R Packages for TLGs in Clinical Research: Tables, Listings, Graphs, and Interactive Reporting

Introduction

In clinical research, data becomes meaningful only when it is transformed into clear, interpretable, and decision-ready outputs. Clinical trial teams collect and process large volumes of data, but the final interpretation often depends on well-designed Tables, Listings, and Graphs, commonly known as TLGs.

TLGs are also referred to as TLFs or TFLs, where “F” stands for Figures. These outputs are central to clinical study reporting, regulatory submission, safety review, efficacy evaluation, and internal decision-making.

In simple terms, TLGs take clinical trial data and convert it into human-interpretable insights. They help clinical teams, statisticians, medical reviewers, sponsors, and regulatory authorities understand what happened during a clinical study.

Today, R is increasingly used to generate clinical trial TLGs because it supports reproducible programming, automation, advanced visualization, and modern reporting workflows. Several R packages are now available to support static tables, listings, figures, interactive applications, and output frameworks.

This article provides an overview of important R packages used for TLG generation in clinical research.

What are TLGs?

TLGs stand for:

• Tables

• Listings

• Graphs

They are the final statistical and clinical outputs generated from clinical trial datasets, usually from ADaM datasets and sometimes from SDTM or derived analysis datasets.

Tables

Tables summarize clinical trial data in a structured format. Examples include:

• Demographics and baseline characteristics

• Subject disposition

• Adverse event summaries

• Laboratory summaries

• Vital signs summaries

• Efficacy endpoint summaries

• Concomitant medication summaries

Listings

Listings present subject-level data in detail. Examples include:

• Adverse event listings

• Serious adverse event listings

• Laboratory abnormality listings

• Protocol deviation listings

• Concomitant medication listings

• Subject disposition listings

Graphs

Graphs visually represent clinical trial data. Examples include:

• Kaplan-Meier survival curves

• Forest plots

• Box plots

• Line plots

• Bar charts

• Shift plots

• Waterfall plots

• Safety trend plots

Static and Interactive TLGs

There are two important stages or categories of TLGs in clinical reporting:

1. Static TLGs

Static TLGs are fixed outputs, usually generated as RTF, PDF, Word, HTML, or other report-ready formats. As of today, static TLGs remain the primary type of evidence submitted to regulatory authorities.

Static TLGs are commonly used in:

• Clinical study reports

• Regulatory submissions

• Safety reports

• Interim analysis reports

• Data monitoring committee packages

• Integrated summaries of safety and efficacy

• Publication support

Static outputs must be accurate, reproducible, validated, and aligned with the statistical analysis plan.

2. Interactive TLGs

Interactive TLGs are dynamic outputs, often created using Shiny applications or interactive dashboards. These tools allow users to filter, explore, drill down, and visualize clinical trial data interactively.

Interactive TLGs are commonly used for:

• Medical data review

• Safety surveillance

• Exploratory analysis

• Internal decision-making

• Clinical data review meetings

• Study team dashboards

• Risk-based monitoring support

Although interactive TLGs are not usually the primary submission evidence, they are becoming increasingly valuable for internal clinical review and data exploration.

Role of R in TLG Generation

R is well suited for clinical TLG generation because it supports:

• Statistical analysis

• Data transformation

• High-quality graphics

• Reproducible reporting

• Automated output generation

• Interactive applications

• Metadata-driven reporting

• Flexible table and figure formatting

R packages can help clinical programmers create standard outputs more efficiently while maintaining transparency and reproducibility.

Recommended Reading: Tables in Clinical Trials with R

For clinical trial tables, the R Consortium – Tables in Clinical Trials with R initiative is a useful reference. It compares examples from multiple R table packages and helps programmers understand different approaches to clinical table generation.

This type of resource is especially useful for clinical programmers, statisticians, and students who want to compare how different R packages handle table creation, formatting, and reporting.

R Packages for Clinical Trial Tables

1. rtables

rtables is a framework for declaring complex multi-level tabulations and applying them to clinical trial data.

It is especially useful for generating structured clinical summary tables such as:

• Demographics tables

• Adverse event tables

• Laboratory summary tables

• Vital signs tables

• Exposure tables

• Efficacy summary tables

Key Strengths

• Supports complex hierarchical tables

• Useful for clinical trial reporting

• Allows flexible row and column structures

• Supports multi-level summaries

• Suitable for regulatory-style outputs

rtables is often used when clinical tables require detailed layout control and complex summary structures.

