Imputation when data cannot be pooled

Distributed data networks are increasingly used to study human health across different populations and countries. Analyses are commonly performed at each study site to avoid the transfer of individual data between study sites due to legal and logistical barriers. Despite many benefits, however, a frequent challenge in such networks is the absence of key variables of interest at one or more study sites. Current imputation methods require the availability of individual data from the involved studies to impute missing data. This creates a need for methods that can impute data in one study using only information that can be easily and freely shared within a data network. To address this need, we introduce a new Stata command, mi impute from, designed to impute missing variables in a single study using a linear predictor and the related variance/covariance matrix from an imputation model fit from one or multiple external studies. In this presentation, the syntax of mi impute from will be presented along with motivating examples from health-related research.