diff --git a/adam/ader.R b/adam/ader.R
new file mode 100644
index 00000000..9617966c
--- /dev/null
+++ b/adam/ader.R
@@ -0,0 +1,290 @@
+## ----r echo=TRUE, message=FALSE-----------------------------------------------
+# Load 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)
+library(metacore)
+library(metatools)
+library(xportr)
+
+## ----r echo=TRUE, message=FALSE-----------------------------------------------
+# ---- Load Specs for Metacore ----
+metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>%
+  select_dataset("ADER")
+
+## ----r------------------------------------------------------------------------
+# ---- Load source datasets ----
+# Load ADRS, ADTTE, ADSL, ADLB, ADVS, ADEX, ADPP and ADAE
+adrs <- pharmaverseadam::adrs_onco
+adtte <- pharmaverseadam::adtte_onco
+adsl <- pharmaverseadam::adsl
+adlb <- pharmaverseadam::adlb
+advs <- pharmaverseadam::advs
+adex <- pharmaverseadam::adex %>%
+  filter(PARCAT1 == "INDIVIDUAL")
+adpp <- pharmaverseadam::adpp
+adae <- pharmaverseadam::adae
+
+## ----r------------------------------------------------------------------------
+# ---- Derive Covariates ----
+# Include numeric values for STUDYIDN, USUBJIDN, SEXN, RACEN etc.
+
+covar <- adsl %>%
+  create_var_from_codelist(metacore, input_var = STUDYID, out_var = STUDYIDN) %>%
+  create_var_from_codelist(metacore, input_var = SEX, out_var = SEXN) %>%
+  create_var_from_codelist(metacore, input_var = RACE, out_var = RACEN) %>%
+  create_var_from_codelist(metacore, input_var = ETHNIC, out_var = ETHNICN) %>%
+  create_var_from_codelist(metacore, input_var = ARMCD, out_var = COHORT) %>%
+  create_var_from_codelist(metacore, input_var = ARMCD, out_var = COHORTC) %>%
+  create_var_from_codelist(metacore, input_var = ARM, out_var = ARMN) %>%
+  create_var_from_codelist(metacore, input_var = ACTARM, out_var = ACTARMN) %>%
+  create_var_from_codelist(metacore, input_var = COUNTRY, out_var = COUNTRYN) %>%
+  create_var_from_codelist(metacore, input_var = COUNTRY, out_var = COUNTRYL) %>%
+  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),
+    ROUTE = unique(adex$EXROUTE)[1],
+    FORM = unique(adex$EXDOSFRM)[1],
+    REGION1 = COUNTRY,
+    REGION1N = COUNTRYN,
+  ) %>%
+  create_var_from_codelist(metacore, input_var = FORM, out_var = FORMN) %>%
+  create_var_from_codelist(metacore, input_var = ROUTE, out_var = ROUTEN)
+
+## ----r------------------------------------------------------------------------
+# ---- 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)
+
+## ----r------------------------------------------------------------------------
+# ---- Add Exposure Metrics  ----
+covar_auc <- covar_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)
+
+## ----r------------------------------------------------------------------------
+# ---- 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
+  )
+
+## ----r------------------------------------------------------------------------
+# ---- Add ADRS data ----
+# Add response date to ADSL for duration of response calculation
+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)
+  )
+
+## ----r------------------------------------------------------------------------
+# ---- Add Sequence Number ----
+ader_aseq <- ader_bor %>%
+  derive_var_obs_number(
+    by_vars = get_admiral_option("subject_keys"),
+    check_type = "error"
+  )
+
+## ----r------------------------------------------------------------------------
+# Combine covariates with ADER data
+
+ader_prefinal <- ader_aseq %>%
+  derive_vars_merged(
+    dataset_add = covar_auc,
+    by_vars = exprs(STUDYID, USUBJID)
+  )
+
+## ----r------------------------------------------------------------------------
+ader <- ader_prefinal %>%
+  drop_unspec_vars(metacore) %>% # Drop unspecified variables from specs
+  check_variables(metacore) %>% # Check all variables specified are present and no more
+  check_ct_data(metacore) %>% # Checks all variables with CT only contain values within the CT
+  order_cols(metacore) %>% # Orders the columns according to the spec
+  sort_by_key(metacore) # Sorts the rows by the sort keys
+
+## ----r------------------------------------------------------------------------
+dir <- tempdir() # Change to whichever directory you want to save the dataset in
+
+ader_xpt <- ader %>%
