diff --git a/.Rbuildignore b/.Rbuildignore
index 97b2c89..005c9d7 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -18,3 +18,4 @@
 ^README.Rmd$
 ^\.devcontainer$
 ^data-raw$
+^doc$
diff --git a/.gitignore b/.gitignore
index bbd195c..6bde539 100644
--- a/.gitignore
+++ b/.gitignore
@@ -50,3 +50,5 @@ admiraltemplate*.tgz
 
 # Docs
 docs/
+/doc/
+/Meta/
diff --git a/_pkgdown.yml b/_pkgdown.yml
index dd93285..b409e9b 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -68,8 +68,8 @@ navbar:
     articles:
       text: User Guides
       menu:
-      - text: Creating ADXX
-        href: articles/adxx.html
+      - text: Creating a Metabolic ADVS
+        href: articles/advs.html
     history:
       icon: fa-history
       href: articles/website-versions.html
diff --git a/inst/WORDLIST b/inst/WORDLIST
index ceb0c87..d583e2b 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -1,13 +1,20 @@
+ADSL
+ADVS
 ADXX
 ADaM
+ADaMs
 admiralci
 Arancibia
+BDS
 Biologics
+BMI
 CDISC
 Changelog
 DM
 Farrugia
 GSK
+HIPCIR
+WSTCIR
 LinkedIn
 Onboarding
 README
@@ -28,4 +35,4 @@ pharmaverse
 renv
 repo
 roxygen
-
+advs
diff --git a/inst/templates/ad_adxx.R b/inst/templates/ad_adxx.R
deleted file mode 100644
index 974fd20..0000000
--- a/inst/templates/ad_adxx.R
+++ /dev/null
@@ -1,20 +0,0 @@
-# Name: ADXX
-#
-# Label: XXX
-#
-# Input: xx, xx, xx
-
-library(admiral)
-library(pharmaversesdtm) # Contains example datasets from the CDISC pilot project
-
-# Add your template ADaM script code
-
-# Save output ----
-
-# Change to whichever directory you want to save the dataset in
-dir <- tools::R_user_dir("admiralmetabolic_templates_data", which = "cache")
-if (!file.exists(dir)) {
-  # Create the folder
-  dir.create(dir, recursive = TRUE, showWarnings = FALSE)
-}
-# save(adxx, file = file.path(dir, "adxx.rda"), compress = "bzip2")
diff --git a/staged_dependencies.yml b/staged_dependencies.yaml
similarity index 100%
rename from staged_dependencies.yml
rename to staged_dependencies.yaml
diff --git a/vignettes/advs.Rmd b/vignettes/advs.Rmd
new file mode 100644
index 0000000..001a5c3
--- /dev/null
+++ b/vignettes/advs.Rmd
@@ -0,0 +1,374 @@
+---
+title: "Creating a Metabolic ADVS ADaM"
+output: 
+  rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Creating a Metabolic ADVS ADaM}
+  %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+editor_options: 
+  markdown: 
+    wrap: 72
+---
+
+```{r setup, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+
+library(admiraldev)
+```
+
+# Introduction
+
+This article describes creating a vital signs ADaM for metabolic
+clinical trials.
+
+We advise you first consult the `{admiral}` [Creating a BDS Finding ADaM
+vignette](https://pharmaverse.github.io/admiral/articles/bds_finding.html).
+The programming workflow around creating the general set-up of an `ADVS`
+using `{admiral}` functions is the same. In this vignette, we focus on the most common
+endpoints and their derivations mainly found in metabolic trials to
+avoid repeating information and maintaining the same content in two
+places. As such, the code in this vignette is not completely executable; we recommend consulting the ADVS template script to view the full workflow.
+
+## Required Packages
+
+The examples of this vignette require the following packages.
