Replicability, Reproducibility, and Agent-based Simulation of Interventions.

Secondary use of medical data and use of observational data for causal inference has been growing. Yet these data bring many challenges such as confounding due to unobserved variables and variation in medical processes across settings. Further, while methods exist to handle some of these problems, researchers lack ground truth to evaluate these methods. When a finding is not replicated across multiple sites, it is unknown whether this is a failure of an algorithm, a genuine difference between populations, or an artifact of structural differences between the sites. We show how agent-based simulation of medical interventions can be used to explore how bias, error, and variation across settings affect inference. Our approach enables users to model not only interventions and outcomes, but also the complex interaction between patients with different risks of mortality and providers with different observed and latent treatment effects. Ultimately we propose that such simulations can be used to better evaluate the behavior of new methods with known ground truth and better calculate sample size for EHR-based studies.