+  xportr_type(metacore, domain = "ADER") %>% # Coerce variable type to match spec
+  xportr_length(metacore) %>% # Assigns SAS length from a variable level metadata
+  xportr_label(metacore) %>% # Assigns variable label from metacore specifications
+  xportr_format(metacore) %>% # Assigns variable format from metacore specifications
+  xportr_df_label(metacore) %>% # Assigns dataset label from metacore specifications
+  xportr_write(file.path(dir, "ader.xpt")) # Write xpt v5 transport file
+
+## ----r echo=TRUE, message=FALSE-----------------------------------------------
+metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>%
+  select_dataset("ADEE")
+
+## ----r------------------------------------------------------------------------
+# ---- Create adee base dataset
+
+# Get variable names from both datasets
+adsl_vars <- names(adsl)
+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"))
+
+# 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
+      )
+    )
+}
+
+# ---- Create ADEE Base
+
+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))
+
+
+## ----r------------------------------------------------------------------------
+# ---- Add Analysis variables
+
+adee_aseq <- 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(PARAMCD),
+    new_var = ASEQ,
+    check_type = "error"
+  )
+
+
+## ----r------------------------------------------------------------------------
+# Combine covariates with ADER data
+
+adee_prefinal <- adee_aseq %>%
+  derive_vars_merged(
+    dataset_add = covar_auc,
+    by_vars = exprs(STUDYID, USUBJID)
+  )
+
+## ----r------------------------------------------------------------------------
+adee <- adee_prefinal %>%
+  drop_unspec_vars(metacore) %>% # Drop unspecified variables from specs
+  check_variables(metacore) %>% # Check all variables specified are present and no more
+  check_ct_data(metacore) %>% # Checks all variables with CT only contain values within the CT
+  order_cols(metacore) %>% # Orders the columns according to the spec
+  sort_by_key(metacore) # Sorts the rows by the sort keys
+
+## ----r------------------------------------------------------------------------
+dir <- tempdir() # Change to whichever directory you want to save the dataset in
+
+adee_xpt <- adee %>%
+  xportr_type(metacore, domain = "ADEE") %>% # Coerce variable type to match spec
+  xportr_length(metacore) %>% # Assigns SAS length from a variable level metadata
+  xportr_label(metacore) %>% # Assigns variable label from metacore specifications
+  xportr_format(metacore) %>% # Assigns variable format from metacore specifications
+  xportr_df_label(metacore) %>% # Assigns dataset label from metacore specifications
+  xportr_write(file.path(dir, "adee.xpt")) # Write xpt v5 transport file
+
+## ----r------------------------------------------------------------------------
+# [Similar structure...]
+
+## ----r------------------------------------------------------------------------
+# [Similar structure...]
diff --git a/adam/ader.qmd b/adam/ader.qmd
new file mode 100644
index 00000000..57d31e7a
--- /dev/null
+++ b/adam/ader.qmd
@@ -0,0 +1,474 @@
+---
+title: "Exposure-Response"
+order: 12
+---
+
+```{r setup script, include=FALSE, purl=FALSE}
+invisible_hook_purl <- function(before, options, ...) {
+  knitr::hook_purl(before, options, ...)
+  NULL
+}
+knitr::knit_hooks$set(purl = invisible_hook_purl)
+source("functions/print_df.R")
+```
+
+# Introduction
+
+Exposure-Response (ER) modeling is a critical tool in drug development for evaluating the relationships between drug exposure, safety, and efficacy. These analyses help characterize dose-response relationships, optimize dosing regimens, and support regulatory decision-making.
+
+While CDISC released the Population Pharmacokinetic (PopPK) Implementation Guide in 2023 (see [Basic Data Structure for ADaM Population PK](https://www.cdisc.org/standards/foundational/adam/basic-data-structure-adam-poppk-implementation-guide-v1-0)), no equivalent standards currently exist specifically for ER data. However, ER datasets share many structural similarities with PopPK datasets, including numeric covariates, relative time variables, and pharmacokinetic exposure metrics. The examples below demonstrate how PopPK principles can be extended to create standardized ER analysis datasets.