+
+```{r, warning=FALSE, message=FALSE}
+library(admiral)
+library(admiralmetabolic)
+library(pharmaversesdtm)
+library(dplyr)
+```
+
+# Programming Workflow
+
+-   [Read in Data](#readdata)
+-   [Assign `PARAMCD`, `PARAM`, `PARAMN`, `PARCAT1`](#paramcd)
+-   [Derive Additional Parameters (e.g. `BMI` for `ADVS`)](#derive_param)
+-   [Common Metabolic Endpoints](#common_endpoints)
+    -   [Derive Categorization Variables (`AVALCATx`, `BASECATx`)](#cat)
+    -   [Derive Criterion Variables (`CRITy`, `CRITyFL`, `CRITyFLN`)](#crit_vars)
+-   [Remaining ADVS Set-up](#advs_end)
+
+## Read in Data {#readdata}
+
+To start, all data frames needed for the creation of `ADVS` should be
+loaded into the global environment. Reading data will usually be a company specific process, however, for the purpose of this vignette, we will use example data from `{pharmaversesdtm}` and `{admiral}`. We will utilize `DM`, `VS` and `ADSL` for the basis of `ADVS`.
+
+```{r, message=FALSE, warning=FALSE}
+dm_metabolic <- admiralmetabolic::dm_metabolic
+vs_metabolic <- admiralmetabolic::vs_metabolic
+admiral_adsl <- admiral::admiral_adsl
+
+dm <- convert_blanks_to_na(dm_metabolic)
+vs <- convert_blanks_to_na(vs_metabolic)
+admiral_adsl <- convert_blanks_to_na(admiral_adsl)
+```
+
+Within this vignette, `DM` is used as the basis for `ADSL`:
+
+```{r eval=TRUE}
+# Retrieve required variables from admiral ADSL for this vignette that are not present in DM dataset
+adsl <- dm %>%
+  select(-DOMAIN) %>%
+  mutate(TRT01P = ARM, TRT01A = ACTARM) %>%
+  left_join(admiral_adsl %>% select(USUBJID, TRTSDT, TRTEDT), by = "USUBJID")
+```
+
+The following steps are to merge `ADSL` variables with the source data and
+derive the usual `ADVS` analysis variables. Note that only the sections required for this vignette are covered in the following steps. To get a detailed guidance on all the steps, refer the `{admiral}` [Creating a BDS Finding ADaM vignette](https://pharmaverse.github.io/admiral/articles/bds_finding.html).
+
+```{r, eval=TRUE, include=FALSE}
+adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01P, TRT01A)
+
+advs <- derive_vars_merged(
+  vs,
+  dataset_add = adsl,
+  new_vars = adsl_vars,
+  by_vars = exprs(STUDYID, USUBJID)
+)
+
+advs <- derive_vars_dt(advs, new_vars_prefix = "A", dtc = VSDTC)
+
+advs <-
+  derive_vars_dy(advs, reference_date = TRTSDT, source_vars = exprs(ADT))
+```
+
+## Assign `PARAMCD`, `PARAM`, `PARAMN`, `PARCAT1` {#paramcd}
+
+The next step is to assign parameter level values such as `PARAMCD`, `PARAM`,
+`PARAMN`, `PARCAT1`, etc. For this, a lookup can be created based on
+the SDTM `--TESTCD` value to join to the source data.
+One key addition in metabolic trials are vital sign parameters associated to body measurements, such as `BMI`, `HIPCIR`, and `WSTCIR`.
