Missing data is a common challenge across scientific disciplines. Current imputation methods require the availability of individual data to impute missing values. Often, however, missingness requires using external data for the imputation. In this paper, we introduce a new Stata command, mi impute from, designed to impute missing values using linear predictors and their related covariance matrix from imputation models estimated 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 mi impute from alongside practical examples of missing data in collaborative research projects.

翻译：缺失数据是各科学领域普遍面临的挑战。当前的数据填补方法需要获取个体数据才能对缺失值进行填补。然而，缺失数据问题往往需要借助外部数据进行填补。本文介绍了一种新的Stata命令——mi impute from，该命令旨在利用一个或多个外部研究中估算的填补模型的线性预测因子及其相关协方差矩阵来填补缺失值。这使得研究之间无需共享个体数据即可填补任何缺失值。我们阐述了该方法的基本原理，介绍了mi impute from的语法，并结合合作研究项目中缺失数据的实际案例进行了说明。