+
+The following four specialized datasets support different aspects of exposure-response analysis:
+
+| Dataset | Full Name | Primary Purpose | Key Features |
+|------------------|------------------|-------------------|------------------|
+| **ADER** | Exposure-Response Analysis Dataset | Integrated exposure-response relationships across safety and efficacy endpoints | Combines PK metrics (AUC, Cmax) with time-to-event outcomes (OS, PFS), tumor response, and baseline covariates |
+| **ADEE** | Exposure-Efficacy Analysis Dataset | Exposure relationships with efficacy and tumor response endpoints | Focuses on efficacy outcomes such as tumor measurements, response rates, and progression metrics linked to drug exposure |
+| **ADES** | Exposure-Safety Analysis Dataset | Exposure relationships with adverse events and safety endpoints | Links exposure metrics to adverse event occurrence, severity, and time-to-onset for safety signal detection |
+| **ADTRR** | Exposure-Tumor Response Rate Analysis Dataset | Exposure relationships with categorical tumor response metrics | Analyzes exposure impact on response categories (CR, PR, SD, PD) and best overall response |
+
+Each dataset builds upon standard ADaM datasets (ADSL, ADRS, ADTTE, ADAE, ADLB, ADVS) and incorporates pharmacokinetic parameters from ADPC/ADPP to create analysis-ready datasets for exposure-response modeling.
+
+## First Load Packages
+
+First we will load the packages required for our project. We will use `{admiral}` and `{admiralonco}` for the creation of analysis data. We will source these from `{pharmaverseadam}`. `{admiral}` requires `{dplyr}`, `{lubridate}` and `{stringr}`. Find other `{admiral}` functions and related variables by searching [admiraldiscovery](https://pharmaverse.github.io/admiraldiscovery/articles/reactable.html). We will use `{metacore}` and `{metatools}` to store and manipulate metadata from our specifications. We will use `{xportr}` to perform checks on the final data and export to a transport file.
+
+Exposure Response data typically use ADaM data as source, so this example will depend on `{pharmaverseadam}` with data from existing `{admiral}` and `{admiralonco}` templates.
+
+```{r echo=TRUE, message=FALSE}
+#| label: Load Packages
+# Load 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)
+library(metacore)
+library(metatools)
+library(xportr)
+```
+
+## Next Load Specifications for Metacore
+
+We have saved our specifications in an Excel file and will load them into `{metacore}` with the `metacore::spec_to_metacore()` function.
+
+```{r echo=TRUE, message=FALSE}
+#| label: Load Specs
+#| warning: false
+# ---- Load Specs for Metacore ----
+metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>%
+  select_dataset("ADER")
+```
+
+## Load Source Datasets
+
+We will load are ADaM data from `{pharmaverseadam}`. The main source will be `ADRS` and `ADTTE`. We will use `ADSL` for baseline characteristics and we will derive additional baselines from vital signs `ADVS` and laboratory data `ADLB`.
+
+```{r}
+#| label: Load Source
+# ---- Load source datasets ----
+# Load ADRS, ADTTE, ADSL, ADLB, ADVS, ADEX, ADPP and ADAE
+adrs <- pharmaverseadam::adrs_onco
+adtte <- pharmaverseadam::adtte_onco
+adsl <- pharmaverseadam::adsl
+adlb <- pharmaverseadam::adlb
+advs <- pharmaverseadam::advs
+adex <- pharmaverseadam::adex %>%
+  filter(PARCAT1 == "INDIVIDUAL")
+adpp <- pharmaverseadam::adpp
+adae <- pharmaverseadam::adae
+```
+
+## Common Derivations
+
+We will include common derivations for all exposure-response datasets first. These include covariates and exposure metrics.
+
+## Derive Covariates Using `{metatools}`
+
+In this step we will create our numeric covariates using the `metatools::create_var_from_codelist()` function.
+
+```{r}
+#| label: Common Covariates
+# ---- Derive Covariates ----
+# Include numeric values for STUDYIDN, USUBJIDN, SEXN, RACEN etc.