+
+```{r, echo=TRUE, message=FALSE}
+param_lookup <- tribble(
+  ~VSTESTCD, ~PARAMCD, ~PARAM, ~PARAMN, ~PARCAT1, ~PARCAT1N,
+  "HEIGHT", "HEIGHT", "Height (cm)", 1, "Subject Characteristic", 1,
+  "WEIGHT", "WEIGHT", "Weight (kg)", 2, "Subject Characteristic", 1,
+  "BMI", "BMI", "Body Mass Index(kg/m^2)", 3, "Subject Characteristic", 1,
+  "HIPCIR", "HIPCIR", "Hip Circumference (cm)", 4, "Subject Characteristic", 1,
+  "WSTCIR", "WSTCIR", "Waist Circumference (cm)", 5, "Subject Characteristic", 1,
+  "DIABP", "DIABP", "Diastolic Blood Pressure (mmHg)", 6, "Vital Sign", 2,
+  "PULSE", "PULSE", "Pulse Rate (beats/min)", 7, "Vital Sign", 2,
+  "SYSBP", "SYSBP", "Systolic Blood Pressure (mmHg)", 8, "Vital Sign", 2,
+  "TEMP", "TEMP", "Temperature (C)", 9, "Vital Sign", 2
+)
+```
+
+This lookup may now be joined to the source data and this is how the
+parameters will look like:
+
+```{r, eval=TRUE, include=TRUE, message=FALSE}
+advs <- derive_vars_merged_lookup(
+  advs,
+  dataset_add = param_lookup,
+  new_vars = exprs(PARAMCD, PARAM, PARAMN, PARCAT1, PARCAT1N),
+  by_vars = exprs(VSTESTCD)
+)
+```
+
+```{r, eval=TRUE, echo=FALSE}
+advs_param <- distinct(advs, USUBJID, PARAMCD, VSTESTCD, PARAM, PARCAT1, PARCAT1N)
+
+dataset_vignette(advs_param, display_vars = exprs(USUBJID, VSTESTCD, PARAMCD, PARAM, PARCAT1, PARCAT1N))
+```
+
+
+```{r, eval=TRUE, include=FALSE}
+advs <- mutate(
+  advs,
+  AVAL = VSSTRESN
+)
+```
+
+## Derive Additional Parameters (e.g. `BMI` for `ADVS`) {#derive_param}
+
+In metabolic trials, `BMI` is often calculated at source. But while creating the `ADVS`
+dataset, we re-derive `BMI` from the collected height and weight values.
+This is done to ensure that the `BMI` is calculated consistently across
+all subjects and visits.
+
+In this step, we create parameter Body Mass Index (`BMI`) for the `ADVS`
+domain using the `derive_param_bmi()` function. Note that only variables
+specified in the `by_vars` argument will be populated in the newly
+created records. Also note that if height is collected only once for a
+subject use `constant_by_vars` to specify the function to merge by the
+subject-level variable - otherwise BMI is only calculated for visits
+where both are collected.
+
+```{r eval=TRUE}
+# Removing BMI collected at source from the dataset
+advs <- advs %>% filter(!VSTESTCD == "BMI")
+
+advs <- derive_param_bmi(
+  advs,
+  by_vars = c(
+    get_admiral_option("subject_keys"),
+    exprs(!!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM)
+  ),
+  set_values_to = exprs(
+    PARAMCD = "BMI",
+    PARAM = "Body Mass Index (kg/m^2)",
+    PARAMN = 3,
+    PARCAT1 = "Subject Characteristic",
+    PARCAT1N = 1
+  ),
+  get_unit_expr = VSSTRESU,
+  filter = VSSTAT != "NOT DONE" | is.na(VSSTAT),
+  constant_by_vars = exprs(USUBJID)
+)
+```
+
+```{r, eval=TRUE, echo=FALSE}
+dataset_vignette(
+  arrange(advs, USUBJID, VISITNUM, VSTPTNUM, ADT, PARAMCD),
+  display_vars = exprs(USUBJID, VSTESTCD, PARAMCD, PARAM, VISIT, AVAL),
+  filter = PARAMCD %in% c("BMI")
+)
+```
+
+```{r eval=TRUE, include=FALSE}
+advs <- restrict_derivation(
+  advs,
+  derivation = derive_var_extreme_flag,
+  args = params(
+    by_vars = c(get_admiral_option("subject_keys"), exprs(PARAMCD)),
+    order = exprs(ADT, VSTPTNUM, VISITNUM),
+    new_var = ABLFL,
+    mode = "last"
+  ),
+  filter = (!is.na(AVAL) & ADT <= TRTSDT)
+)
+
+advs <- derive_var_base(
+  advs,
+  by_vars = c(get_admiral_option("subject_keys"), exprs(PARAMCD)),
+  source_var = AVAL,
+  new_var = BASE
+)
+
+advs <- derive_var_chg(advs)
+
+advs <- derive_var_pchg(advs)
+```
+
+## Common Metabolic Endpoints {#common_endpoints}
+
+In the following sections, we will explore some of the most common
+endpoints typically observed in metabolic trials.
