Inverse Probability of Exposure and Censoring Weights for Marginal Structural Models

Marginal structural models with inverse probability weighted estimators are increasingly used to estimate causal effects of treatment in non-randomized studies using real-world data. This presentation will introduce the basics of inverse probability of treatment weight and censoring weights and will discuss opportunities and challenges of using these approaches for causal effects of medication use in routinely collected healthcare utilization databases.