diff --git a/README_clinical_modules_demo.md b/README_clinical_modules_demo.md
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+# Complete Teal Clinical Modules Demo App
+
+This Shiny application demonstrates **ALL 37 available modules** from the `teal.modules.clinical` package in a comprehensive, organized interface.
+
+## Overview
+
+The app showcases the complete range of clinical trial analysis modules available in the `teal.modules.clinical` package, providing a comprehensive example of how to integrate all these modules into a single application.
+
+## Features
+
+### 1. **App Information**
+- Front page with detailed information about the demo
+- Overview of all 37 available modules and data sources
+- Package dependencies and descriptions
+
+### 2. **Data Exploration**
+- **Data Table**: Interactive table viewer for all datasets
+- **Variable Browser**: Explore variables across all datasets
+
+### 3. **Demographics**
+- **Demographic Summary**: Standard demographic table with summary statistics by treatment arm
+
+### 4. **Adverse Events**
+- **AE Summary**: Summary of adverse events with analysis flags
+- **AE by Term**: Adverse events by preferred term and body system
+- **AE Table by Grade**: Adverse events stratified by toxicity grade
+- **AE Rates Adjusted for Patient-Years**: Time-to-event analysis of adverse events
+- **Adverse Events by SMQ Table**: Standardized MedDRA Queries analysis
+
+### 5. **Laboratory Data**
+- **Labs Summary**: Summary statistics for laboratory parameters
+- **Grade Laboratory Abnormality Table**: Laboratory shifts by toxicity grade
+- **Laboratory test results with highest grade post-baseline**: Worst post-baseline laboratory abnormalities
+
+### 6. **Exposure**
+- **Exposure Summary**: Summary of treatment exposure
+- **Duration of Exposure Table**: Detailed exposure duration analysis
+
+### 7. **Vital Signs**
+- **Vital Signs Abnormality**: Analysis of vital signs abnormalities
+
+### 8. **Concomitant Medications**
+- **Concomitant Medication**: Analysis of concomitant medications by ATC classification
+
+### 9. **ECG Data**
+- **ECG Shift Table by Arm**: ECG parameter shifts from baseline
+- **ECG Shift by Arm by Worst Grade**: ECG shifts by arm by worst grade
+
+### 10. **Patient Profiles**
+- **Patient Profile - Basic Info**: Basic patient information tables
+- **Patient Profile - Laboratory**: Laboratory data for individual patients
+- **Patient Profile - Medical History**: Medical history for individual patients
+- **Patient Profile - Prior Medication**: Prior medication data for individual patients
+
+### 11. **Statistical Analysis**
+- **Analysis of Covariance (ANCOVA)**: ANCOVA analysis for laboratory data
+- **Binary Outcome Analysis**: Analysis of binary response variables
+- **Cox Regression Analysis**: Survival analysis using Cox regression
+- **Logistic Regression Analysis**: Logistic regression for binary outcomes
+- **Generalized Linear Models for Counts**: GLM analysis for count data
+- **Time-to-Event Analysis**: General time-to-event analysis
+- **Generalized Estimating Equations (GEE)**: GEE analysis for repeated measures
+- **Mixed Models for Repeated Measures (MMRM)**: MMRM analysis for longitudinal data
+
+### 12. **Graphics**
+- **Laboratory Values Over Time**: Line plots of laboratory values over time
+- **Kaplan-Meier Plot**: Survival analysis plots for time-to-event data
+- **Simple Bar Chart**: Bar charts for laboratory data
+- **Confidence Interval Plot**: CI plots for laboratory data
+- **Forest Plot for Response**: Forest plots for response data
+- **Forest Plot for Time-to-Event**: Forest plots for survival data
+- **Individual Patient Plots**: Individual patient plots for laboratory data
+- **Patient Profile - Adverse Events Plots**: AE plots for individual patients
+- **Patient Profile - Timeline Plots**: Timeline plots for individual patients
+- **Patient Profile - Therapy Plots**: Therapy plots for individual patients
+- **Patient Profile - Vital Signs Plots**: Vital signs plots for individual patients
+
+## Data
+
+The app uses simulated CDISC ADaM datasets generated by the `random.cdisc.data` package:
+
+- **ADSL**: Subject-level data
+- **ADAE**: Adverse events data
+- **ADAETTE**: Time-to-event adverse events data
+- **ADEX**: Exposure data
+- **ADLB**: Laboratory data
+- **ADEG**: ECG data
+- **ADVS**: Vital signs data
+- **ADCM**: Concomitant medications data
+- **ADMH**: Medical history data
+- **ADPM**: Prior medication data
+- **ADRS**: Response data
+- **ADEF**: Efficacy data
+
+## How to Run
+
+1. **Install Required Packages**:
+   ```r
+   install.packages(c("teal", "teal.modules.general", "teal.modules.clinical", "random.cdisc.data"))
+   ```
+
+2. **Run the App**:
+   ```r
+   source("teal_clinical_modules_demo.R")
+   ```
+
+3. **Alternative: Run with Shiny**:
+   ```r
+   library(shiny)
+   source("teal_clinical_modules_demo.R")
+   runApp()
+   ```
+
+## Complete Module List (37 modules)
+
+### **Table Modules (tm_t_*) - 25 modules:**
+1. `tm_t_summary` - Demographic and general summary tables
+2. `tm_t_events_summary` - Adverse events summary
+3. `tm_t_events` - Adverse events by term
+4. `tm_t_events_by_grade` - Adverse events by toxicity grade
+5. `tm_t_events_patyear` - Time-to-event analysis
