Section Descriptions

Section Descriptions

When you submit your paper, you will need to specify the submission section where you think your paper best fits. Here are the submission sections that have been defined for PharmaSUG 2026:

Section Title	Description	Sample Topics
Advanced Programming	Addresses methodologies, tools, and automation strategies to support efficacy and safety analysis. Topics include (Tables, Listings, and Figures) TLF automation, signal detection, standardization of safety outputs, and graphical safety reviews.	Automating TLF generation across multiple studies Emerging safety signal detection methods Standardizing graphical safety reviews
Advanced Statistical Methods	Highlights innovative biostatistical techniques and their practical applications in regulated clinical trials. Topics may include Bayesian approaches, adaptive trial designs, multiplicity adjustments, complex endpoints, diagnostic methods, and integration with real-world evidence.	Validation strategies for adaptive trial designs Bayesian modeling in rare disease studies Multiplicity adjustment methods for complex endpoints
AI in Pharma, Biotech and Clinical Data Science	Dedicated to exploring the application of AI (Machine Learning, Deep Learning, Generative AI) , in drug development, data synthesis, documentation automation, hypothesis generation, and clinical trial design. Includes discussion on model validation, bias mitigation, ethical and regulatory considerations.	Automating clinical study report drafting Ensuring data privacy and bias mitigation Case studies on AI-driven hypothesis generation
Career Development, Leadership & Soft Skills	Emphasizes leadership, mentoring, project management, communication, and soft skills needed in modern biotech and pharma environments. Includes DEI topics, remote collaboration, and transitioning into the field.	Managing global programming teams remotely Mentoring early-career clinical programmers Strategies for transitioning from academia to industry Independent Consultant talks
Data Standards Implementation (CDISC, SEND, ADaM, SDTM)	Focuses on implementation, customization, and optimization of data standards for clinical trials. Topics may include controlled terminology, SDTM mapping, ADaM traceability, value-level metadata, and interoperability with real-world data (RWD).	Validation of SDTM to ADaM traceability Managing custom domains in SDTM datasets Enhancing metadata consistency for regulatory submissions
Data Visualization & Interactive Analytics	Covers best practices, tools, and strategies for creating clear, insightful data visualizations, interactive tools, and dynamic dashboards for decision-making and regulatory review.	Designing integrated dashboards for study monitoring Visualization techniques for time-to-event data Interactive analytics using R Shiny and Plotly
Emerging Technologies (R, Python, Git/GitHub etc.)	Explores the adoption and integration of open-source tools in clinical programming, data transformation, reporting, analytics, and reproducible delivery pipelines. Topics include libraries, interoperability strategies, best practices, challenges, risks, benefits, and emerging community trends.	Evaluating Open-Source Adoption in Regulated Environments Creating Reproducible Clinical Outputs with Open-Source Tools Leveraging Open-Source Libraries for Clinical Data Transformation Building Scalable Analytical Environments with Open-Source Tools Governance Models for Managing Open-Source Workflows in Pharma Designing Workflow Standards that Include Python, R, and SAS Interoperability Best Practices for Developing Hybrid SAS–Python Programming Pipelines Open-Source Toolchains for Streamlined Statistical Analysis
ePosters	Provides a flexible format for sharing concise, visually rich presentations from any of the sections. Ideal for innovative projects, pilot studies, lessons learned, or emerging ideas that benefit from visual storytelling and brief discussion.	Innovative workflows improving data standardization Visualization techniques for cross-team communication Lessons learned from automation projects
Hands-On Training	Interactive training on a variety of topics that provides attendees with practical “hands-on” experience using industry software tools in a classroom setting taught by seasoned experts.	Speakers are by invitation only.
PK/PD/ADA and Quantitative Pharmacology	Promotes programming practices related to pharmacokinetic (PK), pharmacodynamic (PD), anti-drug antibody (ADA) data, and analysis. Includes non-compartmental analysis (NCA), pharmacometrics, QSP (Quantitative Systems Pharmacology), and model-informed drug development. Addresses tools (SAS, R, Python), automation, and integration with CDISC.	Standardizing ADA assay data for submissions Streamlining NCA reporting workflows using R and Python Automation in pharmacometrics model validation
Real-World Data (RWD) and Real-World Evidence (RWE)	Discusses the programming, statistical, and data challenges of working with RWD sources like EHRs, claims, and registries to derive RWE in support of clinical, regulatory, or commercial decision-making. Includes data transformation, linkage, bias mitigation, and standards harmonization.	Transforming EHR data for study use Validating RWE endpoints for submission Addressing bias in RWD analyses Impact of social determinants on diabetes outcomes using Medicaid data Machine learning model to predict hospital re-admissions Comparative study of mental health treatments using claims data
Study Data Integration & Study-Level Analysis	Focuses on challenges and solutions for integrating data across studies and systems. Includes ADaM-IG v1.3/2.0 implications, pooled datasets, cross-study validation, and integrated analyses (ISS, ISE, CSR appendices).	Consistency in pooled datasets for ISS/ISE submissions Integrated Define.xml best practices Cross-study validation methodologies
Submission Standards and Metadata Management for Global Health Authorities	Covers evolving requirements for data and metadata standards in support of regulatory submissions to agencies worldwide (e.g., FDA, EMA, PMDA, MHRA). Includes agency-specific guidance, submission package automation, reviewer guides, eCTD integration, and electronic standards such as CDISC, Define-XML, SEND, and ADaM. Topics may also include inspection readiness, experience and lessons learned.	Automation of study data reviewer’s guides across multiple agencies Comparative analysis of regulatory expectations (e.g., FDA vs MHRA) Advances in eCTD integration workflows Inspection readiness Research studies
Tools, Tech & Innovation	Share innovations, platforms and tools that improve data processing, analysis workflows, and regulatory readiness. Topics may include Cloud-Based Clinical Data Platforms and Statistical Computing Environments (AWS, Snowflake, Databricks, Veeva, Domino); Workflow Automation (Airflow, GitHub Actions); DevSecOps for Clinical Analytics; Containerization & CI/CD Pipelines; AI/ML in Clinical Analytics (including validation concerns); and Clinical Trial Randomization.	Building a Scalable Statistical Computing Environment on AWS or Snowflakes GitHub Actions for Automated Quality Control in Clinical Programming Balancing Innovation and Compliance: Lessons from AI Use in Submissions Ensuring Data Integrity, Reproducibility, Audit Trails and Version Control in Clinical Systems Regulatory Considerations for Randomization Algorithms in Global Trials