Introduction | Example | Setup | Model Fitting | Tabulation

Scope and Audience | Chapters

Analysis Function Return Values - RowsVerticalSections | Combining RowsVerticalSection Objects | Combining Existing Analysis Functions | Different Analysis Functions For Different Columns | Stacking Analysis Functions | Conditional Stacking | Appendix A - Code For c.RowsVerticalSection

Split Context | Designing Conditional Behavior in afuns | Conditional-On-Column Behavior in afuns | Determining Column-Space Position Within An afun | General Template For Column Aware afun | Using Row Faceting Information Within afuns

Introduction | Declaring Rows And Cell Contents With analyze() | Core Idea | Basic Usage | Declaring Columns With split_cols_by() | Declaring Row-Grouping With split_rows_by() | Adding Marginal Summaries With summarize_row_groups | Some Relevant Details | All Together - A Basic Rectangular Table

Requirements | Workflow | The specifications specs.yml | The filters filters.yml | The functions | The backend machinery | Medical history workflow | Writing custom functions | create example function | Integrate it into the general workflow

Introduction | Nesting | Creating 'Multi-Section' Tables With nested = FALSE | A Brief Note On Removing Sections | Intermediate Nesting | Anchor Resolution | Order Of Intermediate Nesting Placement | Designing Multi-Section Row-Layouts To Support Subgroup Variants

Overview | Pattern A: autoslider Base Theme | Pattern B: Simple Theme Wrappers | Pattern C: Standalone Formats | How to Apply Your Custom Formats | Example: Visualize the output of your format implementation

Overview | Prerequisite | Speaker notes workflow | Step 1: Initial Slide Generation | Step 2: Adding AI Footnotes | Example: Using a Local LLM | Step 3: Create the Final PowerPoint File | Story slides workflow | Step 1: Generate the content deck | Step 2: Add the AI story | Under the hood | From the MCP server | Appendix | Running Your Local LLM

Overview | Generating a Deck with append_all_slides | Example: Generating placeholder slides | Controlling slide text with metadata tokens | Notes

Overview | List available templates | Generate a template for your own table | Notes | Customize the template

Introduction | The criterion | Basic usage | Choosing a test based on the criterion | Empty strata | References

Introduction | Outputs that can be decorated | Decorating ggplot

Overview | Prerequisites | Example 1: Claude Code as the MCP client | Step 1 — Find the server script path | Step 2 — Register the server | Step 3 — Verify the server is registered | Step 4 — Drive the pipeline conversationally | Step 5 — Use your own data | Example 2: Ollama local model (DeepSeek) for AI notes | Step 1 — Install Ollama and pull a model | Step 2 — Register the server (no API key required) | Step 3 — Ask for Ollama-backed notes | Running R in a Docker container? | Calling the R functions directly | Choosing a provider

Introduction | A Brief Review | Split Function Basics | Default Faceting | Recognizing Non-Full-Factorial Faceting | Excluding Factor Levels | Adding Combination Levels | Nested Faceting On Non-Independent Variables | Facets That Vary Meaning Instead of Data Subset | Combining These Faceting Needs

Introduction | Outputs that can be decorated | Decorating ggplot | Decorating grob | Decorating gtable

Introduction | Outputs that can be decorated | Decorating listing_df, ElementaryTable, TableTree | Decorating ggplot | Decorating datatables

Introduction | Main Features | A Simple Application | Try it out in Shinylive

Common Clinical Trials Analyses | Analytical Functions for rtables | Clinical Trial Visualizations | Interactive Apps | Summary

tern Tabulation | tern Analyze Functions | Internals of tern Analyze Functions | Tabulation Examples | Demographic Table | Adverse Event Table | Statistics Functions | Response Table | Summary

teal application to use scatter plot matrix with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

Coming Soon

Analysis and Group Summary Function Review | General Analysis/Summary Function Structure | Arguments To Analysis Functions | afun Special Arguments: Marginal Counts | Marginal Column and Row Counts (.N_col and .N_row) | Other Marginal Count Special Argument | Further Topics On Creating Custom Analysis Functions

Coming Soon

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Split Function Behavioral Building Blocks | Existing BBBs provided by rtables | Creating Post Processing BBBs | Illustrative And/Or Useful Examples | Placing Specific Facets First And/Or Last | Pre-sorting Or Pre-pruning Facets Based On Data Sparsity | Creating Preprocessing BBBs