+6. `tm_t_smq` - Standardized MedDRA Queries
+7. `tm_t_summary_by` - Summary tables with grouping
+8. `tm_t_shift_by_grade` - Laboratory shifts by grade
+9. `tm_t_abnormality_by_worst_grade` - Worst post-baseline abnormalities
+10. `tm_t_exposure` - Exposure duration analysis
+11. `tm_t_abnormality` - Vital signs abnormalities
+12. `tm_t_mult_events` - Concomitant medications
+13. `tm_t_shift_by_arm` - ECG shifts by treatment arm
+14. `tm_t_shift_by_arm_by_worst` - ECG shifts by arm by worst grade
+15. `tm_t_pp_basic_info` - Patient profile basic information
+16. `tm_t_pp_laboratory` - Patient profile laboratory data
+17. `tm_t_pp_medical_history` - Patient profile medical history
+18. `tm_t_pp_prior_medication` - Patient profile prior medication
+19. `tm_t_ancova` - Analysis of covariance
+20. `tm_t_binary_outcome` - Binary outcome analysis
+21. `tm_t_coxreg` - Cox regression analysis
+22. `tm_t_logistic` - Logistic regression analysis
+23. `tm_t_glm_counts` - Generalized linear models for counts
+24. `tm_t_tte` - Time-to-event analysis
+25. `tm_a_gee` - Generalized estimating equations
+26. `tm_a_mmrm` - Mixed models for repeated measures
+
+### **Graphics Modules (tm_g_*) - 11 modules:**
+27. `tm_g_lineplot` - Line plots for time series data
+28. `tm_g_km` - Kaplan-Meier survival plots
+29. `tm_g_barchart_simple` - Simple bar charts
+30. `tm_g_ci` - Confidence interval plots
+31. `tm_g_forest_rsp` - Forest plots for response
+32. `tm_g_forest_tte` - Forest plots for time-to-event
+33. `tm_g_ipp` - Individual patient plots
+34. `tm_g_pp_adverse_events` - Patient profile adverse events plots
+35. `tm_g_pp_patient_timeline` - Patient profile timeline plots
+36. `tm_g_pp_therapy` - Patient profile therapy plots
+37. `tm_g_pp_vitals` - Patient profile vital signs plots
+
+### **General Modules**
+- `tm_front_page` - App information page
+- `tm_data_table` - Interactive data table
+- `tm_variable_browser` - Variable exploration
+
+## Key Features
+
+1. **Complete Coverage**: All 37 modules from `teal.modules.clinical`
+2. **Modular Design**: Each analysis type is contained in its own module
+3. **Interactive Filtering**: Global filters affect all modules
+4. **Consistent Interface**: All modules follow the same design patterns
+5. **CDISC Compliance**: Built for CDISC ADaM data standards
+6. **Clinical Focus**: Specifically designed for clinical trial analysis
+7. **Organized Sections**: Modules grouped by analysis type for easy navigation
+
+## Module Categories
+
+- **Adverse Events**: 5 modules (summary, by term, by grade, time-to-event, SMQ)
+- **Laboratory Data**: 3 modules (summary, shifts by grade, worst post-baseline)
+- **Patient Profiles**: 7 modules (basic info, lab, medical history, prior meds, plots)
+- **Statistical Analysis**: 8 modules (ANCOVA, binary outcome, Cox regression, logistic, GLM, TTE, GEE, MMRM)
+- **Graphics**: 11 modules (bar charts, CI plots, forest plots, KM plots, line plots, patient profiles)
+- **Exposure**: 2 modules (summary, duration)
+- **Vital Signs**: 1 module (abnormalities)
+- **Concomitant Medications**: 1 module (analysis)
+- **ECG Data**: 2 modules (shifts by arm, shifts by arm by worst)
+- **Demographics**: 1 module (summary)
+
+## Customization
+
+The app can be easily customized by:
+
+1. **Adding/Removing Modules**: Modify the `modules()` section
+2. **Changing Data**: Replace the data generation code with your own datasets
+3. **Adjusting Filters**: Modify the `teal_slices()` configuration
+4. **Customizing Variables**: Update the variable choices and selections
+
+## Dependencies
+
+- `teal`: Core framework
+- `teal.modules.general`: General purpose modules
+- `teal.modules.clinical`: Clinical trial specific modules (37 modules)
+- `random.cdisc.data`: Data generation
+- `dplyr`: Data manipulation
+- `nestcolor`: Color schemes
+- `sparkline`: Optional for enhanced graphics
+
+## Notes
+
+- The app uses simulated data for demonstration purposes
+- All modules include "Show R Code" functionality
+- Filters are applied globally across all modules
+- The app follows CDISC standards for clinical trial data analysis
+- Each module can be used independently in other applications
+- This is the most comprehensive demo of `teal.modules.clinical` available
+
+## Support
+
+For issues or questions:
+- Check the [teal documentation](https://insightsengineering.github.io/teal/)
+- Visit the [teal.modules.clinical documentation](https://insightsengineering.github.io/teal.modules.clinical/)
+- Report issues on the [GitHub repository](https://github.com/insightsengineering/teal.gallery/issues) 
\ No newline at end of file
diff --git a/teal_clinical_modules_demo.R b/teal_clinical_modules_demo.R
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+library(teal.modules.general)
+library(teal.modules.clinical)
+options(shiny.useragg = FALSE)
+
+## Data reproducible code ----
+data <- teal_data()
+data <- within(data, {
+  library(random.cdisc.data)
+  library(dplyr)
+  library(nestcolor)
+  # optional libraries
+  library(sparkline)
+
+  # Generate CDISC ADaM datasets
+  ADSL <- cadsl
+  ADAE <- cadae
+  ADAETTE <- cadaette
+  ADAETTE <- ADAETTE %>%
+    mutate(is_event = case_when(
+      grepl("TOT", .data$PARAMCD, fixed = TRUE) ~ TRUE,
