This track focuses on the methodological and statistical approaches used to design, analyze, and interpret data for regulatory, health technology assessment (HTA), and clinical decision-making. It emphasizes innovative statistical methods, real-world data (RWD) study designs, causal inference, and benefit–risk quantification to strengthen the credibility of evidence across the drug development lifecycle. While Track 3 focuses on the generation, curation, integration, and operational enablement of data, Track 10 addresses the frameworks and analytical strategies that transform those data into actionable evidence. Sessions will highlight statistical innovation, effective communication of results, and cross-disciplinary collaboration to support regulatory submissions and HTA evaluations.
Themes:
- Innovative Statistical Methods: Adaptive and Bayesian designs, estimands, and model-informed approaches to optimize trials
- Real-World Evidence (RWE) Methodologies: Study design and analytic frameworks for leveraging RWD, including external comparators and digital twins
- Quantitative Benefit-Risk: Methodologies for assessing benefit-risk across pre- and post-market settings for informed decision-making
- HTA and Comparative Effectiveness: Indirect comparisons, network meta-analysis, and real-world impact on payer decisions
- Effective Communication of Statistical Evidence: Strategies for conveying complex statistical results to regulators, clinicians, and patients
- Roles of Biostatistics, Data Science, and AI in Regulatory Decision-Making: Defining how biostatistics, data science, and AI contribute across the drug development lifecycle, from hypothesis generation to confirmatory analyses, and their impact on regulatory submissions, HTA evaluations, and clinical decision-making
- Pharmacoepidemiology and RWE Applications: Methods for studying the use, benefits, and risks of medical products in real-world settings, including observational study designs, causal inference techniques, safety signal detection, and their role in complementing clinical trial evidence for regulatory and post-marketing decision-making
- In Silico Modeling and Biostatistical Innovation for Personalized Medicine: Advanced biostatistical and in-silico modeling approaches for de-risking early pipelines, identifying predicted outcomes based on unmet patient needs, and supporting personalized medicine development. This includes deconstructing composite endpoints and simulating trial outcomes to inform clinical development, regulatory strategy, and HTA evaluations.
Key Questions to be Addressed:
- How can innovative statistical methods (Bayesian, adaptive, platform designs) enhance trial efficiency, support regulatory confidence, and enable seamless transitions from accelerated to full approval?
- What are the current opportunities and challenges in applying RWE methodologies, such as external controls and digital twins, to generate credible evidence across the development lifecycle?
- How can quantitative benefit-risk methodologies, indirect comparisons, and network meta-analyses strengthen evidence generation for both regulatory and HTA decision-making globally?
- What strategies, frameworks, and communication approaches can help statisticians and data scientists effectively convey complex analyses and their implications to regulators, clinicians, payers, and patients?
- How can advanced modeling techniques and biostatistical innovations, including in-silico simulations and decomposition of composite endpoints, drive personalized medicine and de-risk early-stage development?