Introduction | Getting Started | Content Information | Summary

Introduction | Demographic Table | Variations on the Demographic Table | Using Layouts | Adverse Events | Adverse Events By ID | Adverse Events By ID and By Grade | Response Table | Time to Event Analysis Table

Introduction | Data Pre-Processing | Creating Helper Functions: Cox Regression Model Calculations | tidy Method for summary.coxph Objects: tidy.summary.coxph | Function to Estimate Interaction Effects: h_coxreg_inter_effect | Function to Extract Effect Information: h_coxreg_extract_interaction | Creating a Helper Function: cached_model | Creating the Analysis Function: a_cox_summary | Selecting Parameters | Constructing the Table

Introduction | Getting Started | Nested Tables | NA Values | Custom Aggregation | Marginal Summaries | Table Decorations | Summary

Formats Precedence | Format Precedence and Inheritance Rules | Standard Format | Cell Format | Parent Table Format and Inheritance | NA Handling | Replacing NA Values at the Cell Level | Parent Table Replacement of NA Values and Inheritance Principles

Disclaimer | Creating an Example Table | Converting the Table to a Result Data Frame (ARD) | Customizing the Output | More Complex Outputs

Introduction | Overview | Data | Building a Table | Quick Start | Layout Instructions | Adding Column Structure | Adding Row Structure | Adding Group Information | Comparing with Other Tabulation Frameworks | Summary

Introduction | Getting Started | Detailed Table Structure | Insights on Value Format Structure | Applications | Summary

Introduction | Traditional Subsetting and modification with [ | Dealing with titles, foot notes, and top left information | Path Based Cell Value Accessing:

GENERAL | General Concepts | 1. Input dataset and dataset names | 2. Expected variables in input analysis dataset | 3. Character vs Factor | 4. Testing the codes for plot generation | General Control Arguments | 1. lbl_overall: Column of Total | 2. Column counts: N=xxx | TABLES | Safety Summary (AET01) | 1. Safety Summary | 2. Safety Summary with Modified Rows | Safety Summary (Adverse Events of Special Interest) (AET01_AESI) | 1. Safety Summary (Adverse Events of Special Interest) | 2. Safety Summary (Adverse Events of Special Interest) (optional lines) | 3. Safety Summary (Adverse Events of Special Interest) (for studies with multiple drugs) | Adverse Events (AET02) | 1. Adverse Events | 2. Adverse Events (with High-level Term) | 3. Adverse Events (Preferred Terms only) | Adverse Events by Greatest Intensity(AET03) | 1. Adverse Events by Greatest Intensity | Adverse Events by Highest NCI CTCAE Grade (AET04) | 1. Adverse Events by Highest NCI CTCAE Grade | 2. Adverse Events by Highest NCI CTCAE Grade (Fill in of Grades) | 3. Adverse Events by Highest NCI CTCAE Grade with modified grouping of grade | Adverse Event Rate Adjusted for Patient-Years at Risk - First Occurrence (AET05) | 1. Adverse Event Rate Adjusted for Patient-Years at Risk - First Occurrence | 2. Adverse Event Rate Adjusted for Patient-Years at Risk - First Occurrence (setting type of confidence interval) | Adverse Event Rate Adjusted for Patient-Years at Risk - All Occurrences (AET05_ALL) | 1. Adverse Event Rate Adjusted for Patient-Years at Risk - All Occurrences | 2. Adverse Event Rate Adjusted for Patient-Years at Risk - All Occurrences (setting type of confidence interval) | Most Common (>=5%) Adverse Events (AET10) | 1. Most Common (>=5%) Adverse Events | 2. Most Common (>=8%) Adverse Events (setting threshold) | Absolute Value and Change from Baseline by Visit (CFBT01) | 1. Absolute Value and Change from Baseline by Visit | 2. Absolute Value and Change from Baseline by Visit without Screening | 3. Absolute Value by Visit | Concomitant Medications by Medication Class and Preferred Name (CMT01A) | 1. Concomitant Medications by Medication Class and Preferred Name | 2. Concomitant Medications by Medication Class and Preferred Name (changing ATC class level) | 3. Concomitant Medications by Medication Class and Preferred Name (classes sorted by frequency) | 4. Concomitant Medications by Medication Class and Preferred Name (total number of treatments per medication class suppressed) | Concomitant Medications by Preferred Name (CMT02_PT) | 1. Concomitant Medications by Preferred Name | Cox Regression (COXT01) | 1. Cox Regression | 2. Cox Regression (with interaction term) | 3. Cox Regression (specifying covariates) | 4. Cox Regression (setting strata, ties, and alpha level) | Multi-variable Cox Regression (COXT02) | 1. Multi-variable Cox Regression | 2. Multi-variable Cox Regression (specifying covariates) | 3. Multi-variable Cox Regression (setting strata, ties, and alpha level) | Demographics and Baseline Characteristics (DMT01) | 1. Demographics and Baseline Characteristics with All Patients | 2. Demographics and Baseline Characteristics without All Patients | 3. Demographics and Baseline Characteristics with an additional study specific continuous variable | 4. Demographics and Baseline Characteristics with an additional study specific categorical variable | 5. Demographics and Baseline Characteristics with additional vital signs baseline values from ADVS or ADSUB | Patient Disposition (DST01) | 1. Patient Disposition | 2. Patient Disposition (with grouping of reasons) | 3. Patient Disposition (adding end of treatment status) | 4. Patient Disposition (adding details of study ongoing status) | Deaths (DTHT01) | 1. Deaths | 2. Deaths (adding "Primary Cause of Death" details for 'Other' category) | 3. Deaths (adding summary by days from last study drug administration) | ECG Results and Change from Baseline by Visit (EGT01) | 1. ECG Results and Change from Baseline by Visit | ECG Abnormalities (Regardless of Abnormality at Baseline) (EGT02_1) | 1. ECG Abnormalities (Regardless of Abnormality at Baseline) | ECG Abnormalities (Among Subject Without Abnormality at Baseline) (EGT02_2) | 1. ECG Abnormalities (Among Subject Without Abnormality at Baseline) | Shift Table of ECG Interval Data - Baseline versus Minimum/Maximum Post-Baseline (EGT03) | 1. Shift Table of ECG Interval Data - Baseline versus Minimum Post-Baseline | 2. Shift Table of ECG Interval Data - Baseline versus Maximum Post-Baseline | ECG Actual Values and Changes from Baseline by Visit (EGT05_QTCAT) | 1. ECG Actual Values and Changes from Baseline by Visit | 2. ECG Actual Values and Changes from Baseline by Visit (removing default analyses) | Study Drug Exposure (EXT01) | 1. Study Drug Exposure | Laboratory Test Results and Change from Baseline by Visit (LBT01) | 1. Laboratory Test Results and Change from Baseline by Visit | 2. Laboratory Test Results and Change from Baseline by Visit (customized precision) | Laboratory Abnormalities (LBT04) | 1. Laboratory Abnormalities | Laboratory Abnormalities with Single and Replicated Marked (LBT05) | 1. Laboratory Abnormalities with Single and Replicated Marked | 2. Laboratory Abnormalities with Single and Replicated Marked showing all categories | 3. Laboratory Abnormalities with Single and Replicated Marked with study specific MLAs | Laboratory Abnormalities by Visit and Baseline Status (LBT06) | 1. Laboratory Abnormalities by Visit and Baseline Status | Laboratory Test Results with Highest NCI CTCAE Grade Post-Baseline (LBT07) | 1. Laboratory Test Results with Highest NCI CTCAE Grade Post-Baseline | Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (LBT14) | 1. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (High) | 2. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (Low) | 3. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (High) Without Patients with Missing Baseline | 4. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (Low) with Missing Baseline Considered as Grade 0 | 4. Laboratory Test Results Shift Table - Highest NCI-CTCAE Grade Post-Baseline by Baseline NCI-CTCAE Grade (with fill in of grades) | Medical History (MHT01) | 1. Medical History | 2. Medical History showing additional column 'All Patients' | Major Protocol Deviations (PDT01) | 1. Major Protocol Deviations | Reasons for Major Protocol Deviations Related to Epidemic/Pandemic (PDT02) | 1. Reasons for Major Protocol Deviations Related to Epidemic/Pandemic | Duration of Exposure for Risk Management Plan (RMPT01) | 1. Duration of Exposure for Risk Management Plan | Extent of Exposure by Age Group and Gender for Risk Management Plan (RMPT03) | 1. Extent of Exposure by Age Group and Gender for Risk Management Plan | Extent of Exposure by Ethnic Origin for Risk Management Plan (RMPT04) | 1. Extent of Exposure by Ethnic Origin for Risk Management Plan | Extent of Exposure by Race for Risk Management Plan (RMPT05) | 1. Extent of Exposure by Race for Risk Management Plan | Best Overall Response (RSPT01) | 1. Best Overall Response | 2. Best Overall Response (Ordering of treatment groups) | 3. Best Overall Response (selecting sections to display) | 4. Best Overall Response (with stratified analysis) | 5. Best Overall Response (modifying analysis details like type of confidence interval, alpha level, test for p-value) | 6. Best Overall Response (modifying the definition of overall response) | Time-to-event Summary (TTET01) | 1. Time-to-event Summary | 2. Time-to-event Summary (selecting sections to display) | 3. Time-to-event Summary (modifying analysis details like confidence interval type, ties, and alpha level) | 4. Time-to-event Summary (with stratified analysis) | 5. Time-to-event Summary (modifying time point for the "survival at xx months" analysis) | 6. Time-to-event Summary (modifying the p-value method for testing hazard ratio) | Vital Signs (VST01) | 1. Vital Sign Results and Change from Baseline by Visit | Vital Signs Abnormalities (Regardless of Abnormality at Baseline) (VST02_1) | 1. Vital Sign Abnormalities (Regardless of Abnormality at Baseline) | Vital Signs Abnormalities (Among Subject Without Abnormality at Baseline) (VST02_2) | 1. Vital Sign Abnormalities (Among Subject Without Abnormality at Baseline) | LISTINGS | Glossary of Adverse Event Preferred Terms and Investigator-Specified Terms (AEL01_NOLLT) | 1. Glossary of Adverse Event Preferred Terms and Investigator-Specified Terms | Graphics | Forest Plot for Odds Ratio (FSTG01) | 1. Forest Plot for Odds Ratio (with subgroup analysis) | 2. Forest Plot for Odds Ratio (with a user-defined confidence level) | 3. Forest Plot for Odds Ratio (with p-values and/or different statistics) | 4. Forest Plot for Odds Ratio (with user-defined subgroup analysis) | 5. Forest Plot for Odds Ratio (with stratified analysis) | 6. Forest Plot for Odds Ratio (without proportional sizing of the odds ratio symbol) | Forest Plot for Hazard Ratio (FSTG02) | 1. Forest Plot for Hazard Ratio (with subgroup analysis) | 2. Forest Plot for Hazard Ratio (with p-values and/or different statistics) | 3. Forest Plot for Hazard Ratio (with user-defined subgroup analysis) | 4. Forest Plot for Hazard Ratio (with stratified analysis) | 5. Forest Plot for Hazard Ratio (without proportional sizing of the hazard ratio symbol) | Kaplan-Meier Plot (KMG01) | 1. Kaplan-Meier Plot (without comparative statistics) | 2. Kaplan-Meier Plot (with comparative statistics) | 3. Kaplan-Meier Plot (without censoring marks) | 4. Kaplan-Meier Plot (without estimation of median survival time) | 5. Kaplan-Meier Plot (with statistical annotation of either median or min of survival time) | 6. Kaplan-Meier Plot (without the table of patients at risk) | Mean Plot (MNG01) | 1. Plot of Mean and Confidence Interval (with Table Section) | 2. Plot of Mean and Confidence Interval of Change from Baseline of Vital Signs | 3. Plot of Mean (+/-SD) (Changing the Statistics) | 4. Plot of Mean and Confidence Interval (Modify Alpha Level) | 5. Plot of Mean and Confidence Interval （With Number of Patients Only) | 6. Plot of Mean and Confidence Interval (without Table Section)

