Multiple imputation of missing values
Patrick Royston
Cancer Division
MRC Clinical Trials Unit
222 Euston Road
London NW1 2DA
UK
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Abstract. Following the seminal publications of Rubin about thirty years ago,
statisticians have become increasingly aware of the inadequacy of
“complete-case” analysis of datasets with missing observations. In
medicine, for example, observations may be missing in a sporadic way for
different covariates, and a complete-case analysis may omit as many as half
of the available cases. Hotdeck imputation was implemented in Stata in 1999
by Mander and Clayton. However, this technique may perform poorly when many
rows of data have at least one missing value. This article describes an
implementation for Stata of the MICE method of multiple multivariate
imputation described by van Buuren, Boshuizen, and Knook (1999). MICE
stands for multivariate imputation by chained equations. The basic idea of
data analysis with multiple imputation is to create a small number (e.g.,
5–10) of copies of the data, each of which has the missing values suitably
imputed, and analyze each complete dataset independently. Estimates of
parameters of interest are averaged across the copies to give a single
estimate. Standard errors are computed according to the “Rubin rules”,
devised to allow for the between- and within-imputation components of
variation in the parameter estimates. This article describes five ado-files.
mvis creates multiple multivariate imputations. uvis imputes missing values
for a single variable as a function of several covariates, each with
complete data. micombine fits a wide variety of regression models to a
multiply imputed dataset, combining the estimates using Rubin’s rules, and
supports survival analysis models (stcox and streg), categorical data
models, generalized linear models, and more. Finally, misplit and mijoin are
utilities to interconvert datasets created by mvis and by the miset program
from John Carlin and colleagues. The use of the routines is illustrated with
an example of prognostic modeling in breast cancer.
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Patrick Royston
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