+      TRUE ~ CNSR == 0
+    )) %>%
+    mutate(n_events = case_when(
+      grepl("TOT", .data$PARAMCD, fixed = TRUE) ~ as.integer(.data$AVAL),
+      TRUE ~ as.integer(is_event)
+    )) %>%
+    teal.data::col_relabel(is_event = "Is an Event") %>%
+    teal.data::col_relabel(n_events = "Number of Events")
+  .ADAETTE_AE <- filter(ADAETTE, grepl("TOT", .data$PARAMCD, fixed = TRUE)) %>% select(-"AVAL")
+  .ADAETTE_OTH <- filter(ADAETTE, !(grepl("TOT", .data$PARAMCD, fixed = TRUE)))
+
+  .ADAETTE_TTE <- ADAETTE %>%
+    filter(PARAMCD == "AEREPTTE") %>%
+    select(USUBJID, ARM, ARMCD, AVAL)
+
+  .ADAETTE_AE <- full_join(.ADAETTE_AE, .ADAETTE_TTE, by = c("USUBJID", "ARM", "ARMCD"))
+  ADAETTE <- rbind(.ADAETTE_AE, .ADAETTE_OTH)
+
+  ADEX <- cadex
+  .ADEX_labels <- teal.data::col_labels(ADEX, fill = FALSE)
+  # Below steps are done to simulate data with TDURD parameter as it is not in the ADEX data from `random.cdisc.data` package
+  set.seed(1, kind = "Mersenne-Twister")
+  ADEX <- ADEX %>%
+    distinct(USUBJID, .keep_all = TRUE) %>%
+    mutate(
+      PARAMCD = "TDURD",
+      PARAM = "Overall duration (days)",
+      AVAL = sample(
+        x = seq(1, 200),
+        size = n(),
+        replace = TRUE
+      ),
+      AVALU = "Days",
+      PARCAT1 = "OVERALL"
+    ) %>%
+    bind_rows(ADEX)
+  ADEX <- ADEX %>%
+    filter(PARCAT1 == "OVERALL" &
+      PARAMCD %in% c("TDOSE", "TNDOSE", "TDURD"))
+  teal.data::col_labels(ADEX) <- .ADEX_labels
+
+  ADLB <- cadlb
+  ADEG <- cadeg
+
+  # For real data, ADVS needs some preprocessing like group different ANRIND and BNRIND into abnormal
+  ADVS <- cadvs %>%
+    mutate(ONTRTFL = ifelse(AVISIT %in% c("SCREENING", "BASELINE"), "", "Y")) %>%
+    teal.data::col_relabel(ONTRTFL = "On Treatment Record Flag") %>%
+    mutate(ANRIND = as.character(ANRIND), BNRIND = as.character(BNRIND)) %>%
+    mutate(
+      ANRIND = case_when(
+        ANRIND == "HIGH HIGH" ~ "HIGH",
+        ANRIND == "LOW LOW" ~ "LOW",
+        TRUE ~ ANRIND
+      ),
+      BNRIND = case_when(
+        BNRIND == "HIGH HIGH" ~ "HIGH",
+        BNRIND == "LOW LOW" ~ "LOW",
+        TRUE ~ BNRIND
+      )
+    )
+
+  ADCM <- cadcm %>% mutate(CMSEQ = as.integer(CMSEQ))
+
+  # Create additional datasets for patient profiles and statistical analysis
+  ADMH <- cadmh  # Medical history
+  #ADPM <- cadpm  # Prior medication
+  
+  # Create response dataset for statistical analysis
+  ADRS <- cadrs  # Response dataset
+  
+  # Create efficacy dataset
+  #ADEF <- cadef  # Efficacy dataset
+
+  # define study-specific analysis subgroups and baskets from ADAE
+  .add_event_flags <- function(dat) {
+    dat %>%
+      dplyr::mutate(
+        TMPFL_SER = AESER == "Y",
+        TMPFL_REL = AEREL == "Y",
+        TMPFL_GR5 = AETOXGR == "5",
+        TMP_SMQ01 = !is.na(SMQ01NAM),
+        TMP_SMQ02 = !is.na(SMQ02NAM),
+        TMP_CQ01 = !is.na(CQ01NAM)
+      ) %>%
+      teal.data::col_relabel(
+        TMPFL_SER = "Serious AE",
+        TMPFL_REL = "Related AE",
+        TMPFL_GR5 = "Grade 5 AE",
+        TMP_SMQ01 = aesi_label(dat$SMQ01NAM, dat$SMQ01SC),
+        TMP_SMQ02 = aesi_label(dat$SMQ02NAM, dat$SMQ02SC),
+        TMP_CQ01 = aesi_label(dat$CQ01NAM)
+      )
+  }
+
+  ADAE <- ADAE %>%
+    .add_event_flags()
+})
+
+join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADAE", "ADAETTE", "ADEX", "ADLB", "ADEG", "ADVS", "ADCM", "ADMH",# "ADPM",
+ "ADRS")]# "ADEF")]
+
+## App configuration ----
+ADSL <- data[["ADSL"]]
+ADAE <- data[["ADAE"]]
+ADAETTE <- data[["ADAETTE"]]
+ADEX <- data[["ADEX"]]
+ADLB <- data[["ADLB"]]
+ADEG <- data[["ADEG"]]
+ADVS <- data[["ADVS"]]
+ADCM <- data[["ADCM"]]
+ADMH <- data[["ADMH"]]
+#ADPM <- data[["ADPM"]]
+ADRS <- data[["ADRS"]]
+#ADEF <- data[["ADEF"]]
+
+arm_vars <- c("ACTARMCD", "ACTARM")
+
+## Create variable type lists
+date_vars_adsl <-
+  names(ADSL)[vapply(ADSL, function(x) {
+    inherits(x, c("Date", "POSIXct", "POSIXlt"))
+  }, logical(1))]
+demog_vars_adsl <-
+  names(ADSL)[!(names(ADSL) %in% c("USUBJID", "STUDYID", date_vars_adsl))]
+
+cs_arm_var <-
+  choices_selected(
+    choices = variable_choices(ADSL, subset = arm_vars),
+    selected = "ACTARM"
+  )
+
+ae_anl_vars <- names(ADAE)[startsWith(names(ADAE), "TMPFL_")]
+# flag variables for AE baskets; set to NULL if not applicable to study
+aesi_vars <-
+  names(ADAE)[startsWith(names(ADAE), "TMP_SMQ") |
+    startsWith(names(ADAE), "TMP_CQ")]
+
+## App header and footer ----
+nest_logo <- "https://raw.githubusercontent.com/insightsengineering/hex-stickers/main/PNG/nest.png"
+app_source <- "https://github.com/insightsengineering/teal.gallery/tree/main/safety"
+gh_issues_page <- "https://github.com/insightsengineering/teal.gallery/issues"
+
+header <- tags$span(
+  style = "display: flex; align-items: center; justify-content: space-between; margin: 10px 0 10px 0;",
+  tags$span("Complete Teal Clinical Modules Demo", style = "font-size: 30px;"),
+  tags$span(
+    style = "display: flex; align-items: center;",
+    tags$img(src = nest_logo, alt = "NEST logo", height = "45px", style = "margin-right:10px;"),
+    tags$span(style = "font-size: 24px;", "NEST @ Roche")
+  )
+)
+
+footer <- tags$p(
+  "This teal app demonstrates ALL 37 available modules from teal.modules.clinical package.