tern Formatting Functions Overview | Comparing tern & formatters Formats | Formatting Function Basics | Number of Decimal Places to Display | Value Thresholds | Creating Custom Formatting Functions | Summary

Variable Class Conversion | Including Missing Values in rtables | Missing Values in Numeric Variables

For Developers

Definition | Implementation | Examples | Example 1 | Example 2 | Example 3 | Example 4 | References

NOTE | Control splitting with provided function (limited customization) | Custom Split Functions | Adding an Overall Column Only When The Split Would Already Define 2+ Facets | Leveraging .spl_context | What Is .spl_context? | Different Formats For Different Values Within A Row-Split | Simulating 'Baseline Comparison' In Row Space

The Old Way | Setting column counts to visible at Layout time | Modifying counts on an existing table | Hiding counts | Higher Level Column Counts | Setting Column Counts to Visible at Layout Time | Manipulating Column Counts In An Existing Table | Setting individual column counts | Setting Col Count Visibility | Advanced Settings

Introduction | Row Splitting

Controlling Facet Levels | Provided Functions | Controlling Combinations of Levels Across Multiple Variables | trim_levels_in_group | trim_levels_to_map | Combining Levels | add_overall_level | add_combo_levels | Fully Customizing Split (Facet) Behavior | An Example Custom Split Function | An Illustrative Example of A Custom Split Function | Overriding the Core Split Function | Design of Pre- and Post-Processing Functions For Use in make_split_fun

Rows and cell values alignments | Top-left Materials | Table Inset | Horizontal Separation | Section Dividers | Indent Modifier | Variable Label Visibility | Cell, Label, and Annotation Wrapping | Cell & Label Wrapping | Title & Footer Wrapping

Introduction | A Table Shell | A Brief Review Of rtables Layouts | Translating | Translating Column Structure | Translating Row Structure | Translating Cell Contents | Spanning Column Headers | Heterogeneous Column Structures (e.g., Risk Difference Columns) | Risk Difference Columns | Column-structure Aware Analysis Functions | Mixed Nesting Levels

Introduction | A Table In Need of Attention | Trimming | Trimming Rows | Trimming Columns | Pruning | Sorting | Sorting Fundamentals | Wildcards in Sort Paths | Sorting Within an Analysis Subtable | Writing Custom Pruning Criteria and Scoring Functions | Useful Functions and Accessors | Example Custom Scoring Functions | Sort by a character "score" | Sort by the Percent Difference in Counts Between Genders in Arm C

Introduction | Building A Table Row By Row | Tabulation With Row Structure | Calculating the Mean | Group Summaries | Pattern III

Titles and Non-Referential Footer Materials | Page-by splitting | Referential Footnotes | Adding Cell- and Analysis-row Referential Footnotes At Tabulation Time | Annotating an Existing Table with Referential Footnotes

Motivation | Setting an option | Options used in a teal application | teal.bs_theme (bslib::bs_theme object) | teal.load_nest_code (character) | teal.threshold_slider_vs_checkboxgroup (numeric) | teal.basic_table_args (basic_table_args object) | teal.ggplot2_args (ggplot2_args object) | teal.plot_dpi (integer value 24 or larger) | teal.log_layout (character) | teal.log_level (character) | teal.show_js_log (logical) | teal.lockfile.mode (character) | teal.sidebar.position (character) | teal.sidebar.width (numeric) | teal.reporter.nav_buttons (character) | teal.reporter.rmd_output (character) | teal.reporter.rmd_yaml_args (character) | teal.reporter.global_knitr (list) | teal.reporter.max_request_size (numeric) | teal.show_src (logical) | teal.enable_deep_linking (logical) | teal.snapshot_manager.enable (logical) | Deprecated options | teal_logging | teal_show_js_log | teal.ui.class (character(1))

Introduction | Outputs that can be decorated | Decorating ElementaryTable | Decorating ggplot | Decorating grob | Decorating datatables | Decorating patchwork

Introduction | An Illustrative Example | Row Paths | Column Paths | Uniqueness of Paths | Wildcards In Paths | Operating On Tables With Paths | Column Counts | Section Dividers | Sorting Within An rtables Table | Extracting And Modifying Values | Footnotes | Table Structure And Technical Details | Row Structure | Label And Group Summary Rows | Column Structure

1 Retrieved dropout models in a nutshell | 2 Data simulation using function simulate_data() | 3 Estimators based on retrieved dropout models | 4 Implementation of the defined retrieved dropout models in rbmi | 4.1 Basic MAR model | 4.2 Retrieved dropout model 1 (RD1) | 4.3 Retrieved dropout model 2 (RD2) | 4.4 Brief summary of results | References

