Multiple imputation for systematically missing confounders within a distributed data drug safety network: A simulation study and real-world example.

Multiple imputation adapted to distributed data settings is a feasible method to reduce bias from unmeasured but measurable confounders when at least one database contains the variables of interest. Further research is needed to evaluate its validity in real distributed data networks.