White paper: Designing real-world evidence studies for causal inference
A comprehensive guide to generating stronger and more powerful evidence
Causal studies seek to use subject matter expertise, carefully curated data, and statistical methods to identify and control for confounding influences and other potential biases.
Traditionally, regulatory approvals and health plan decisions to cover new medical interventions have relied on findings from randomized controlled trials (RCTs). However, with careful design and the use of appropriate statistical methodologies, studies that use observational data and examine interventions delivered through existing healthcare systems can also yield causal estimates.
Because causal studies conducted in real-world settings can provide actionable evidence about effectiveness, safety, and access, so-called “real-world evidence (RWE) studies” have gained broader acceptance with the US Food and Drug Administration, other regulatory bodies, and coverage decision makers throughout the world.
In this white paper, experts from Carelon Research:
- Seek to increase understanding of the factors that inform the design and conduct of causal studies in real-world settings.
- Describe in greater detail how the design of RCTs promotes causal inference and how “target trial” thinking can support the use of RWE studies to estimate causal effects.
- Describe specific considerations for the design of rigorous causal RWE studies.
- Feature selected Carelon Research case studies that solve common RWE design challenges.