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ADSL Filter: ARM = "Placebo" AND AGE >= 65
    ↓
Child Datasets: Only events/medications for elderly placebo subjects

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ADAE (Child) Filter: AESEV = "SEVERE"
    ↓
ADSL (Parent) Result: No change - still shows all subjects
ADCM (Sibling) Result: No change - still shows all medications

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ADSL (Parent) Filter: ARM = "Placebo"
    ↓
ADAE (Child) Result: Only AEs for placebo subjects
ADCM (Child) Result: Only medications for placebo subjects

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# Demonstrate hierarchical filtering with parent-child relationships
hierarchical_demo <- teal::init(
  data = coupled_demo_data,
  modules = teal::modules(
    # Parent dataset view
    teal::modules(
      label = "👤 Subject Level (Parent)",
      tm_t_summary(
        label = "Subject Summary",
        dataname = "ADSL",

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# Create data with clear relationships to demonstrate coupling
coupled_demo_data <- teal_data()
coupled_demo_data <- within(coupled_demo_data, {
  # ADSL - 20 subjects for clear demonstration
  ADSL <- adsl[1:20, ]

  # ADAE - Only adverse events for first 15 subjects
  ADAE <- adae[adae$USUBJID %in% ADSL$USUBJID[1:15], ]

  # ADCM - Only concomitant meds for first 10 subjects

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# Complex filter expressions for sophisticated population definitions
advanced_filters <- teal_slices(
  # Complex expression filter
  teal_slice(
    dataname = "ADSL",
    expr = "AGE >= 65 & BMIBL < 30",
    title = "Elderly Non-Obese",
    id = "elderly_normal_bmi"
  ),

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# Create application with initial filter state
initial_filters <- teal_slices(
  # Population definition filters
  teal_slice(
    dataname = "ADSL",
    varname = "ITTFL",
    selected = "Y",
    title = "Intent-to-Treat Population"
  ),
  teal_slice(

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# Load and apply saved filter state
# saved_filters <- readRDS("clinical_analysis_filters.rds")

# Apply saved filters to a new application
restored_app <- teal::init(
  data = demo_data,
  modules = teal::modules(
    tm_data_table(
      label = "Filtered ADSL Data",
      dataname = "ADSL"

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# Create and save filter states programmatically
library(teal.slice)

# Define a specific filter state
saved_filters <- teal_slices(
  teal_slice(
    dataname = "ADSL",
    varname = "ARM",
    selected = c("Placebo", "Xanomeline High Dose"),
    title = "Treatment Groups"

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# Application with save/restore capabilities
save_restore_demo <- teal::init(
  data = demo_data,
  modules = teal::modules(
    tm_data_table(
      label = "ADSL Data",
      dataname = "ADSL"
    ),
    tm_g_distribution(
      label = "Age Distribution",