Imputing missing values with external data: Applications for multisite settings and federated analyses

Robert Thiesmeier (), Matteo Bottai () and Nicola Orsini
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Robert Thiesmeier: Karolinska Institutet
Matteo Bottai: Karolinska Institutet

Stata Journal, 2025, vol. 25, issue 4, 812-835

Abstract: Missing data are a common challenge across scientific disciplines. Current imputation methods require the availability of individual data to impute missing values. However, missingness often requires using external data for the im- putation, particularly in multisite settings and federated analyses. We introduce a new command, mi impute from, designed to impute missing values using linear predictors and their related covariance matrix from imputation models fit in one or multiple external studies. This allows for the imputation of any missing val- ues without sharing individual data between studies. We describe the underlying method and present the syntax of mi impute from alongside practical examples of missing data in collaborative research projects.

Keywords: mi impute from; mi_impute_from_get; univariate imputation; missing data; systematically missing data; meta-analysis; quantile regression; logistic regression; multiple imputation; pooling projects; data network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1177/1536867X251398605

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