Robert Thiesmeier: A new framework for multiple imputation in distributed data networks

Multisite studies are increasingly used to study human health across different populations and countries. However, a common challenge in using data from multiple studies is the presence of systematically missing values – when some studies have not recorded information on certain variables. Although it is possible to use data from sites with recorded observations to impute the missing values, this process becomes challenging when data pooling is not feasible because of logistic or legal constraints. In this talk, I am going to introduce a framework for multiple imputation in distributed data networks allowing for the imputation of missing values across study sites without the need of sharing individual data. Some motivating examples alongside further steps and developments will be discussed.