Patrick Royston () (MRC Clinical Trials Unit, London) John Galati John Carlin
Abstract
There has been a considerable growth of interest among Stata users and more widely in the practical use of multiple imputation as a principled route to the analysis of datasets with missing covariate values. Sophisticated Stata software (ice) is available for creating multiply imputed datasets. However, equally sophisticated and flexible tools are required to carry out the analyses. Carlin et al (2003)’s MI Tools package and Royston’s micombine command (packaged with ice) made a start. We present a new set of tools, called mim, which carries the postimputation process a step further. mim defines a standardized architecture for MI datasets and has features for manipulating MI data. More importantly, it supports a wide range of regression models, including those for panel and survey data. Limited facilities for postestimation analysis are provided, and these are expected to be further developed. The package is in beta-testing form and has been submitted for publication in the Stata Journal.
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