID, USUBJID),
+    key_var = PARAMCDB,
+    value_var = AVAL
+  ) %>%
+  mutate(
+    TBILBL = BILIBL,
+    CRCLBL = compute_egfr(
+      creat = CREATBL, creatu = "SI", age = AGE,
+      weight = WTBL, sex = SEX, method = "CRCL"
+    ),
+    EGFRBL = compute_egfr(
+      creat = CREATBL, creatu = "SI", age = AGE,
+      weight = WTBL, sex = SEX, method = "CKD-EPI"
+    )
+  ) %>%
+  select(-BILIBL)
+# ---- Derive Exposure Metrics
+
+exposure_final <- adsl_vslb %>%
+  derive_vars_transposed(
+    dataset_merge = adpp,
+    filter = PARAMCD %in% c("AUCLST", "CMAX"),
+    by_vars = get_admiral_option("subject_keys"),
+    key_var = PARAMCD,
+    value_var = AVAL
+  ) %>%
+  rename(AUCSS = AUCLST, CMAXSS = CMAX)
+
+# ---- Create tumor size parameter dataset
+
+# Get variable names for clean dropping
+adsl_vars <- names(exposure_final)
+adtr_vars <- names(adtr)
+common_vars <- intersect(adsl_vars, adtr_vars)
+vars_to_drop <- setdiff(common_vars, c("STUDYID", "USUBJID"))
+
+tsize_final <- adtr %>%
+  filter(PARAMCD == "SDIAM") %>%
+  mutate(
+    PARAMCD = "TSIZE",
+    PARAM = "Target Lesions Sum of Diameters",
+    PARAMN = 1
+  ) %>%
+  derive_var_nfrlt(
+    new_var = NFRLT,
+    new_var_unit = FRLTU,
+    out_unit = "HOURS",
+    visit_day = ADY
+  ) %>%
+  select(-any_of(vars_to_drop)) %>%
+  derive_vars_merged(
+    dataset_add = exposure_final,
+    by_vars = exprs(STUDYID, USUBJID)
+  )
+
+# ---- Add BOR from ADRS
+
+adrs_vars <- names(adrs)
+common_vars_adrs <- intersect(adsl_vars, adrs_vars)
+vars_to_drop_adrs <- setdiff(common_vars_adrs, c("STUDYID", "USUBJID"))
+
+bor <- adrs %>%
+  filter(PARAMCD == "BOR" & SAFFL == "Y") %>%
+  mutate(
+    PARAMN = 2,
+    # Create BORN from AVALC if AVAL doesn't exist
+    BORN = if ("AVAL" %in% names(.)) {
+      AVAL
+    } else {
+      case_when(
+        AVALC == "CR" ~ 4,
+        AVALC == "PR" ~ 3,
+        AVALC == "SD" ~ 2,
+        AVALC == "PD" ~ 1,
+        AVALC == "NE" ~ 0,
+        TRUE ~ NA_real_
+      )
+    }
+  ) %>%
+  select(-any_of(vars_to_drop_adrs)) %>%
+  derive_vars_merged(
+    dataset_add = exposure_final,
+    by_vars = exprs(STUDYID, USUBJID)
+  )
+
+# ---- Derive Nadir
+
+# Calculate nadir from TSIZE records
+# Keep BASE, CHG, PCHG from the nadir timepoint
+nadir <- tsize_final %>%
+  filter(AVISITN > 0 & !is.na(AVAL)) %>%
+  group_by(STUDYID, USUBJID) %>%
+  filter(AVAL == min(AVAL, na.rm = TRUE)) %>%
+  slice(1) %>%
+  ungroup() %>%
+  mutate(
+    PARAMCD = "NADIR",
+    PARAM = "Nadir Tumor Size",
+    PARAMN = 3,
+    NADIR = AVAL,
+    NADPCHG = PCHG, # Keep PCHG at nadir
+    NADVST = AVISIT # Keep visit of nadir
+  )
+
+# ---- Combine parameters
+
+adtrr_base <- bind_rows(
+  tsize_final,
+  bor,
+  nadir
+) %>%
+  arrange(USUBJID, PARAMN, AVISITN)
+
+# ---- Add analysis variables
+
+# Ensure AVALU exists before mutating
+if (!"AVALU" %in% names(adtrr_base)) {
+  adtrr_base <- adtrr_base %>%
+    mutate(AVALU = NA_character_)
+}
+
+adtrr <- adtrr_base %>%
+  # Analysis flags
+  mutate(
+    # Baseline flag
+    ABLFL = case_when(
+      !is.na(ABLFL) ~ ABLFL,
+      !is.na(AVISITN) & AVISITN == 0 ~ "Y",
+      TRUE ~ ""
+    ),
+
+    # Post-baseline flag
+    ANL01FL = if_else(!is.na(AVISITN) & AVISITN > 0, "Y", ""),
+
+    # Responders (CR or PR)
+    ANL02FL = if_else(!is.na(AVALC) & AVALC %in% c("CR", "PR"), "Y", ""),
+
+    # Has change from baseline
+    ANL03FL = if_else(!is.na(PCHG), "Y", "")
+  ) %>%
+  # Parameter categories
+  mutate(
+    PARCAT1 = "TUMOR RESPONSE",
+    PARCAT2 = case_when(
+      PARAMCD == "TSIZE" ~ "MEASUREMENT",
+      PARAMCD == "BOR" ~ "OVERALL RESPONSE",
+      PARAMCD == "NADIR" ~ "BEST RESPONSE",
+      TRUE ~ NA_character_
+    )
+  ) %>%
+  # Set AVALU (now guaranteed to exist)
+  mutate(
+    AVALU = case_when(
+      !is.na(AVALU) & AVALU != "" ~ AVALU, # Keep existing non-empty
+      PARAMCD == "TSIZE" ~ "mm",
+      PARAMCD == "NADIR" ~ "mm",
+      TRUE ~ NA_character_
+    )
+  ) %>%
+  # Sequence number
+  derive_var_obs_number(
+    by_vars = exprs(STUDYID, USUBJID),
+    order = exprs(PARAMN, AVISITN),
+    new_var = ASEQ,
+    check_type = "error"
+  ) %>%
+  select(-DOMAIN, -all_of(c(starts_with("RS"), starts_with("TR"), starts_with("TU")))) %>%
+  arrange(USUBJID, PARAMN, AVISITN)
+
+# Save output ----
+
+# Change to whichever directory you want to save the dataset in
+dir <- tools::R_user_dir("admiralonco_templates_data", which = "cache")
+if (!file.exists(dir)) {
+  # Create the folder
+  dir.create(dir, recursive = TRUE, showWarnings = FALSE)
+}
+save(adtrr, file = file.path(dir, "adtrr.rda"), compress = "bzip2")