Introduction | Your first teal application: | Try the above app in shinylive | Creating your own applications | Application data | Modules | Defining filters | Reporting | Reproducible code | Embedding teal in shiny application | Where to go next

Introduction | How to Transform outputs? | Requirements and Limitations | Building Output Transformations (Decorators) | Server | UI | Variable Names as Arguments | Using Output Transformations (Decorators) | Single Transformation (Decoration) | Module | Application | Transforming Multiple Outputs (Decorators) | Transformators | Convenience | Reducing Boilerplate | Multiple Transformations

Introduction | Usage | teal.bs_theme R option | Bootstrap version and themes | Default Bootstrap theme | Reset the Bootstrap theme | Theme not updated | Custom teal CSS | Bootstrap NULL vs Bootstrap 3 | Regular shiny::fluidPage | Interactive theming guide | Set overall app theme | Customize a bootswatch theme | Apply the customized theme | Try the above app in shinylive | Sources

Introduction | Default Logging Setup of teal.logger | Customizing the Log Level | Customizing the Log Layout | Customizing the Log Destination | Logging in Other teal.X Packages | Example of Usage

Generating minimal data to test teal.modules.clinical | Setup & Helper Functions | ADSL | ADAE | ADAETTE | ADCM | ADEG | ADEX | ADLB | ADMH | ADQS | ADRS | ADTTE | ADVS | Generate Data

Introduction to qenv | Initialization | qenv basic usage | Substitutions | Combining qenv objects | Warnings and messages in qenv objects | Utilizing qenv inside shiny applications | Reproducibility | qenv and teal applications

Introduction

Introduction | Who Are Your Numbers And What Do They Do? | There's Counting And Then There's Counting... | Denominators | "Yeah But What If" ... Conditionality In afuns | Conditionality Based On Column Facet | Conditionality Based On Row Facet | Why Not Both? | A Final Note On afun Complexity

Introduction | Creating a teal_report | Adding content to the teal_report | Adding arbitrary markdown content | Adding reproducible code chunks | Modify teal_report content | Document metadata | Preview report | Output teal_report | Key Benefits | Further Reading

Scope and Audience | Chapters

Introduction | Integration with teal | Automatic Report Functionality | More control

Introduction | Reporter Modules | Module Overview

Introduction | Custom module | Add support for reporting | Modify the declaration of the server function | Return the reporter object | Add content to the card | Add non-text content to the card | Removing support for displaying reproducible code | Removing reporting | Customizing the reporter

Introduction | Understanding the Inputs and Requirements | Setting Up the teal Module UI | Setting Up the teal Module Server | Passing the data Argument to the Server Function | Understanding teal_data as a Reactive Object in Server Logic | Using names() to Access Dataset Names in teal_data object | Accessing Specific Datasets with Double Brackets ([[ ]]) | Setting Up Server Logic Using teal_data and Dynamic Variable Injection | Creating the Custom teal Module Function | Integrating the Custom teal Module into a teal App | What's next? | Adding reporting to a module | Using standard widgets in your custom module

Acknowledgments | Installation | BioConductor | GitHub | Introduction | Importing Data | Importing a SummarizedExperiment | Importing an ExpressionSet | Importing a Matrix | Annotations | Connection to Database | Querying and Saving Annotations | Quality Control Flags | Automatic Gene and Sample Flagging | Manual Sample Flagging | Accessing Flags | Filtering Data | Based on Default QC Flags | Based on Custom Variables | Normalizing Counts | Descriptive Plots | Simple Plots | Top Genes | Heatmap of Genes among Samples | Correlation between Samples | Principal Components | PCA of Samples | Correlation with Sample Variables | QC Report Template | Differential Expression | Summary | Session Info

Filter Panel API | Getting and setting filter states | 1. Setting the filter state | 2. Updating filter states. *Works only in the shiny reactive context. | 3. Getting the filter state | 4. Removing filter states | 5. Clearing the filter state | Controlling settings of the filter panel | Filter panel as a module

Introduction | The Basics | NSE Principle | Replace an object name | What about dataframe names? | In a nutshell | rtables | Direct use of substitute | substitute in a function | Chain expressions in a pipeline | Including pre-processing