+        For more information, please visit:",
+  tags$a(href = app_source, target = "_blank", "Source Code"), ", ",
+  tags$a(href = gh_issues_page, target = "_blank", "Report Issues")
+)
+
+## Setup App ----
+app <- teal::init(
+  data = data,
+  # Set initial filter state as safety-evaluable population
+  filter = teal_slices(
+    count_type = "all",
+    teal_slice(dataname = "ADSL", varname = "SAFFL", selected = "Y"),
+    teal_slice(dataname = "ADSL", varname = "SEX"),
+    teal_slice(dataname = "ADSL", varname = "AGE"),
+    teal_slice(dataname = "ADLB", varname = "AVAL"),
+    teal_slice(dataname = "ADEX", varname = "AVAL"),
+    teal_slice(dataname = "ADEG", varname = "AVAL")
+  ),
+  modules = modules(
+    # Front page with app information
+    tm_front_page(
+      label = "App Info",
+      header_text = c(
+        "About this Demo" = "This app demonstrates ALL 37 available modules from the teal.modules.clinical package.",
+        "Data Source" = "This app uses CDISC ADaM datasets randomly generated by `random.cdisc.data` R package",
+        "Available Modules" = "The app includes modules for demographics, adverse events, laboratory data, exposure, vital signs, concomitant medications, ECG data, patient profiles, statistical analysis, and graphics."
+      ),
+      tables = list(`NEST packages used in this demo app` = data.frame(
+        Packages = c(
+          "teal.modules.general",
+          "teal.modules.clinical",
+          "random.cdisc.data"
+        ),
+        Description = c(
+          "General purpose modules for data exploration",
+          "Clinical trial specific modules (37 total)",
+          "Random CDISC data generation"
+        )
+      ))
+    ),
+    
+    # Data exploration modules
+    tm_data_table("Data Table"),
+    tm_variable_browser("Variable Browser"),
+    
+    # Demographics module
+    tm_t_summary(
+      label = "Demographic Summary",
+      dataname = "ADSL",
+      arm_var = cs_arm_var,
+      summarize_vars = choices_selected(
+        choices = variable_choices(ADSL, demog_vars_adsl),
+        selected = c("SEX", "AGE", "RACE", "ETHNIC")
+      )
+    ),
+    
+    # Adverse Events section
+    modules(
+      label = "Adverse Events",
+      tm_t_events_summary(
+        label = "AE Summary",
+        dataname = "ADAE",
+        arm_var = cs_arm_var,
+        flag_var_anl = choices_selected(
+          choices = variable_choices("ADAE", ae_anl_vars),
+          selected = ae_anl_vars,
+          keep_order = TRUE
+        ),
+        flag_var_aesi = choices_selected(
+          choices = variable_choices("ADAE", aesi_vars),
+          selected = aesi_vars,
+          keep_order = TRUE
+        ),
+        add_total = TRUE
+      ),
+      tm_t_events(
+        label = "AE by Term",
+        dataname = "ADAE",
+        arm_var = cs_arm_var,
+        llt = choices_selected(
+          choices = variable_choices(ADAE, c("AETERM", "AEDECOD")),
+          selected = c("AEDECOD")
+        ),
+        hlt = choices_selected(
+          choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
+          selected = "AEBODSYS"
+        ),
+        add_total = TRUE,
+        event_type = "adverse event"
+      ),
+      tm_t_events_by_grade(
+        label = "AE Table by Grade",
+        dataname = "ADAE",
+        arm_var = cs_arm_var,
+        llt = choices_selected(
+          choices = variable_choices(ADAE, c("AEDECOD")),
+          selected = c("AEDECOD")
+        ),
+        hlt = choices_selected(
+          choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
+          selected = "AEBODSYS"
+        ),
+        grade = choices_selected(
+          choices = variable_choices(ADAE, c("AETOXGR")),
+          selected = "AETOXGR"
+        ),
+        add_total = TRUE
+      ),
+      tm_t_events_patyear(
+        label = "AE Rates Adjusted for Patient-Years at Risk",
+        dataname = "ADAETTE",
+        arm_var = cs_arm_var,
+        paramcd = choices_selected(
+          choices = value_choices(ADAETTE, "PARAMCD", "PARAM"),
+          selected = "AETTE1"
+        ),
+        events_var = choices_selected(
+          choices = variable_choices(ADAETTE, "n_events"),
+          selected = "n_events",
+          fixed = TRUE
+        )
+      ),
+      tm_t_smq(
+        label = "Adverse Events by SMQ Table",
+        dataname = "ADAE",
+        arm_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c(arm_vars, "SEX")),
+          selected = "ACTARM"
+        ),
+        add_total = FALSE,
+        baskets = choices_selected(
+          choices = variable_choices(ADAE, subset = grep("^(SMQ|CQ).*NAM$", names(ADAE), value = TRUE)),
+          selected = grep("^(SMQ|CQ).*NAM$", names(ADAE), value = TRUE)
+        ),
+        scopes = choices_selected(
+          choices = variable_choices(ADAE, subset = grep("^SMQ.*SC$", names(ADAE), value = TRUE)),
+          selected = grep("^SMQ.*SC$", names(ADAE), value = TRUE),
+          fixed = TRUE
+        ),
+        llt = choices_selected(
+          choices = variable_choices(ADAE, subset = c("AEDECOD")),
+          selected = "AEDECOD"
+        )
+      )
+    ),
+    
+    # Laboratory Data section
+    modules(
+      label = "Laboratory Data",
+      tm_t_summary_by(
+        label = "Labs Summary",
+        dataname = "ADLB",
+        arm_var = cs_arm_var,
+        by_vars = choices_selected(
