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Multiple imputation of missing values: update

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Author Info
Patrick Royston () (Cancer Division, MRC Clinical Trials Unit)
Abstract

This article describes a substantial update to mvis, which brings it more closely in line with the feature set of S. van Buuren and C. G. M. Oudshoorn’s implementation of the MICE system in R and S-PLUS (for details, see http://www.multiple-imputation.com). To make a clear distinction from mvis,the principal program of the new Stata release is called ice. I will give details of how to use the new features and a practical illustrative example using real data. All the facilities of mvis are retained by ice. Some improvements to micombine for computing estimates from multiply imputed datasets are also described. Copyright 2005 by StataCorp LP.

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Publisher Info
Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 5 (2005)
Issue (Month): 2 (June)
Pages: 188-201
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:tsj:stataj:v:5:y:2005:i:2:p:188-201

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Related research
Keywords: ice; mvis; uvis; micombine; mijoin; misplit; missing data; missing at random; multiple imputation; multivariate imputation; regression modeling;

Cited by:
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  1. Rodrigo Alfaro & Marcelo Fuenzalida, 2008. "Análisis de Información Faltante en Encuestas Microeconómicas," Economic Statistics Series 67, Central Bank of Chile. [Downloadable!]
  2. Andrea Marshall & Lucinda Billingham & Stirling Bryan, 2009. "Can we afford to ignore missing data in cost-effectiveness analyses?," The European Journal of Health Economics, Springer, vol. 10(1), pages 1-3, February. [Downloadable!] (restricted)
  3. Gita Mishra & Annette J. Dobson & Adrian G. Barnett & Gretchen Carrigan, 2007. "Compensating for Missing Data from Longitudinal Studies Using WinBUGS," Journal of Statistical Software, American Statistical Association, vol. 19(07), 06. [Downloadable!]
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This page was last updated on 2009-12-20.


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