Speakers
Neisha Opper, MPH, PhD, Landmark Science, La Crescenta, CA, United States; Shivani Aggarwal, MS, PhD, Landmark Science, Inc, Los Angeles, CA, United States; Hoa Le, PhD, MD, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Jennifer B. Christian, PharmD, MPH, PhD, FISPE, Durham, NC, United States
Separate registration required.
Target trial emulation (TTE) has become a cornerstone of real-world evidence (RWE) generation, particularly for external control arms and hybrid trial designs. However, translating the conceptual framework into credible, decision-ready evidence requires careful alignment of key design elements—including eligibility criteria, index date selection, follow-up, and confounding control. Small deviations in these choices can introduce substantial bias, including immortal time bias, prevalent user bias/ left truncation, informative censoring, and residual confounding driven by disease trajectory.
This course provides a comprehensive, applied framework for designing fit-for-purpose TTE studies, with a focus on time-related design challenges. Drawing on recent methodological advances and real-world applications, we integrate three core components: (1) foundational TTE principles and sources of bias, (2) a structured decision framework for index date selection, and (3) practical strategies for addressing time-related biases, including time-varying confounding.
Participants will engage with a series of case-based exercises that simulate real-world study design decisions. Through live polling and interactive dashboards, attendees will evaluate tradeoffs across alternative design strategies—such as line-of-therapy selection, comparator definition, and confounding adjustment—and observe how these decisions influence study outputs in real time (e.g., survival curves, hazard ratios, covariate balance, and weighting diagnostics).
By integrating conceptual guidance with hands-on application, this course equips researchers, regulators, and decision-makers with practical tools to operationalize TTE principles and generate credible, transparent, and defensible RWE across therapeutic areas.
PREREQUISITES: Basic familiarity with observational research and real-world data. Prior exposure to causal inference concepts is helpful but not required.
Topic
Epidemiology & Public Health, Methodological & Statistical Research