2021 04 21 Marginal Structural Models

Health administrative data is longitudinal with measures captured on individuals over time. Conventional regression-based methods applied to longitudinal data do not explicitly account for time-varying confounders and can produce biased estimates for causal effects. Marginal structural models are an estimation process used in longitudinal data for causal inference analysis and the control of time-varying confounding. These approaches require careful conceptual consideration of assumptions. This webinar will focus on introducing marginal structural models and specifically their utility in the context of population-wide health administrative data.