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Apply causal inference methods to real-world evidence and clinical research

This course introduces causal inference principles and methods to support decision-making using real-world evidence (RWE) and clinical trial data. It focuses on addressing bias, defining appropriate estimands, and applying causal methods in health technology assessment (HTA) and regulatory contexts.

Technical topics include:

• Causal principles, directed acyclic graphs (DAGs), and target trial emulation
• Common biases, including time-zero and immortal time bias
• Methods for baseline confounding (multivariate regression, propensity scores)
• Methods for time-varying confounding (g-formula, marginal structural models, inverse probability weighting)
• Estimand selection for observational studies and trials with treatment switching

This course includes tools and concepts that can be immediately applied, including:

• Frameworks for designing and analyzing observational studies and clinical trials
• Application of causal methods in HTA case examples (eg, external control arms, treatment switching)
• Practical guidance on selecting appropriate estimands for decision-making
• Insights into HTA agency perspectives, including acceptance and barriers

Participants will gain the knowledge and skills to apply causal inference methods and estimands to generate robust, decision-relevant evidence across healthcare settings.

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

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*Conference attendance is not required to attend an ISPOR Short Course. Separate registration is required for conference attendees.

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