Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Gretchen Carrigan, Adrian G. Barnett, Annette J. Dobson, Gita Mishra
Title: Compensating for Missing Data from Longitudinal Studies Using WinBUGS
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.

Page views:: 12476. Submitted: 2006-11-30. Published: 2007-06-07.
Paper: Compensating for Missing Data from Longitudinal Studies Using WinBUGS     Download PDF (Downloads: 12975)
Supplements:
coda2sas.sas: macro CODA2SAS Download (Downloads: 1448; 5KB)
example.sas: Practical application of the longitudinal imputation in WinBUGS from SAS Download (Downloads: 1440; 2KB)
longimp.odc: main WinBUGS code Download (Downloads: 1455; 4KB)
longimp: WinBUGS code to model missing longitudinal data Download (Downloads: 1554; 3KB)
longimp.sas: Macro to run longitudinal imputation in WinBUGS from SAS Download (Downloads: 1572; 17KB)
paldata.csv: Excel data Download (Downloads: 1540; 16KB)

DOI: 10.18637/jss.v019.i07

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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.