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Imputing missing values with external data: Applications for multisite settings and federated analyses
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DOI：10.1177/1536867x251398605.png)
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Abstract
En 中文
<jats:p>
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 imputation, particularly in multisite settings and federated analyses. We introduce a new command,
<jats:monospace>mi impute from</jats:monospace>
, 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 values without sharing individual data between studies. We describe the underlying method and present the syntax of
<jats:monospace>mi impute from</jats:monospace>
alongside practical examples of missing data in collaborative research projects.
</jats:p>
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