Data in teal Applications | General data | Reproducible data | code from file | Creating data in-app | Clinical data | ADaM data | MultiAssayExperiment data | teal_data properties | join_keys | verified | Hidden datasets | Further reading

Example of using tern.rbmi | The Data | Creating intercurrent event data | Draws | Define key variables | Impute | Analyze | Pool | Create output

Overview | Step 1: Write your template | Step 2: Create a softlink in inst/templates/ | Step 3: Verify with list_all_templates() | Step 4: Generate with use_template()

Step 1/5 - Preparing the Data | Step 2/5 - Creating data extract specifications | Step 3/5 - Creating the UI | Step 4/5 - Creating the Server Logic | Step 5/5 - Creating the shiny App | Try it out in Shinylive

data_extract_spec | Step 1/4 - Preparing the Data | Step 2/4 - Creating a data_extract_spec Object | Step 3/4 - Creating the shiny UI and Server Modules | Step 4/4 - Creating the shiny App | Try it out in Shinylive

merge_expression_module | Step 1/5 - Preparing the Data | Step 2/5 - Creating the Data Extracts | Step 3/5 - Creating the UI | Step 4/5 - Creating the Server Logic | Step 5/5 - Creating the shiny App | data_extract_multiple_srv + merge_expression_srv | Modifying the Server Logic | Updating the shiny app | Output from merging | Merging of non CDISC datasets

Advanced Customizations | Column Widths | Pagination | Horizontal separators (note section_div = )

Introduction | Creating a data transformation module | Single transformator | Multiple transformators | Targeting multiple datasets | Targeting one dataset

Introduction | Data Extraction and Data Merging

Introduction | Creating data in-app | Modification of data in-app | WARNING | Extending existing teal_data_modules

Introduction | Example

teal apps with the filter panel | Extending teal.slice | Filter panel respective to teal_module | Global and module specific filter panel

Introduction | Information for users of the filter panel in applications | Information for R developers using the filter panel

Introduction | Main features | Example application | Try it out in Shinylive

teal application to use association plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use bivariate plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use cross table with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to display data table with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to analyze and report outliers with various datasets types. | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use regression plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use response plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

teal application to use scatter plot with various datasets types | 1 - Load libraries | 2 - Create data sets | 3 - Create an app variable | 4 - Run the app | 5 - Try it out in Shinylive

Additional topics | Loading libraries and data

Example - Single-Table Module

Introduction | Quick Start | Reproducibility | Relational data models | Hidden objects

Introduction | Referential Footnotes Workaround | Summary

Introduction | Main features | A simple application

Overview | Uses of join_keys class in teal applications | Anatomy of join_keys | join_keys(...): Join Keys Constructor / Getter / Setter | join_key(dataset_1, dataset_2, key, parent): Single Join Key Constructor | Example & Output | Accessing and Modifying keys | Merging Join Keys | Join Keys Relationships | Primary Key with teal_data | Foreign Keys with teal_data | Implicit Relationships | Join Keys in ADaM Datasets

Reproducibility of teal_data objects | Verification | Verification status | Verification process | verified | unverified | Retrieving code | Tracking object dependencies

Introduction | Default Formatting in as_listing | Column-Wise Formatting in as_listing | Adding Formatted Columns to a Listing via add_listing_col | Summary

Introduction | Descriptive Statistics | Methodology | Statistical model | Estimation | Inference | Covariance structure | Model fitting | Setup | Model fitting via tern.mmrm | Model fitting manually in R | Model fitting via SAS | Diagnostics | Residual analysis | Outlook

Acknowledgments | Installation | BioConductor | GitHub | Introduction | Importing Data | Importing a SummarizedExperiment | Importing an ExpressionSet | Importing a Matrix | Annotations | Connection to Database | Querying and Saving Annotations | Quality Control Flags | Automatic Gene and Sample Flagging | Manual Sample Flagging | Accessing Flags | Filtering Data | Based on Default QC Flags | Based on Custom Variables | Normalizing Counts | Descriptive Plots | Simple Plots | Top Genes | Heatmap of Genes among Samples | Correlation between Samples | Principal Components | PCA of Samples | Correlation with Sample Variables | QC Report Template | Differential Expression | Summary | Session Info