2. chevron

chevron holds standard TLG template structures to create clinical trial reporting outputs with limited parameterization.

This package is useful when organizations want to standardize commonly used outputs across studies.

Key Strengths

• Provides standard table templates

• Reduces repetitive programming

• Supports consistent reporting formats

• Useful for standard safety and efficacy outputs

• Helps accelerate TLG production

chevron is particularly helpful for organizations that want reusable templates for clinical trial reporting.

3. pharmaRTF

pharmaRTF is an enhanced RTF wrapper written in R. It can be used with existing R table packages such as huxtable or gt.

RTF remains an important format in clinical reporting because many regulatory and CSR outputs are prepared in RTF or Word-compatible formats.

Key Strengths

• Supports RTF output generation

• Works with existing table packages

• Helps create report-ready clinical outputs

• Supports headers, footers, pagination, and formatting

• Useful for regulatory-style table production

4. Tplyr

Tplyr is designed to simplify the data manipulation required to create clinical reports.

Clinical tables often require multiple layers of summarization, grouping, counting, percentage calculation, and formatting. Tplyr helps streamline these steps.

Key Strengths

• Simplifies clinical summary table creation

• Supports counts, percentages, and descriptive statistics

• Useful for safety and efficacy summaries

• Works well with tidy data workflows

• Reduces manual data manipulation

Tplyr is useful for programmers who want a structured way to create clinical tables using R.

5. gtsummary

gtsummary creates publication-ready and clinical-style summary tables from data frames or Analysis Results Dataset structures.

It is widely used for statistical summaries, baseline characteristics, regression summaries, and medical research reporting.

Key Strengths

• Easy to create summary tables

• Supports descriptive statistics

• Supports regression model summaries

• Useful for medical publications and clinical reports

• Can work with analysis result data structures

gtsummary is particularly useful for quick, clean, and readable clinical summary tables.

6. tfrmt

tfrmt provides a language for defining display-related metadata to automate transformation from Analysis Results Data into formatted tables.

This is important because clinical reporting often separates analysis results from display formatting. tfrmt helps define how results should appear in final outputs.

Key Strengths

• Supports metadata-driven table formatting

• Helps separate results from display rules

• Useful for standardized reporting

• Supports automation of formatted tables

• Works with analysis results data

7. tfrmtbuilder

tfrmtbuilder is a Shiny application interface for the tfrmt package.

It allows users to interactively build and review table formatting specifications. This can be useful for programmers and reporting teams who want a more visual way to define table layouts.

Key Strengths

• Interactive interface for table formatting

• Supports tfrmt workflows

• Helps users build display specifications

• Useful for training and review

• Reduces manual formatting effort

8. tidytlg

tidytlg helps generate tables, listings, and graphs using the tidyverse approach.

It is useful for programmers who are already familiar with tidy data principles and want to create clinical outputs using tidy-style workflows.

Key Strengths

• Supports tables, listings, and graphs

• Uses tidyverse-friendly syntax

• Helps generate clinical outputs

• Useful for reproducible workflows

• Suitable for training and practical implementation

9. eudract

eudract creates safety results summaries in XML format for upload to EudraCT or ClinicalTrials.gov.

This package is useful for clinical trial results disclosure and registry-related safety reporting.

Key Strengths

• Supports safety results summary generation

• Creates XML output

• Useful for EudraCT submissions

• Supports ClinicalTrials.gov-related workflows

• Helps with structured results reporting

ARD and ARS in Clinical Reporting

What is ARD?

ARD stands for Analysis Results Data. It is an emerging CDISC model for encoding statistical analysis summaries in a machine-readable format.

Instead of only producing human-readable tables, ARD allows analysis results to be stored in a structured format that can be reused, reviewed, and transformed into multiple output types.

What is ARS?

ARS stands for Analysis Results Standard. It is an overarching CDISC framework intended to standardize the specification and representation of analysis results using metadata.

Together, ARD and ARS support the future of metadata-driven clinical reporting.

R Packages for ARD and ARS

1. cards

cards helps construct CDISC Analysis Results Dataset objects.

Key Strengths

• Supports ARD object creation

• Helps structure analysis results

• Useful for metadata-driven reporting

• Supports machine-readable summaries

• Can support future automation workflows

2. cardx

cardx provides extra Analysis Results Data summary objects supplementary to cards.