+
+One such endpoint is the improvement in weight category from baseline to
+the end of treatment, which is often assessed using Body Mass Index
+(`BMI`). To capture this, we will derive variables such as `AVALCATy`
+and `BASECATy`, as detailed in the subsequent section.
+
+Additionally, the achievement of weight reduction thresholds, such as ≥
+5%, ≥ 10%, or ≥ 15% from baseline to end of treatment or at a certain visit,
+is a common endpoint in metabolic trials. To accommodate these criteria,
+we will derive relevant criterion variables such as `CRITy`, `CRITyFL`,
+and `CRITyFLN`, with the necessary functions provided by `{admiral}`
+outlined below.
+
+### Derive Categorization Variables (`AVALCATx`, `BASECATx`) {#cat}
+
+`{admiral}` does not currently have a generic function to aid in
+assigning `AVALCATy`/ `AVALCAvN` and `BASECATy`/ `BASECAvN` values.
+Below is a simple example of how these values may be assigned:
+
+```{r eval=TRUE}
+avalcat_lookup <- tibble::tribble(
+  ~PARAMCD, ~AVALCA1N, ~AVALCAT1,
+  "BMI",    1,         "Underweight",
+  "BMI",    2,         "Normal weight",
+  "BMI",    3,         "Overweight",
+  "BMI",    4,         "Obesity class I",
+  "BMI",    5,         "Obesity class II",
+  "BMI",    6,         "Obesity class III",
+  "BMI",    NA,        NA_character_
+)
+
+format_avalcat1n <- function(param, aval) {
+  case_when(
+    param == "BMI" & aval < 18.5 ~ 1,
+    param == "BMI" & aval >= 18.5 & aval < 25 ~ 2,
+    param == "BMI" & aval >= 25 & aval < 30 ~ 3,
+    param == "BMI" & aval >= 30 & aval < 35 ~ 4,
+    param == "BMI" & aval >= 35 & aval < 40 ~ 5,
+    param == "BMI" & aval >= 40 ~ 6,
+    TRUE ~ NA_real_
+  )
+}
+
+advs <- advs %>%
+  mutate(AVALCA1N = format_avalcat1n(param = PARAMCD, aval = AVAL)) %>%
+  derive_vars_merged(
+    avalcat_lookup,
+    by = exprs(PARAMCD, AVALCA1N)
+  )
+```
+
+```{r eval=TRUE, echo=FALSE}
+dataset_vignette(
+  advs,
+  display_vars = exprs(USUBJID, PARAMCD, VISIT, AVAL, AVALCA1N, AVALCAT1),
+  filter = PARAMCD == "BMI"
+)
+```
+
+In a similar way, we will create `BASECATy`/ `BASECAvN` variables.