Pull and run the docker image | Open the RStudio in the browser | Clean pkgcache cache before repeated runs | Run the code below inside the container

Introduction | Common Clinical Trials Analyses | Interactive Apps | Data Requirements | Summary

Introduction | Basics of Pagination | Alternative Methods to Specify Page Size | 1. pg_width and pg_height | 2. lpp and cpp | 3. colwidths | Pagination with Exporters | export_as_txt | export_as_rtf | Pagination by Parameter | Combining Pagination by Parameter with Regular Pagination | Combining Pagination by Parameter with export_as_txt | Summary

Introduction | Building a Listing | Key Columns | Titles and Footers | Summary

Introduction | Ad-hoc module example | UI function | Server function | App function | Testing it

Getting Started | Code Maintenance | Update Cached Data

Introduction | The Formatting Map Structure | Calling reformat | Example | Rule Attributes

Introduction | Initialization | Class Description | FilteredData | FilteredDataset | FilterStates | FilterState | FilterStateExpr | teal_slice | Making a reproducible filter call | Overview | Example | Filter panel modules

1 Introduction | 2 Data and model specification | 3 Reference-based conditional mean imputation - frequentist inference | 3.1 Draws | 3.2 Impute | 3.3 Analyse | 3.4 Pool | 4 Reference-based conditional mean imputation - information-anchored inference | 4.1 Draws | 4.2 Imputation step including calculation of delta-adjustment | 4.3 Analyse | 4.4 Pool | References

formatters | Formatting values | Format labels | Table Rendering Framework

Introduction | Cor(PFS, OS): Statistical Background | Example: Computing Cor(PFS, OS) directly | Estimating Cor(PFS, OS) from data | Example: Estimating Cor(PFS, OS) from data | References

Introduction | Simulation Specifications | Application | Output Data | References

Introduction | Overall survival calculation | Implementation

Introduction | Scenario - PFS and OS as Co-primary Endpoints | Type I Error - Simulation Under $H_0$ | Sample size and power calculation - simulation under $H_1$ | References

Introduction | TLG-functions | preprocessing | postprocessing | Example AET02 | chevron_tlg object customization | Custom chevron_tlg object creation

Introduction | Using a chevron-defined object | With a modified chevron object

Documentations | Short Tutorial

Introduction | ADSL | ADLB | Other Basic Data Structures | Required Variables | TRTORD | AVISITCD | AVISITCDN | AVALU | LBSTRESC | LOQFL | BASE2 | CHG2 | PCHG2 | Additional Variables | AVALL2, BASEL2, BASE2L2

PARAMCD | Pull down menus | AVISIT | The Data Constraint Filter | Data Point Brushing | Log2 - Points to Consider | Right Hand Data Filter Panel | Visualization Specifics

Overview | Simple Example

Introduction | Main Features | A Simple Example

1 Introduction | 2 Data simulation using function simulate_data() | 3 Handling of observed post-ICE data in rbmi under reference-based imputation | 4 Efficiently changing reference-based imputation strategies | 5 Imputation under MAR with time-varying covariates | 6 Custom imputation strategies | 7 Custom analysis functions | 8 Sensitivity analyses: Delta adjustments and tipping point analyses | 8.1 Simple delta adjustments and tipping point analyses | 8.2 More flexible delta-adjustments using the dlag and delta arguments of delta_template() | References

1 Introduction | 2 The Data | 3 Draws | 4 Impute | 5 Analyse | 6 Pool | 7 Code

1 Scope of this document | 2 Introduction to estimands and estimation methods | 2.1 Estimands | 2.2 Alignment between the estimand and the estimation method | 3 Statistical methodology | 3.1 Overview of the imputation procedure | 3.2 Setting, notation, and missing data assumptions | 3.3 The base imputation model | 3.4 Imputation step | 3.5 \(\delta\)-adjustment | 3.6 Analysis step | 3.7 Pooling step for inference of (approximate) Bayesian MI and Rubin’s rules | 3.8 Bootstrap and jackknife inference for conditional mean imputation | 3.9 Pooling step for inference of the bootstrapped MI methods | 3.10 Comparison between the implemented approaches | 4 Mapping of statistical methods to rbmi functions | 5 Comparison to other software implementations | References