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Research article summary (published 11 Jul 2008):

Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response.

Full Abstract

Methods specifically targeting missing values in a wide spectrum of statistical analyses are now part of serious statistical thinking due to many advances in computational statistics and increased awareness among sophisticated consumers of statistics. Despite many advances in both theory and applied methods for missing data, missing-data methods in multilevel applications lack equal development. In this paper, I consider a popular inferential tool via multiple imputation in multilevel applications with missing values. I specifically consider missing values occurring arbitrarily at any level of observational units. I use Bayesian arguments for drawing multiple imputations from the underlying (posterior) predictive distribution of missing data. Multivariate extensions of well-known mixed-effects models form the basis for simulating the posterior predictive distribution, hence creating the multiple imputations. The discussion of these topics is demonstrated in an application assessing correlates to unmet need for mental health care among children with special health care needs.

 

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Author information

Author/s: Yucel, Recai M (RM);

Affiliation: Department of Epidemiology and Biostatistics, University at Albany, School of Public Health, One University Place, Room 139, Rensselaer, NY 12144, USA. ryucel(-atsign-)albany.edu

Journal and publication information

Publication Type: Journal Article

Journal: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences (Philos Transact A Math Phys Eng Sci), published in England. (Language: eng)

Reference: 2008-Jul; vol 366 (issue 1874) : pp 2389-403

Dates: Created 2008/05/22; Completed 2008/08/13;

PMID: 18407897, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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