+
+covar <- adsl %>%
+  create_var_from_codelist(metacore, input_var = STUDYID, out_var = STUDYIDN) %>%
+  create_var_from_codelist(metacore, input_var = SEX, out_var = SEXN) %>%
+  create_var_from_codelist(metacore, input_var = RACE, out_var = RACEN) %>%
+  create_var_from_codelist(metacore, input_var = ETHNIC, out_var = ETHNICN) %>%
+  create_var_from_codelist(metacore, input_var = ARMCD, out_var = COHORT) %>%
+  create_var_from_codelist(metacore, input_var = ARMCD, out_var = COHORTC) %>%
+  create_var_from_codelist(metacore, input_var = ARM, out_var = ARMN) %>%
+  create_var_from_codelist(metacore, input_var = ACTARM, out_var = ACTARMN) %>%
+  create_var_from_codelist(metacore, input_var = COUNTRY, out_var = COUNTRYN) %>%
+  create_var_from_codelist(metacore, input_var = COUNTRY, out_var = COUNTRYL) %>%
+  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),
+    ROUTE = unique(adex$EXROUTE)[1],
+    FORM = unique(adex$EXDOSFRM)[1],
+    REGION1 = COUNTRY,
+    REGION1N = COUNTRYN,
+  ) %>%
+  create_var_from_codelist(metacore, input_var = FORM, out_var = FORMN) %>%
+  create_var_from_codelist(metacore, input_var = ROUTE, out_var = ROUTEN)
+```
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(covar %>% select(USUBJID, SEX, SEXN, RACE, RACEN, FORM, FORMN))
+```
+
+### Derive Additional Baselines
+
+Next we add additional baselines from vital signs and laboratory data.
+
+```{r}
+#| label: Baselines
+# ---- 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)
+```
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(covar_vslb %>% select(USUBJID, WTBL, HTBL, BMIBL, CRCLBL))
+```
+
+## Add Exposure Metrics from `ADPP`
+
+An essential component of ER modeling is the exposure metrics calculated from the PK concentration data in `ADPC` or `ADPPK`. Here we will use `AUCLST` and `CMAX` from the `ADPP` template from `{admiral}` in `{pharmaverseadam}`. See the `{aNCA}` package for details about calculating `AUC`, `CMAX`, and other parameters.
+
+```{r}
+#| label: AUCSS
+# ---- Add Exposure Metrics  ----
+covar_auc <- covar_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)
+```
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(covar_auc %>% select(
+  USUBJID, TRT01A, SEX, WTBL, HTBL, AUCSS, CMAXSS
+))
+```
+
+# Code for Individual Datasets
+
+Derivations specific to each dataset continue below. Select the appropriate tab to view.
+
+## Dataset-Specific Derivations
+
+::: {.panel-tabset}
+
+## ADER
+
+### Derive Time to Event Variables from `ADTTE`
+
+We will use parameters from from `ADTTE` for Overall Survival, Progression Free Survival and Duration of Response. We will use `admiral::derive_vars_transposed()` to transpose the `AVAL` for each `PARAMCD`.
+
+```{r}
+#| label: ADER Add ADTTE variables
+# ---- 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
+  )
+```
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(ader_tte %>% select(USUBJID, OS, OSIND, PFS, PFSIND, RSD, RSDIND))
+```
+
+We will use Best Overall Response by Investigator (BOR) from `ADRS`. We attach this to the data with `admiral::derive_vars_merged()`. Note that `derive_vars_transposed()` and `derive_vars_merged()` will be our primary ways of combining data from the different `{admiral}` and `{admiralonco}` templates.
+
+```{r}
+#| label: ADER Add ADRS
+# ---- Add ADRS data ----
+# Add response date to ADSL for duration of response calculation
+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 Analysis Sequence Number using `admiral::derive_var_obs_number()`.