+
+```{r eval=TRUE}
+basecat_lookup <- tibble::tribble(
+  ~PARAMCD, ~BASECA1N, ~BASECAT1,
+  "BMI",    1,         "Underweight",
+  "BMI",    2,         "Normal weight",
+  "BMI",    3,         "Overweight",
+  "BMI",    4,         "Obesity class I",
+  "BMI",    5,         "Obesity class II",
+  "BMI",    6,         "Obesity class III",
+  "BMI",    NA,        NA_character_
+)
+
+format_basecat1n <- function(param, base) {
+  case_when(
+    param == "BMI" & base < 18.5 ~ 1,
+    param == "BMI" & base >= 18.5 & base < 25 ~ 2,
+    param == "BMI" & base >= 25 & base < 30 ~ 3,
+    param == "BMI" & base >= 30 & base < 35 ~ 4,
+    param == "BMI" & base >= 35 & base < 40 ~ 5,
+    param == "BMI" & base >= 40 ~ 6,
+    TRUE ~ NA_real_
+  )
+}
+advs <- advs %>%
+  mutate(BASECA1N = format_basecat1n(param = PARAMCD, base = BASE)) %>%
+  derive_vars_merged(
+    basecat_lookup,
+    by = exprs(PARAMCD, BASECA1N)
+  )
+```
+
+```{r, eval=TRUE, echo=FALSE}
+dataset_vignette(
+  advs,
+  display_vars = exprs(USUBJID, PARAMCD, VISIT, AVAL, BASE, ABLFL, BASECA1N, BASECAT1),
+  filter = PARAMCD == "BMI"
+)
+```
+
+### Derive Criterion Variables (`CRITy`, `CRITyFL`, `CRITyFLN`) {#crit_vars}
+
+For deriving criterion variables (`CRITy`, `CRITyFL`, `CRITyFLN`)
+`{admiral}` provides
+[`derive_vars_crit_flag()`](https://pharmaverse.github.io/admiral/dev/reference/derive_vars_crit_flag.html).
+It ensures that they are derived in an ADaM-compliant way (see
+documentation of the function for details).
+
+In most cases the criterion depends on the parameter. In the following
+example, the criterion flags for weight based on percentage change in
+weight reduction from baseline is derived. Additional criterion flags
+can be added as needed.
+
+```{r eval=TRUE}
+advs <- advs %>%
+  derive_vars_crit_flag(
+    condition = PCHG <= -5 & PARAMCD == "WEIGHT",
+    description = "Achievement of ≥ 5% weight reduction from baseline",
+    crit_nr = 1,
+    values_yn = TRUE,
+    create_numeric_flag = FALSE
+  ) %>%
+  derive_vars_crit_flag(
+    condition = PCHG <= -10 & PARAMCD == "WEIGHT",
+    description = "Achievement of ≥ 10% weight reduction from baseline",
+    crit_nr = 2,
+    values_yn = TRUE,
+    create_numeric_flag = FALSE
+  )
+```
+
+```{r, eval=TRUE, echo=FALSE}
+dataset_vignette(
+  arrange(advs, USUBJID, VISITNUM, VSTPTNUM, PARAMCD),
+  display_vars = exprs(USUBJID, PARAMCD, PCHG, CRIT1, CRIT1FL, CRIT2, CRIT2FL),
+  filter = PARAMCD %in% c("WEIGHT")
+)
+```
+
+## Remaining ADVS Set-up {#advs_end}
+
+The `{admiral}` [Creating a BDS Finding ADaM
+vignette](https://pharmaverse.github.io/admiral/articles/bds_finding.html)
+covers all the steps that are not shown here, such as merging the
+parameter-level values, timing variables, and analysis flags.
+
+# Example Scripts {#example}
+
+ADaM | Sample Code
+---- | --------------
+ADVS | [ad_advs.R](https://github.com/pharmaverse/admiralmetabolic/blob/main/inst/templates/ad_advs.R){target="_blank"} 
diff --git a/vignettes/adxx.Rmd b/vignettes/adxx.Rmd
deleted file mode 100644
index b847846..0000000
--- a/vignettes/adxx.Rmd
+++ /dev/null
@@ -1,30 +0,0 @@
----
-title: "Creating ADXX"
-output: 
-  rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Creating ADXX}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r setup, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-# Introduction
-
-This article describes creating an `ADXX` ADaM.
-
-# Programming Workflow
-
-Steps involved to create an `ADXX` ADaM.
-
-# Example Scripts
-
-ADaM | Sourcing Command
----- | --------------
-ADXX | `admiral::use_ad_template("ADXX", package = "admiraltemplate")`