Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Patrick Royston, Ian R. White
Title: Multiple Imputation by Chained Equations (MICE): Implementation in Stata
Abstract: Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random assumption. We describe ice, an implementation in Stata of the MICE approach to multiple imputation. Real data from an observational study in ovarian cancer are used to illustrate the most important of the many options available with ice. We remark briefly on the new database architecture and procedures for multiple imputation introduced in releases 11 and 12 of Stata.

Page views:: 20833. Submitted: 2009-09-12. Published: 2011-12-12.
Paper: Multiple Imputation by Chained Equations (MICE): Implementation in Stata     Download PDF (Downloads: 33471)
Supplements: Stata source package Download (Downloads: 891; 49KB) Stata replication code Download (Downloads: 831; 3KB)
ovariancancer.dta: Example data in Stata format Download (Downloads: 1009; 50KB) ovariancancer.xls: Example data in Excel format Download (Downloads: 810; 96KB)

DOI: 10.18637/jss.v045.i04

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.