+
+```{r}
+#| label: ADER ASEQ
+# ---- Add Sequence Number ----
+ader_aseq <- ader_bor %>%
+  derive_var_obs_number(
+    by_vars = get_admiral_option("subject_keys"),
+    check_type = "error"
+  )
+```
+
+```{r eval=TRUE, echo=FALSE, purl=FALSE}
+print_df(ader_bor %>% select(
+  USUBJID, BOR, BORC
+))
+```
+
+### Combine with Covariates
+
+We combine our covariates with the rest of the data
+
+```{r}
+#| label: ADER Combine with Covariates
+# Combine covariates with ADER data
+
+ader_prefinal <- ader_aseq %>%
+  derive_vars_merged(
+    dataset_add = covar_auc,
+    by_vars = exprs(STUDYID, USUBJID)
+  )
+```
+
+### Check Data With metacore and metatools
+
+We use `{metacore}` objects with `{metatools}` functions to perform a number of checks on the data. We will drop variables not in the specs and make sure all the variables from the specs are included.
+
+```{r}
+#| label: ADER Metacore
+#| warning: false
+
+ader <- ader_prefinal %>%
+  drop_unspec_vars(metacore) %>% # Drop unspecified variables from specs
+  check_variables(metacore) %>% # Check all variables specified are present and no more
+  check_ct_data(metacore) %>% # Checks all variables with CT only contain values within the CT
+  order_cols(metacore) %>% # Orders the columns according to the spec
+  sort_by_key(metacore) # Sorts the rows by the sort keys
+```
+
+### Apply Labels and Formats with xportr
+
+Using {xportr} we check variable type, assign variable length, add variable labels, add variable formats, and save a transport file with `xportr::xportr_write()`.
+
+```{r}
+#| label: ADER xportr
+dir <- tempdir() # Change to whichever directory you want to save the dataset in
+
+ader_xpt <- ader %>%
+  xportr_type(metacore, domain = "ADER") %>% # Coerce variable type to match spec
+  xportr_length(metacore) %>% # Assigns SAS length from a variable level metadata
+  xportr_label(metacore) %>% # Assigns variable label from metacore specifications
+  xportr_format(metacore) %>% # Assigns variable format from metacore specifications
+  xportr_df_label(metacore) %>% # Assigns dataset label from metacore specifications
+  xportr_write(file.path(dir, "ader.xpt")) # Write xpt v5 transport file
+```
+
+## ADEE
+
+### Load Specifications
+```{r echo=TRUE, message=FALSE}
+#| label: ADEE Load Specs
+#| warning: false
+metacore <- spec_to_metacore("./metadata/pk_spec.xlsx") %>%
+  select_dataset("ADEE")
+```
+
+```{r}
+#| label: ADEE Base dataset
+# ---- Create adee base dataset
+
+# Get variable names from both datasets
+adsl_vars <- names(adsl)
+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"))
+
+# 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
+      )
+    )
+}
+
+# ---- Create ADEE Base
+
+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))
+```
+
+### Add Analysis Variables
+
+```{r}
+#| label: ADEE Analysis variables
+# ---- Add Analysis variables
+
+adee_aseq <- 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(PARAMCD),
+    new_var = ASEQ,
+    check_type = "error"
+  )
+```
+
+### Combine with Covariates and Exposure
+
+We combine our covariates with the rest of the data
+
+```{r}
+#| label: ADEE Combine with Covariates
+# Combine covariates with ADER data
+
+adee_prefinal <- adee_aseq %>%
+  derive_vars_merged(
+    dataset_add = covar_auc,
+    by_vars = exprs(STUDYID, USUBJID)
+  )
+```
+
+
+
+### Check Data With metacore and metatools
+
+We use `{metacore}` objects with `{metatools}` functions to perform a number of checks on the data. We will drop variables not in the specs and make sure all the variables from the specs are included.
+
+```{r}
+#| label: Metacore
+#| warning: false
+
+adee <- adee_prefinal %>%
+  drop_unspec_vars(metacore) %>% # Drop unspecified variables from specs
+  check_variables(metacore) %>% # Check all variables specified are present and no more
+  check_ct_data(metacore) %>% # Checks all variables with CT only contain values within the CT
+  order_cols(metacore) %>% # Orders the columns according to the spec
+  sort_by_key(metacore) # Sorts the rows by the sort keys
+```
+
+### Apply Labels and Formats with xportr
+
+Using {xportr} we check variable type, assign variable length, add variable labels, add variable formats, and save a transport file with `xportr::xportr_write()`.
