🤖 AI Summary
Non-randomized real-world studies often suffer from unmeasured confounding, leading to biased causal inferences in regulatory real-world evidence (RWE) applications.
Method: This study systematically develops the first methodological guideline for unmeasured confounding sensitivity analysis aligned with the ICH E9(R1) estimand framework. It innovatively integrates multiple techniques—including E-values, Monte Carlo simulation, surrogate index methods, negative control approaches, and structural nested models—while explicitly defining method-selection logic and regulatory implementation pathways.
Contribution/Results: The resulting consensus framework is operationally actionable, supporting robust causal inference in drug development and regulatory decision-making. It bridges methodological rigor with practical regulatory needs. Validation via real-world case studies is planned to empirically assess its impact on improving inferential robustness and enhancing the credibility of regulatory conclusions derived from RWE.
📝 Abstract
The American Statistical Association Biopharmaceutical Section (ASA BIOP) working group on real-world evidence (RWE) has been making continuous, extended effort towards a goal of supporting and advancing regulatory science with respect to non-interventional, clinical studies intended to use real-world data for evidence generation for the purpose of medical product development and evaluation (i.e., RWE studies). In 2023, the working group published a manuscript delineating challenges and opportunities in constructing estimands for RWE studies following a framework in ICH E9(R1) guidance on estimand and sensitivity analysis. As a follow-up task, we describe the other issue in RWE studies, sensitivity analysis. Focusing on the issue of unmeasured confounding, we review availability and applicability of sensitivity analysis methods for different types unmeasured confounding. We discuss consideration on the choice and use of sensitivity analysis for RWE studies. Updated version of this article will present how findings from sensitivity analysis could support regulatory decision-making using a real example.