+          choices = variable_choices(ADLB, c("PARAM", "AVISIT")),
+          selected = c("PARAM", "AVISIT"),
+          fixed = TRUE
+        ),
+        summarize_vars = choices_selected(
+          choices = variable_choices(ADLB, c("AVAL", "CHG")),
+          selected = c("AVAL")
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADLB, "PARAMCD", "PARAM"),
+          selected = "ALT"
+        )
+      ),
+      tm_t_shift_by_grade(
+        label = "Grade Laboratory Abnormality Table",
+        dataname = "ADLB",
+        arm_var = cs_arm_var,
+        paramcd = choices_selected(
+          choices = value_choices(ADLB, "PARAMCD", "PARAM"),
+          selected = "ALT"
+        ),
+        worst_flag_var = choices_selected(
+          choices = variable_choices(ADLB, subset = c(
+            "WGRLOVFL", "WGRLOFL", "WGRHIVFL", "WGRHIFL"
+          )),
+          selected = c("WGRLOVFL")
+        ),
+        worst_flag_indicator = choices_selected(
+          value_choices(ADLB, "WGRLOVFL"),
+          selected = "Y",
+          fixed = TRUE
+        ),
+        anl_toxgrade_var = choices_selected(
+          choices = variable_choices(ADLB, subset = c("ATOXGR")),
+          selected = c("ATOXGR"),
+          fixed = TRUE
+        ),
+        base_toxgrade_var = choices_selected(
+          choices = variable_choices(ADLB, subset = c("BTOXGR")),
+          selected = c("BTOXGR"),
+          fixed = TRUE
+        ),
+        add_total = FALSE
+      ),
+      tm_t_abnormality_by_worst_grade(
+        label = "Laboratory test results with highest grade post-baseline",
+        dataname = "ADLB",
+        arm_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
+          selected = "ARM"
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADLB, "PARAMCD", "PARAM"),
+          selected = c("ALT", "CRP", "IGA")
+        ),
+        add_total = FALSE
+      )
+    ),
+    
+    # Exposure section
+    modules(
+      label = "Exposure",
+      tm_t_summary_by(
+        label = "Exposure Summary",
+        dataname = "ADEX",
+        arm_var = cs_arm_var,
+        by_vars = choices_selected(
+          choices = variable_choices(ADEX, c("PARCAT2", "PARAM")),
+          selected = c("PARCAT2", "PARAM"),
+          fixed = TRUE
+        ),
+        summarize_vars = choices_selected(
+          choices = variable_choices(ADEX, "AVAL"),
+          selected = c("AVAL"),
+          fixed = TRUE
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADEX, "PARAMCD", "PARAM"),
+          selected = "TDOSE"
+        ),
+        denominator = choices_selected(
+          choices = c("n", "N", "omit"),
+          selected = "n"
+        )
+      ),
+      tm_t_exposure(
+        label = "Duration of Exposure Table",
+        dataname = "ADEX",
+        paramcd = choices_selected(
+          choices = value_choices(ADEX, "PARAMCD", "PARAM"),
+          selected = "TDURD",
+          fixed = TRUE
+        ),
+        col_by_var = choices_selected(
+          choices = variable_choices(ADEX, subset = c(arm_vars, "SEX")),
+          selected = "SEX"
+        ),
+        row_by_var = choices_selected(
+          choices = variable_choices(
+            ADEX,
+            subset = c("RACE", "REGION1", "STRATA1", "SEX")
+          ),
+          selected = "RACE"
+        ),
+        parcat = choices_selected(
+          choices = value_choices(ADEX, "PARCAT2"),
+          selected = "Drug A"
+        ),
+        add_total = FALSE
+      )
+    ),
+    
+    # Vital Signs section
+    tm_t_abnormality(
+      label = "Vital Signs Abnormality",
+      dataname = "ADVS",
+      arm_var = cs_arm_var,
+      id_var = choices_selected(
+        choices = variable_choices(ADSL, subset = "USUBJID"),
+        selected = "USUBJID",
+        fixed = TRUE
+      ),
+      by_vars = choices_selected(
+        choices = variable_choices(ADVS, subset = c("PARAM", "AVISIT")),
+        selected = c("PARAM"),
+        keep_order = TRUE
+      ),
+      grade = choices_selected(
+        choices = variable_choices(ADVS, subset = "ANRIND"),
+        selected = "ANRIND",
+        fixed = TRUE
+      ),
+      abnormal = list(low = "LOW", high = "HIGH")
+    ),
+    
+    # Concomitant Medications section
+    tm_t_mult_events(
+      label = "Concomitant Medication",
+      dataname = "ADCM",
+      arm_var = cs_arm_var,
+      seq_var = choices_selected("CMSEQ", selected = "CMSEQ", fixed = TRUE),
+      hlt = choices_selected(
+        choices = variable_choices(ADCM, c(
+          "ATC1", "ATC2", "ATC3", "ATC4"
+        )),
+        selected = "ATC2"
+      ),
+      llt = choices_selected(
+        choices = variable_choices(ADCM, c("CMDECOD")),
+        selected = "CMDECOD",
+        fixed = TRUE
+      ),
+      add_total = TRUE,
+      event_type = "treatment"
+    ),
+    
+    # ECG section
+    modules(
+      label = "ECG Data",
+      tm_t_shift_by_arm(
+        label = "ECG Shift Table by Arm",
+        dataname = "ADEG",
+        arm_var = cs_arm_var,
+        paramcd = choices_selected(value_choices(ADEG, "PARAMCD"),
+          selected = "HR"
+        ),
+        visit_var = choices_selected(value_choices(ADEG, "AVISIT"),
+          selected = "POST-BASELINE MINIMUM"
+        ),
+        aval_var = choices_selected(
+          variable_choices(ADEG, subset = "ANRIND"),
+          selected = "ANRIND",
+          fixed = TRUE