+
+```{r}
+#| label: xportr
+dir <- tempdir() # Change to whichever directory you want to save the dataset in
+
+adee_xpt <- adee %>%
+  xportr_type(metacore, domain = "ADEE") %>% # Coerce variable type to match spec
+  xportr_length(metacore) %>% # Assigns SAS length from a variable level metadata
+  xportr_label(metacore) %>% # Assigns variable label from metacore specifications
+  xportr_format(metacore) %>% # Assigns variable format from metacore specifications
+  xportr_df_label(metacore) %>% # Assigns dataset label from metacore specifications
+  xportr_write(file.path(dir, "adee.xpt")) # Write xpt v5 transport file
+```
+
+
+## ADES
+
+```{r}
+# [Similar structure...]
+```
+
+## ADTRR
+
+```{r}
+# [Similar structure...]
+```
+
+
+:::
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 2c098312..4f050385 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -3,6 +3,8 @@ ABLFL
 aCRF
 ACTARM
 ACTARMCD
+ACTARMN
+ACTARMN
 adae
 ADAE
 adam
@@ -12,6 +14,12 @@ ADaMs
 addadsl
 ADDL
 addparams
+ADEE
+ader
+ADER
+ADES
+adex
+ADEX
 adlb
 ADLB
 admiraldiscovery
@@ -20,6 +28,8 @@ admiralophtha
 admiralvaccine
 adpc
 ADPC
+adpp
+ADPP
 adppk
 ADPPK
 ADRG
@@ -32,8 +42,10 @@ adslvars
 ADT
 ADTF
 ADTM
-adtte
+ADTRR
 ADTTE
+adtte
+adttei
 ADURN
 ADURU
 advs
@@ -43,8 +55,8 @@ ae
 AE
 AEACN
 AEBDSYCD
-aebodsys
 AEBODSYS
+aebodsys
 AEDECOD
 AEDIS
 AEDTC
@@ -86,8 +98,8 @@ AESOD
 AESTDAT
 AESTDTC
 AESTDY
-AETERM
 aeterm
+AETERM
 AETHNIC
 AETHNICN
 AFRLT
@@ -99,6 +111,8 @@ Alanine
 ALLOQ
 AMBUL
 Aminotransferase
+analyte
+Analyte
 aNCA
 anl
 ANL
@@ -106,23 +120,22 @@ anldates
 anldur
 anlflags
 ANLzzFL
-analyte
-Analyte
+ANRHI
 anrind
 ANRIND
-ANRHI
 ANRLO
 AOCCIFL
 APPPK
 Appsilon
-aprlt
 APRLT
+aprlt
 ard
 ARD
 ARDs
 arg
 args
 ARMCD
+ARMN
 arrlt
 ARRLT
 articlehere
@@ -140,17 +153,20 @@ ATPT
 ATPTN
 ATPTREF
 attr
+auc
+AUCLST
+AUCSS
 autofit
 autolog
 autoslider
 autoslideR
 AutoslideR
-aval
 AVAL
+aval
 AVALC
 AVALCA
-avalcat
 AVALCAT
+avalcat
 AVALCATy
 AVALU
 avalvars
@@ -166,9 +182,9 @@ BASETYPE
 basetypes
 bds
 BDS
+bigN
 BILI
 BILIBL
-bigN
 BLAS
 blq
 BLQ
@@ -181,6 +197,9 @@ BMIBL
 BMIBLU
 BMRKR
 BNRIND
+bor
+BOR
+BORC
 br
 bsa
 BSABL
@@ -192,15 +211,19 @@ cairoFT
 callr
 callout
 categorizationvars
-cdisc
 CDISC
+cdisc
 CDISCPILOT
 chgpchg
 CKD
 cli
 clst
+Cmax
+CMAX
+CMAXSS
 CMT
 CNSDTDSC
+CNSR
 codebases
