Event

Enhance your ability to generate real-world evidence (RWE) for regulatory, payer, and HTA decisions through causal inference methods and target trial emulation.

This course explores the use of causal principles and modeling to improve the design and analysis of observational studies and clinical trials, especially in scenarios where traditional approaches, such as the intention-to-treat (ITT) analysis, fall short—such as treatment switching. Participants will learn how to define appropriate estimands, mitigate common biases, and apply cutting-edge methods to support transparent, decision-relevant evidence generation.

Technical Topics Include:

• Causal principles & causal diagrams – Learn how to use directed acyclic graphs (DAGs) to identify and minimize bias (e.g., time-zero bias, immortal time bias)

• Target trial emulation – Design observational studies that mimic the structure of randomized trials

• Methods to adjust for baseline confounding – Including multivariate regression and propensity score techniques

• Methods for time-varying confounding:

  • G-formula

  • Marginal structural models (MSMs) with inverse probability of treatment weighting (IPTW)

  • Rank-preserving structural failure-time models using g-estimation

• Estimands for decision-making – Define and apply appropriate estimands to address clinical or policy questions in settings impacted by treatment switching

• Applied case studies in HTA:

  • Use of external control arms in single-arm trials

  • Examples of trials influenced by treatment switching

• HTA agency insights – Examine global perspectives on acceptance and barriers to implementing causal inference methods, with a focus on Asia and beyond

This Course Offers Practical Tools You Can Use Immediately:

• Step-by-step guidance for selecting and implementing causal inference methods

• Best practices for aligning estimands with decision problems

• Tools for applying causal approaches in both RWE and trial data

• Recommendations for incorporating causal modeling into HTA frameworks

The course features lectures, real-world case examples, and interactive discussions. It is designed for researchers, statisticians, epidemiologists, health economists, outcome researchers, and health policy decision-makers working across all domains of health and healthcare.

PREREQUISITE: Students are expected to have a basic knowledge in epidemiologic studies and methods (including the concept of confounding).

LEVEL: Experienced

TRACK: Real World Data & Information Systems

This short course is offered in-person at the ISPOR Real-World Evidence Summit 2025 . Separate registration is required.  Visit the ISPOR Real-World Evidence Summit 2025 Program page to register and learn more.

FACULTY MEMBERS

ISPOR short courses are designed to enhance knowledge and techniques in core health economics and outcomes research (HEOR) topics as well as emerging trends in the field. Short courses offer 4 or 8 hours of premium scientific education and an electronic course book. Active attendee participation combined with our expert faculty creates an immersive and impactful learning experience. Short courses are not recorded and are only available during the live course presentation.