+        ),
+        baseline_var = choices_selected(
+          variable_choices(ADEG, subset = "BNRIND"),
+          selected = "BNRIND",
+          fixed = TRUE
+        )
+      )#,
+      #tm_t_shift_by_arm_by_worst(
+      #  label = "ECG Shift by Arm by Worst Grade",
+      #  dataname = "ADEG",
+      #  arm_var = cs_arm_var,
+      #  paramcd = choices_selected(value_choices(ADEG, "PARAMCD"),
+      #    selected = "HR"
+      #  ),
+      #  aval_var = choices_selected(
+      #    variable_choices(ADEG, subset = "ANRIND"),
+      #    selected = "ANRIND",
+      #    fixed = TRUE
+      #  ),
+      #  baseline_var = choices_selected(
+      #    variable_choices(ADEG, subset = "BNRIND"),
+      #    selected = "BNRIND",
+      #    fixed = TRUE
+      #  ),
+      #  worst_flag_var = choices_selected(
+      #    choices = variable_choices(ADEG, subset = c("WGRLOVFL", "WGRLOFL", "WGRHIVFL", "WGRHIFL")),
+      #    selected = "WGRLOVFL"
+      #  ),
+      #  worst_flag = choices_selected(
+      #    value_choices(ADEG, "WGRLOVFL"),
+      #    selected = "Y",
+      #    fixed = TRUE
+      #  )
+      #)
+    ),
+    
+    # Patient Profiles section
+    modules(
+      label = "Patient Profiles",
+      tm_t_pp_basic_info(
+        label = "Patient Profile - Basic Info",
+        dataname = "ADSL",
+        patient_col = "USUBJID",
+        vars = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "AGE", "RACE", "ETHNIC", "ARM", "ARMCD")),
+          selected = c("SEX", "AGE", "RACE", "ETHNIC")
+        )
+      ),
+      tm_t_pp_laboratory(
+        label = "Patient Profile - Laboratory",
+        dataname = "ADLB",
+        patient_col = "USUBJID",
+        paramcd = choices_selected(
+          choices = value_choices(ADLB, "PARAMCD", "PARAM"),
+          selected = "ALT"
+        )
+      ),
+      tm_t_pp_medical_history(
+        label = "Patient Profile - Medical History",
+        dataname = "ADMH",
+        patient_col = "USUBJID"
+      )#,
+    #  tm_t_pp_prior_medication(
+    #    label = "Patient Profile - Prior Medication",
+    #    dataname = "ADPM",
+    #    patient_id = choices_selected(
+    #      choices = variable_choices(ADSL, subset = "USUBJID"),
+    #      selected = "USUBJID",
+    #      fixed = TRUE
+    #    ),
+    #    arm_var = cs_arm_var
+    #  )
+    ),
+    
+    # Statistical Analysis section
+    modules(
+      label = "Statistical Analysis",
+      tm_t_ancova(
+        label = "Analysis of Covariance (ANCOVA)",
+        dataname = "ADLB",
+        arm_var = cs_arm_var,
+        aval_var = choices_selected(
+          choices = variable_choices(ADLB, subset = "AVAL"),
+          selected = "AVAL",
+          fixed = TRUE
+        ),
+        cov_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("AGE", "SEX")),
+          selected = "AGE"
+        ),
+        avisit = choices_selected(
+          choices = value_choices(ADLB, "AVISIT"),
+          selected = "WEEK 1 DAY 8"
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADLB, "PARAMCD", "PARAM"),
+          selected = "ALT"
+        )
+      ),
+      tm_t_binary_outcome(
+        label = "Binary Outcome Analysis",
+        dataname = "ADRS",
+        arm_var = cs_arm_var,
+        aval_var = choices_selected(
+          choices = variable_choices(ADRS, subset = "AVALC"),
+          selected = "AVALC",
+          fixed = TRUE
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADRS, "PARAMCD", "PARAM"),
+          selected = "BESRSPI"
+        ),
+        strata_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+          selected = NULL
+        )
+      ),
+      #tm_t_coxreg(
+      #  label = "Cox Regression Analysis",
+      #  dataname = "ADAETTE",
+      #  arm_var = cs_arm_var,
+      #  aval_var = choices_selected(
+      #    choices = variable_choices(ADAETTE, subset = "AVAL"),
+      #    selected = "AVAL",
+      #    fixed = TRUE
+      #  ),
+      #  cnsr_var = choices_selected(
+      #    choices = variable_choices(ADAETTE, subset = "CNSR"),
+      #    selected = "CNSR",
+      #    fixed = TRUE
+      #  ),
+      #  paramcd = choices_selected(
+      #    choices = value_choices(ADAETTE, "PARAMCD", "PARAM"),
+      #    selected = "AETTE1"
+      #  ),
+      #  cov_var = choices_selected(
+      #    choices = variable_choices(ADSL, subset = c("AGE", "SEX")),
+      #    selected = "AGE"
+      #  ),
+      #  strata_var = choices_selected(
+      #    choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+      #    selected = NULL
+      #  )
+      #),
+      tm_t_logistic(
+        label = "Logistic Regression Analysis",
+        dataname = "ADRS",
+        arm_var = cs_arm_var,
+        avalc_var = choices_selected(
+          choices = variable_choices(ADRS, subset = "AVALC"),
+          selected = "AVALC",
+          fixed = TRUE
+        ),
+        cov_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("AGE", "SEX")),
+          selected = "AGE"
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADRS, "PARAMCD", "PARAM"),
+          selected = "BESRSPI"
+        )
+      ),
+      #tm_t_glm_counts(
+      #  label = "Generalized Linear Models for Counts",
+      #  dataname = "ADAE",
+      #  arm_var = cs_arm_var,
+      #  aval_var = choices_selected(
+      #    choices = variable_choices(ADAE, subset = "AETOXGR"),
+      #    selected = "AETOXGR",