Key Strengths

• Extends ARD summary capabilities

• Supports additional analysis result structures

• Works alongside cards

• Useful for advanced reporting workflows

3. siera

siera generates analysis results programs using ARS metadata.

Key Strengths

• Supports ARS metadata-driven programming

• Helps generate analysis programs

• Useful for standardized analysis result workflows

• Supports automation and reproducibility

R Packages for Clinical Listings

rlistings

rlistings is a framework for creating clinical data listings.

Listings are important because they provide detailed subject-level information. While tables summarize data, listings allow reviewers to inspect individual subject records.

Key Strengths

• Supports subject-level listings

• Useful for adverse event listings

• Useful for lab abnormality listings

• Supports clinical data review

• Helps create structured listing outputs

Common use cases include:

• AE listings

• SAE listings

• Death listings

• Protocol deviation listings

• Laboratory listings

• Concomitant medication listings

R Packages for Clinical Graphs and Figures

1. ggsurvfit

ggsurvfit simplifies the creation of time-to-event, or survival, summary figures.

Survival analysis is especially important in oncology and other therapeutic areas where endpoints such as overall survival, progression-free survival, and time to event are analyzed.

Key Strengths

• Supports Kaplan-Meier plots

• Useful for time-to-event analysis

• Helps create survival summary figures

• Works well with survival analysis workflows

• Useful for oncology and long-term outcome studies

2. ggplot2

ggplot2 is a lower-level, non-clinical-specific plotting package, but it is widely accepted as one of the most important graphics packages in R.

It is based on the Grammar of Graphics and is used to create high-quality static plots.

Key Strengths

• Highly flexible plotting system

• Supports many types of clinical graphs

• Widely used across industry and academia

• Can create publication-quality figures

• Works with many other R packages

Common clinical uses include:

• Line plots

• Bar charts

• Box plots

• Scatter plots

• Forest plots

• Waterfall plots

• Laboratory trend plots

• Safety visualizations

R Packages for Interactive Clinical Reporting

1. tidyCDISC

tidyCDISC is a Shiny application that helps users create custom tables and figures from ADaM-like datasets.

It is useful for exploratory clinical reporting and interactive review.

Key Strengths

• Interactive table and figure generation

• Works with analysis-style datasets

• Useful for data exploration

• Supports clinical review workflows

• Helps non-programmers interact with data

2. rhino

rhino supports creating and extending enterprise Shiny applications using best practices.

As Shiny applications become more common in clinical data review and reporting, enterprise-grade development practices become important. rhino helps structure larger Shiny applications in a more maintainable way.

Key Strengths

• Supports enterprise Shiny development

• Encourages modular app design

• Improves maintainability

• Useful for production-grade dashboards

• Helps teams scale Shiny applications

3. dataviewR

dataviewR is an interactive and feature-rich data viewer.

It helps users explore datasets in an interactive format, which can be useful during clinical data review, quality control, and exploratory analysis.

Key Strengths

• Interactive data viewing

• Useful for data review

• Supports exploratory analysis

• Helps inspect clinical datasets

• Can support QC and review activities

Framework Packages for TLG Output Production

Some R packages act as frameworks that allow different engines to generate final outputs. For example, they may work with packages such as ggplot2, rtables, plotly, or Tplyr.

These frameworks help standardize the final presentation of tables, listings, and figures.

1. clinify

clinify provides clinical table styling tools and utilities.

Key Strengths

• Supports clinical table styling

• Helps standardize output appearance

• Useful for regulatory-style tables

• Provides formatting utilities

• Supports consistent reporting presentation

2. gridify

gridify enriches tables and figures with custom headers, footers, and additional layout elements.

Key Strengths

• Adds headers and footers

• Supports custom output framing

• Helps improve presentation quality

• Useful for report-ready outputs

• Can support standardized layout requirements

3. docorator

docorator is a framework for framing a table, listing, or figure with headers and footers and saving it across multiple file types.

Key Strengths

• Supports headers and footers

• Works with tables, listings, and figures

• Saves outputs across multiple formats

• Useful for standardized reporting

• Helps automate final output generation

4. autoslider

autoslider supports slide automation for tables, listings, and figures.

This package is useful when clinical outputs need to be placed into presentation slides for internal review meetings, data monitoring committee meetings, or executive summaries.

Key Strengths

• Automates slide creation

• Supports TLG presentation workflows

• Useful for review meetings

• Helps reduce manual slide preparation

• Supports communication of clinical results

5. tern

tern provides analytical layers ranging from descriptive summaries to more complex statistics on top of foundational table layouts and content controls.

