Compensating for missing data from longitudinal studies using WinBUGS

Carrigan, Gretchen, Barnett, Adrian G., Dobson, Annette J. and Mishra, Gita (2007) Compensating for missing data from longitudinal studies using WinBUGS. Journal of Statistical Software, 19 7: 1-17.

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Author Carrigan, Gretchen
Barnett, Adrian G.
Dobson, Annette J.
Mishra, Gita
Title Compensating for missing data from longitudinal studies using WinBUGS
Journal name Journal of Statistical Software   Check publisher's open access policy
ISSN 1548-7660
Publication date 2007-06
Sub-type Article (original research)
Open Access Status File (Publisher version)
Volume 19
Issue 7
Start page 1
End page 17
Total pages 17
Editor J. de Leeuw
Place of publication Alexandria, VA, United States
Publisher American Statistical Association
Collection year 2008
Language eng
Subject C1
321203 Health Information Systems (incl. Surveillance)
730299 Public health not elsewhere classified
321200 Public Health and Health Services
Abstract Missing data is a common problem in survey based research. There are many packages that compensate for missing data but few can easily compensate for missing longitudinal data. WinBUGS compensates for missing data using multiple imputation, and is able to incorporate longitudinal structure using random effects. We demonstrate the superiority of longitudinal imputation over cross-sectional imputation using WinBUGS. We use example data from the Australian Longitudinal Study on Women's Health. We give a SAS macro that uses WinBUGS to analyze longitudinal models with missing covariate date, and demonstrate its use in a longitudinal study of terminal cancer patients and their carers.
Keyword Missing data
Multiple imputation
Longitudinal data
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Fri, 28 Mar 2008, 16:39:36 EST