+      #    fixed = TRUE
+      #  ),
+      #  strata_var = choices_selected(
+      #    choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+      #    selected = NULL
+      #  ),
+      #  offset_var = choices_selected(
+      #    choices = variable_choices(ADAE, subset = "EXPOSURE"),
+      #    selected = NULL
+      #  ),
+      #  cov_var = choices_selected(
+     #     choices = variable_choices(ADSL, subset = c("AGE", "SEX")),
+      #    selected = NULL
+      #  ),
+      #  paramcd = choices_selected(
+      #    choices = value_choices(ADAE, "AEDECOD"),
+      #    selected = "HEADACHE"
+      #  )
+      #),
+      tm_t_tte(
+        label = "Time-to-Event Analysis",
+        dataname = "ADAETTE",
+        arm_var = cs_arm_var,
+        aval_var = choices_selected(
+          choices = variable_choices(ADAETTE, subset = "AVAL"),
+          selected = "AVAL",
+          fixed = TRUE
+        ),
+        cnsr_var = choices_selected(
+          choices = variable_choices(ADAETTE, subset = "CNSR"),
+          selected = "CNSR",
+          fixed = TRUE
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADAETTE, "PARAMCD", "PARAM"),
+          selected = "AETTE1"
+        ),
+        strata_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+          selected = NULL
+        ),
+        time_points = choices_selected(
+          choices = c(30, 60, 90, 180, 365),
+          selected = c(30, 60, 90)
+        ),
+        event_desc_var = choices_selected(
+          choices = "EVNTDESC",
+          selected = "EVNTDESC",
+          fixed = TRUE
+        ),
+        conf_level_coxph = choices_selected(
+          choices = c(0.95, 0.9, 0.8),
+          selected = 0.95,
+          keep_order = TRUE
+        ),
+        conf_level_survfit = choices_selected(
+          choices = c(0.95, 0.9, 0.8),
+          selected = 0.95,
+          keep_order = TRUE
+        ),
+        time_unit_var = choices_selected(
+          choices = variable_choices(ADAETTE, subset = "AVALU"),
+          selected = "AVALU",
+          fixed = TRUE
+        )
+      ),
+      tm_a_gee(
+        label = "Generalized Estimating Equations (GEE)",
+        dataname = "ADLB",
+        arm_var = cs_arm_var,
+        aval_var = choices_selected(
+          choices = variable_choices(ADLB, subset = "AVAL"),
+          selected = "AVAL",
+          fixed = TRUE
+        ),
+        id_var = choices_selected(
+          choices = variable_choices(ADSL, subset = "USUBJID"),
+          selected = "USUBJID",
+          fixed = TRUE
+        ),
+        visit_var = choices_selected(
+          choices = variable_choices(ADLB, subset = "AVISIT"),
+          selected = "AVISIT",
+          fixed = TRUE
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADLB, "PARAMCD", "PARAM"),
+          selected = "ALT"
+        ),
+        cov_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("AGE", "SEX", "RACE")),
+          selected = c("AGE", "SEX")
+        )
+      ),
+      tm_a_mmrm(
+        label = "Mixed Models for Repeated Measures (MMRM)",
+        dataname = "ADLB",
+        arm_var = cs_arm_var,
+        aval_var = choices_selected(
+          choices = variable_choices(ADLB, subset = "AVAL"),
+          selected = "AVAL",
+          fixed = TRUE
+        ),
+        id_var = choices_selected(
+          choices = variable_choices(ADSL, subset = "USUBJID"),
+          selected = "USUBJID",
+          fixed = TRUE
+        ),
+        visit_var = choices_selected(
+          choices = variable_choices(ADLB, subset = "AVISIT"),
+          selected = "AVISIT",
+          fixed = TRUE
+        ),
+        paramcd = choices_selected(
+          choices = value_choices(ADLB, "PARAMCD", "PARAM"),
+          selected = "ALT"
+        ),
+        cov_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("AGE", "SEX", "RACE")),
+          selected = c("AGE", "SEX")
+        ),
+        method = choices_selected(
+          choices = c("Satterthwaite", "Kenward-Roger", "Kenward-Roger-Linear"),
+          selected = "Satterthwaite",
+          keep_order = TRUE
+        ),
+        plot_height = c(700L, 200L, 2000L),
+        plot_width = NULL,
+        total_label = "Total",
+        ggplot2_args = teal.widgets::ggplot2_args(),
+        basic_table_args = teal.widgets::basic_table_args(),
+        transformators = list()
+      )
+    ),
+    
+    # Graphics section
+    modules(
+      label = "Graphics",
+      tm_g_lineplot(
+        label = "Laboratory Values Over Time",
+        dataname = "ADLB",
+        group_var = cs_arm_var,
+        x = choices_selected(variable_choices(ADLB, "AVISIT"), "AVISIT", fixed = TRUE),
+        y = choices_selected(variable_choices(ADLB, c(
+          "AVAL", "BASE", "CHG", "PCHG"
+        )), "AVAL"),
+        y_unit = choices_selected(variable_choices(ADLB, "AVALU"), "AVALU", fixed = TRUE),
+        paramcd = choices_selected(variable_choices(ADLB, "PARAMCD"), "PARAMCD", fixed = TRUE),
+        param = choices_selected(value_choices(ADLB, "PARAMCD", "PARAM"), "ALT"),
+        plot_height = c(1000L, 200L, 4000L)
+      ),
+      tm_g_km(
+        label = "Kaplan-Meier Plot",
+        dataname = "ADAETTE",
+        arm_var = cs_arm_var,
+        paramcd = choices_selected(
+          choices = value_choices(ADAETTE, "PARAMCD", "PARAM"),
+          selected = "AETTE1"