It is useful for clinical trial analysis and reporting where statistical summaries need to be integrated with table structures.

Key Strengths

• Supports descriptive and advanced statistics

• Works with table layouts

• Useful for clinical trial reporting

• Helps generate safety and efficacy summaries

• Supports statistical content within tables

6. teal

teal is a framework that uses R Shiny to support scalable development of interactive clinical applications.

It is useful for building reusable and modular clinical data review applications.

Key Strengths

• Supports interactive clinical data review

• Built on Shiny

• Helps scale application development

• Useful for exploratory analysis

• Supports reusable modules and dashboards

TLG Catalogs and Example Applications

For learners and clinical programmers, example catalogs are very helpful because they show how TLG outputs are structured and generated.

Useful resources include:

• TLG Catalog

• Biomarker Catalog

• Teal Gallery

These resources provide practical examples of clinical tables, figures, and interactive applications. They can be useful for training, reference, and implementation planning.

Upcoming R Package: cardinal

cardinal is an upcoming package being developed toward CRAN submission. It is intended to implement table-generating functions for standard FDA safety tables according to published guidelines.

This package may become useful for teams that want to generate standardized safety tables more efficiently.

Potential Benefits

• Supports FDA-style safety tables

• Helps standardize safety reporting

• Reduces manual programming effort

• Supports reproducible table generation

• Useful for safety analysis workflows

Why TLG Package Knowledge is Important for Clinical Programmers

Clinical programmers and statistical programmers are often responsible for generating final outputs used in clinical study reports, submissions, and internal reviews.

Knowledge of TLG packages helps professionals:

• Generate clinical tables efficiently

• Create subject-level listings

• Build high-quality clinical graphs

• Automate reporting workflows

• Support reproducible programming

• Improve quality control

• Prepare regulatory-style outputs

• Develop interactive clinical dashboards

Suggested Learning Path for Students

For students and professionals learning R-based clinical reporting, the following learning path can be useful:

Level 1: Foundation

• Understand TLGs and their role in clinical research

• Learn basic R data manipulation

• Learn dplyr, tidyr, and ggplot2

• Understand ADaM datasets used for reporting

Level 2: Tables

• Learn gtsummary

• Learn Tplyr

• Learn rtables

• Understand table shells and mock shells

• Practice demographics, AE, and lab summary tables

Level 3: Listings and Figures

• Learn rlistings

• Learn ggsurvfit

• Practice AE listings and lab listings

• Create Kaplan-Meier plots and safety graphs

Level 4: Output Formatting

• Learn pharmaRTF

• Learn clinify

• Learn gridify

• Learn docorator

• Practice RTF and report-ready output generation

Level 5: Interactive Reporting

• Learn Shiny basics

• Explore tidyCDISC

• Learn teal

• Understand enterprise Shiny development with rhino

Level 6: Metadata-Driven Reporting

• Understand ARD and ARS concepts

• Learn cards

• Learn cardx

• Explore tfrmt and siera

Career Relevance

R-based TLG generation is becoming an important skill for clinical research professionals. It is useful for roles such as:

• Clinical SAS Programmer

• R Clinical Programmer

• Statistical Programmer

• Biostatistician

• Clinical Data Scientist

• Clinical Trial Data Analyst

• Safety Programmer

• Regulatory Reporting Programmer

• Medical Data Reviewer

• Clinical Reporting Specialist

As the industry moves toward automation, reusable programming, and interactive data review, professionals who understand both clinical reporting concepts and R packages will have a strong career advantage.

Conclusion

TLGs are essential outputs in clinical research because they convert clinical trial data into meaningful, human-interpretable insights. Static TLGs remain central to regulatory submission and clinical study reporting, while interactive TLGs are becoming increasingly important for internal review, safety monitoring, and exploratory analysis.

R provides a strong ecosystem for generating tables, listings, graphs, interactive dashboards, and report-ready outputs. Packages such as rtables, chevron, pharmaRTF, Tplyr, gtsummary, tfrmt, tidytlg, rlistings, ggsurvfit, ggplot2, tidyCDISC, rhino, dataviewR, clinify, gridify, docorator, autoslider, tern, and teal support different parts of the TLG workflow.

For clinical programmers, biostatisticians, data scientists, and students, learning these R packages can improve practical readiness for clinical trial reporting, regulatory-style output generation, and modern clinical data review.