 codelist
 codelists
@@ -239,11 +262,11 @@ datacutr
 dataname
 datanames
 datasetjson
-datetime
 Datetime
+datetime
 datetimes
-Davide
 davidblair
+Davide
 de
 DEATHDT
 deathvars
@@ -268,10 +291,10 @@ DM
 DMDTC
 DMDY
 dmvars
+docx
 DOSEA
 DOSEP
 DOSEU
-docx
 dplyr
 ds
 DS
@@ -298,8 +321,8 @@ DTHDTC
 DTHDTF
 DTHFL
 DTHSEQ
-dtm
 DTM
+dtm
 dtype
 DTYPE
 DV
@@ -310,9 +333,9 @@ dy
 dylib
 DYTPE
 ec
-eCTD
 ECENDAT
 ECSTDAT
+eCTD
 ectd
 edelarua
 eg
@@ -327,6 +350,7 @@ EPI
 esub
 eSub
 eSubmission
+ETHNICN
 eval
 Evaluable
 EVID
@@ -435,8 +459,8 @@ libRblas
 libRlapack
 lifecycle
 lineplot
-linter
 Linter
+linter
 lintr
 lintR
 lm
@@ -469,8 +493,8 @@ Metacore
 metatools
 miniUI
 minumum
-mmm
 mmHg
+mmm
 Mosteller
 MRRLT
 mtcars
@@ -494,10 +518,10 @@ nPK
 NPRLT
 nProtocol
 nr
-nrrlt
 NRRLT
-ns
+nrrlt
 Ns
+ns
 nSampling
 nTime
 num
@@ -510,10 +534,11 @@ occflags
 OID
 onco
 ONCO
-ontrtfl
 ONTRTFL
+ontrtfl
 ord
 os
+OSIND
 othgrpvars
 outfile
 outputId
@@ -528,6 +553,7 @@ param
 PARAM
 paramcd
 PARAMCD
+PARAMCDB
 PARAMN
 params
 paramval
@@ -536,8 +562,8 @@ PARCAT
 PATNUM
 pc
 PCDTC
-pchg
 PCHG
+pchg
 PCLLOQ
 PCRFTDT
 PCRFTDTM
@@ -552,8 +578,9 @@ PCTESTCD
 PCTPT
 PCTPTNUM
 pcts
-pfs
 PFS
+pfs
+PFSIND
 pharma
 pharmacokinetic
 Pharmacokinetic
@@ -572,11 +599,12 @@ plotOutput
 PN
 png
 poc
+PopPK
 pos
 POSIXct
 POSIXlt
-pptx
 PPTX
+pptx
 pre
 Pre
 preconfigured
@@ -602,8 +630,8 @@ QD
 qmd
 QNAM
 qtc
-racegr
 RACEGR
+racegr
 RACEN
 RACEx
 RANDDT
@@ -654,6 +682,8 @@ ROUTEN
 rowcounts
 RRLTU
 Rscript
+RSD
+RSDIND
 RSDTC
 RSEVAL
 RSSEQ
@@ -672,15 +702,15 @@ SASFL
 SCRFDT
 sd
 sdi
-sdtm
 SDTM
+sdtm
 sdtmchecks
 SDTMs
 selectInput
 sep
 SER
-sessioninfo
 sessionInfo
+sessioninfo
 setdiff
 setequal
 SEXN
@@ -739,7 +769,6 @@ tempfile
 TEMPLOC
 TESTCD
 tf
-tfrmt
 tformat
 tfrmt
 tgdt
@@ -749,8 +778,8 @@ tibble
 tidyr
 tidyselect
 tidyverse
-timepoint
 Timepoint
+timepoint
 timingvars
 TLG
 TLGs
@@ -768,15 +797,15 @@ traceback
 TRE
 treatmentvars
 tribble
-trt
 TRT
+trt
 TRTA
-trtdurd
 TRTDURD
+trtdurd
 TRTEDT
 TRTEDTM
-trtemfl
 TRTEMFL
+trtemfl
 TRTETMF
 TRTP
 TRTSDT
@@ -808,6 +837,7 @@ USUBJID
 USUBJIDN
 utf
 vapply
+vards
 varlabels
 vctrs
 viewerHeight
diff --git a/metadata/pk_spec.xlsx b/metadata/pk_spec.xlsx
index 00f2e241..18acc382 100644
Binary files a/metadata/pk_spec.xlsx and b/metadata/pk_spec.xlsx differ