Track 10: Statistics, Evidence Generation Real-World Data
This track explores the methodological and statistical approaches that design, analyze, and interpret evidence for regulatory, health technology assessment (HTA), payer, and clinical decision-making. It emphasizes innovative statistical methods, real-world data (RWD), causal inference, external controls, evidence synthesis, Bayesian methods, model-informed drug development, and benefit-risk assessment to strengthen the credibility of evidence across the product lifecycle. While Track 3 focuses on the generation, curation, integration, and operational enablement of data, Track 10 focuses on the analytical frameworks, statistical methodologies, and evidence strategies that transform data into decision-grade evidence. Sessions highlight statistical innovation, AI-enabled evidence generation, patient-centered evidence, trusted data and methods, and cross-disciplinary collaboration to support regulatory submissions, HTA evaluations, and healthcare decision-making.
Themes
- Causal Inference, External Controls, and Synthetic Comparators: Target-trial emulation; historical trial and RWD controls; hybrid designs; transportability, unmeasured confounding, quantitative bias and sensitivity analyses
- Evidence Synthesis and Indirect Treatment Comparisons: Network and individual-participant-data meta-analysis; MAIC, STC and cross-design synthesis; heterogeneity, effect modification and regulatory/HTA interpretation
- Bayesian Methods and Quantitative Decision-Making: Evidence borrowing, adaptive learning, dose selection, subgroup and safety assessment, benefit–risk and development decisions; prior conflict and operating characteristics
- Innovative Trial Designs and Master Protocols: Adaptive, platform, basket, umbrella, pragmatic and externally augmented trials; nonconcurrent controls, multiplicity, simulation and operational bias
- Estimands, Missing Data, and Complex Clinical Questions: Treatment discontinuation, rescue medication, switching, competing events and missing-not-at-random assumptions; alignment of objectives, data collection and analysis
- Synthetic Data, Virtual Populations, and Model-Informed Drug Development: Synthetic patients from historical trials and RWD; QSP, PBPK and disease models integrating multi-omics and biological knowledge; validation and uncertainty quantification
- AI-Enabled Evidence and the Transformation of Biopharma and Regulation: AI applications in data, endpoints, analytics and evidence synthesis; impact on operating models, statistical roles, regulatory review, governance and policy
- Putting Global RWD and RWE Guidance into Practice: Operationalizing ICH M14, emerging E23, E6(R3) and regional expectations; data fitness, prespecification, transparency, reproducibility and regulator engagement
- Decision-Grade RWE Across the Product Lifecycle: RWE for approvals, labeling, post-approval commitments, safety, HTA and payer decisions; successful, unsuccessful and inconclusive case studies
- Patient-Centered Evidence Across the Product Lifecycle: Patient partnership, PROs, preferences, digital measures and patient-generated data; meaningful effects, missingness, representativeness and decision-maker acceptance
- Trusted RWD, Methods, and Endpoints Across Studies and Submissions: Data quality, provenance, linkage, phenotype and endpoint validation, analytic methods and software; efficient reuse with reassessment for each context of use
- Scalable Evidence Ecosystems and Public–Private Collaboration: Federated networks, common data models, interoperability and privacy-enhancing technologies; sustainable governance, cross-border research and reproducible evidence generation
Key Questions to be Addressed
- How can advanced statistical methods, innovative trial designs, and external or synthetic comparators generate reliable evidence while addressing bias, uncertainty, multiplicity, and operational complexity?
- How can organizations translate global RWD and RWE guidance into decision-grade evidence that supports regulatory, safety, HTA, payer, and lifecycle decisions?
- How are AI, synthetic data, virtual populations, and model-informed approaches transforming evidence generation, statistical practice, regulatory review, and governance?
- What data, methods, infrastructure, and partnerships are needed to produce trusted, patient-centered, reproducible evidence across studies, submissions, and global healthcare systems?
-
Real-World Evidence – The Evolving Landscape for Regulators, Data, and Integrated Use
Learn More! -
Medical Devices and Drug-Device Combination Products Workshop: Post-Market Surveillance and Clinical Evidence
Learn More! -
A Guide to ICH Guidelines in Clinical Trials On-Demand
Learn More! -
Benefit-Risk Assessment and Management Across the Lifecycle
Learn More! -
Drug Development and Lifecycle Management eLearning Program
Learn More! -
Patient Engagement Evaluation and Metrics
Learn More!