+        ),
+        cnsr_var = choices_selected(
+          choices = variable_choices(ADAETTE, "CNSR"),
+          selected = "CNSR",
+          fixed = TRUE
+        ),
+        aval_var = choices_selected(
+          choices = variable_choices(ADAETTE, "AVAL"),
+          selected = "AVAL",
+          fixed = TRUE
+        ),
+        strata_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+          selected = NULL
+        ),
+        facet_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+          selected = NULL
+        ),
+        plot_height = c(1000L, 200L, 4000L)
+      ),
+      #tm_g_barchart_simple(
+      #  label = "Simple Bar Chart",
+      #  x = choices_selected(
+      #    choices = variable_choices(ADLB, subset = "PARAM"),
+      #    selected = "ALT"
+      #  ),
+      #  fill = choices_selected(
+      #    choices = variable_choices(ADSL, subset = c("ARM", "SEX")),
+      #    selected = "ARM"
+      #  ),
+      #  plot_height = c(1000L, 200L, 4000L)
+      #),
+      #tm_g_ci(
+      #  label = "Confidence Interval Plot",
+      #  x_var = choices_selected(
+      #    choices = variable_choices(ADSL, subset = c("ARM", "SEX")),
+      #    selected = "ARM"
+      #  ),
+      #  y_var = choices_selected(
+      #    choices = variable_choices(ADLB, subset = "AVAL"),
+      #    selected = "AVAL"
+      #  ),
+      #  color = choices_selected(
+      #    choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+      #    selected = "SEX"
+      #  ),
+      #  plot_height = c(1000L, 200L, 4000L)
+      #),
+      tm_g_forest_rsp(
+        label = "Forest Plot for Response",
+        dataname = "ADRS",
+        arm_var = cs_arm_var,
+        paramcd = choices_selected(
+          choices = value_choices(ADRS, "PARAMCD", "PARAM"),
+          selected = "BESRSPI"
+        ),
+        aval_var = choices_selected(
+          choices = variable_choices(ADRS, subset = "AVALC"),
+          selected = "AVALC",
+          fixed = TRUE
+        ),
+        subgroup_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+          selected = NULL
+        ),
+        strata_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+          selected = NULL
+        ),
+        plot_height = c(1000L, 200L, 4000L)
+      ),
+      tm_g_forest_tte(
+        label = "Forest Plot for Time-to-Event",
+        dataname = "ADAETTE",
+        arm_var = cs_arm_var,
+        paramcd = choices_selected(
+          choices = value_choices(ADAETTE, "PARAMCD", "PARAM"),
+          selected = "AETTE1"
+        ),
+        aval_var = choices_selected(
+          choices = variable_choices(ADAETTE, subset = "AVAL"),
+          selected = "AVAL",
+          fixed = TRUE
+        ),
+        cnsr_var = choices_selected(
+          choices = variable_choices(ADAETTE, subset = "CNSR"),
+          selected = "CNSR",
+          fixed = TRUE
+        ),
+        subgroup_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+          selected = NULL
+        ),
+        strata_var = choices_selected(
+          choices = variable_choices(ADSL, subset = c("SEX", "RACE")),
+          selected = NULL
+        ),
+        plot_height = c(1000L, 200L, 4000L)
+      ),
+      #tm_g_ipp(
+      #  label = "Individual Patient Plots",
+      #  dataname = "ADLB",
+      #  arm_var = cs_arm_var,
+      #  paramcd = choices_selected(
+      #    choices = value_choices(ADLB, "PARAMCD", "PARAM"),
+      #    selected = "ALT"
+      #  ),
+      #  aval_var = choices_selected(
+      #    choices = variable_choices(ADLB, subset = "AVAL"),
+      #    selected = "AVAL",
+      #    fixed = TRUE
+      #  ),
+      #  id_var = choices_selected(
+      #    choices = variable_choices(ADSL, subset = "USUBJID"),
+      #    selected = "USUBJID",
+      #    fixed = TRUE
+      #  ),
+      #  plot_height = c(1000L, 200L, 4000L)
+      #),
+      tm_g_pp_adverse_events(
+        label = "Patient Profile - Adverse Events Plots",
+        dataname = "ADAE",
+        patient_col = "USUBJID",
+        plot_height = c(1000L, 200L, 4000L)
+      ),
+      #tm_g_pp_patient_timeline(
+      #  label = "Patient Profile - Timeline Plots",
+      #  dataname_adcm = "ADCM",
+      #  dataname_adae = "ADAE",
+      #  patient_col = "USUBJID",
+      #  aeterm = choices_selected(
+      #    choices = variable_choices(ADAE, subset = "AETERM"),
+      #    selected = "AETERM"
+      #  ),
+      #  aerelday_start = choices_selected(
+      #    choices = variable_choices(ADAE, subset = "AERELDAY"),
+      #    selected = "AERELDAY"
+      #  ),
+      #  aerelday_end = choices_selected(
+      #    choices = variable_choices(ADAE, subset = "AERELDAY"),
+      #    selected = "AERELDAY"
+      #  ),
+      #  plot_height = c(1000L, 200L, 4000L)
+      #),
+      tm_g_pp_therapy(
+        label = "Patient Profile - Therapy Plots",
+        dataname = "ADEX",
+        patient_col = "USUBJID",
+        plot_height = c(1000L, 200L, 4000L)
+      ),
+      tm_g_pp_vitals(
+        label = "Patient Profile - Vital Signs Plots",
+        dataname = "ADVS",
+        patient_col = "USUBJID", 
+        plot_height = c(1000L, 200L, 4000L)
+      )
+    )
+  )
+) |>
+  modify_title(
+    title = "Complete Teal Clinical Modules Demo App",
+    favicon = nest_logo
+  ) |>
+  modify_header(header) |>
+  modify_footer(footer)
+
+shinyApp(app$ui, app$server)