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Compensating for Missing Data from Longitudinal Studies Using WinBUGS

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  • Carrigan, Gretchen
  • Barnett, Adrian G.
  • Dobson, Annette J.
  • Mishra, Gita

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.

Suggested Citation

  • Carrigan, Gretchen & Barnett, Adrian G. & Dobson, Annette J. & Mishra, Gita, 2007. "Compensating for Missing Data from Longitudinal Studies Using WinBUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i07).
    • Handle: RePEc:jss:jstsof:v:019:i07
    DOI: http://hdl.handle.net/10.18637/jss.v019.i07
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    References listed on IDEAS

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    1. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz & Amy H. Herring, 2005. "Missing-Data Methods for Generalized Linear Models: A Comparative Review," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 332-346, March.
    2. Patrick Royston, 2005. "Multiple imputation of missing values: update," Stata Journal, StataCorp LP, vol. 5(2), pages 188-201, June.
    3. Patrick Royston, 2005. "Multiple imputation of missing values: Update of ice," Stata Journal, StataCorp LP, vol. 5(4), pages 527-536, December.
    4. A. N. Pettitt & T. T. Tran & M. A. Haynes & J. L. Hay, 2006. "A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 97-114, January.
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    2. Cai, Zhen & Aguilar, Francisco X., 2013. "Meta-analysis of consumer's willingness-to-pay premiums for certified wood products," Journal of Forest Economics, Elsevier, vol. 19(1), pages 15-31.
    3. Célina Roda & Ioannis Nicolis & Isabelle Momas & Chantal Guihenneuc, 2014. "New Insights into Handling Missing Values in Environmental Epidemiological Studies," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-8, September.
    4. Oya Kalaycioglu & Andrew Copas & Michael King & Rumana Z. Omar, 2016. "A comparison of multiple-imputation methods for handling missing data in repeated measurements observational studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 683-706, June.

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