How to Create Tables, Listings, and Figures (TLFs) for Clinical Trial Statistical Analysis Plans

Tables, Listings, and Figures (TLFs) are the visual and tabular backbone of clinical trial data presentation. They transform complex datasets into interpretable formats for regulatory agencies, stakeholders, and scientific publications. TLFs must align with the Statistical Analysis Plan (SAP) and reflect the trial’s objectives and endpoints accurately. Developing TLFs is not merely a technical task—it’s a regulatory obligation and a critical step in data integrity assurance.

This tutorial outlines how to create, structure, and validate TLFs in accordance with ICH E3, USFDA, and industry standards.

What Are TLFs in Clinical Trials?

TLFs—Tables, Listings, and Figures—are standardized outputs generated as part of statistical reporting. Each type serves a unique purpose:

• Tables: Summarize key results numerically (e.g., demographic summaries, efficacy outcomes)
• Listings: Present raw or patient-level data line-by-line (e.g., adverse events, lab values)
• Figures: Visualize trends or distributions (e.g., Kaplan-Meier plots, box plots)

These elements form the core statistical outputs submitted in Clinical Study Reports (CSRs) and regulatory dossiers.

Why TLFs Are Crucial in SAP Implementation

• They ensure standardized interpretation of results
• Serve as evidence in regulatory submissions and audits
• Facilitate review by clinical, regulatory, and QA teams
• Are often re-used in publications and labeling claims

TLFs should be predefined and mock templates included in the SAP to ensure clarity and alignment across stakeholders.

TLF Development Lifecycle

Creating TLFs is a collaborative, multi-step process. Below is a typical workflow:

1. SAP Finalization

• Defines endpoints, populations, and statistical methods
• Lists planned TLFs and their specifications

2. Mock TLF Creation

• Biostatistician drafts templates with placeholders
• Reviewed by medical writers and clinical leads

3. Programming Specification

• Statistical programmers write specifications for each TLF
• Includes dataset inputs, derivation rules, sorting, and formats

4. TLF Generation and QC

• Programs executed in validated software (e.g., SAS, R)
• Outputs quality checked by independent reviewer

5. TLF Integration into CSR

• Tables/figures included in appendices per ICH E3
• Listings often kept in submission packages or portals

All steps should be traceable and aligned with validation protocols for data integrity.

Common Types of TLFs in Clinical Trials

Demographic and Baseline Tables

• Age, sex, race, weight, baseline disease status
• Grouped by treatment arm

Efficacy Tables and Figures

• Mean change from baseline, response rates, hazard ratios
• Figures: Forest plots, Kaplan-Meier survival curves

Safety Listings and Tables

• Adverse Events (AEs) by severity and relationship
• Laboratory data shifts, ECG outliers

Protocol Deviations and Exposure Summaries

• Exposure time, dosing adherence, discontinuations
• Protocol deviation frequency and classification

Consistency in format ensures readability and regulatory acceptance, particularly during stability studies reporting and audits.

Mock TLFs: What to Include in the SAP

Mock tables should be part of the SAP appendices and include:

• Table/Listing/Figure Number and Title
• Column and row headers with footnotes
• Units of measure, statistical methods, sorting logic
• Denominator definitions (e.g., N=number of subjects per arm)

Mock TLFs act as a contract between biostatistics and programming and guide TLF production.

Programming Best Practices for TLFs

1. Use validated code in SAS, R, or other regulated software
2. Follow CDISC standards (ADaM datasets preferred)
3. Ensure consistent formatting across tables (decimal places, footnotes)
4. Perform independent QC by a different programmer or statistician
5. Document all assumptions and derivations in specs

TLFs and Regulatory Submissions

TLFs are included in:

• Clinical Study Reports (ICH E3 Appendix 16.2 and 16.4)
• eCTD Module 5
• Submission data packages to CDSCO, EMA, and FDA

Ensure table and listing filenames match SAP and CSR cross-references exactly. Regulatory agencies may request the complete TLF package during inspections or reviews.

Common Pitfalls and How to Avoid Them

• Mismatch between SAP and generated TLFs: Always use approved mock TLFs
• Inconsistent formats: Use standard templates across studies
• Lack of QC documentation: Retain audit trails and QC logs
• Missing legends or units: Footnotes should clarify assumptions and calculations
• Overloaded figures: Simplify for clarity and interpretability

Best Practices Summary

• Predefine all TLFs in the SAP
• Use standardized formats and file naming
• Perform thorough QC with independent verification
• Archive TLF specs and outputs in document control systems
• Train programmers on GMP SOPs for TLF production

Conclusion: TLFs Are the Storytelling Engine of Clinical Data

TLFs bridge raw data and regulatory narratives. Done right, they ensure that results are accurate, interpretable, and ready for submission. By investing in structured templates, strong collaboration, and rigorous quality control, sponsors can deliver clear and compliant data summaries that stand up to regulatory scrutiny and scientific inquiry.

Further Resources

• GMP training
• Pharma Regulatory