Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function

Statistics in Medicine
Li Su, Michael J Daniels

Abstract

In long-term follow-up studies, irregular longitudinal data are observed when individuals are assessed repeatedly over time but at uncommon and irregularly spaced time points. Modeling the covariance structure for this type of data is challenging, as it requires specification of a covariance function that is positive definite. Moreover, in certain settings, careful modeling of the covariance structure for irregular longitudinal data can be crucial in order to ensure no bias arises in the mean structure. Two common settings where this occurs are studies with 'outcome-dependent follow-up' and studies with 'ignorable missing data'. 'Outcome-dependent follow-up' occurs when individuals with a history of poor health outcomes had more follow-up measurements, and the intervals between the repeated measurements were shorter. When the follow-up time process only depends on previous outcomes, likelihood-based methods can still provide consistent estimates of the regression parameters, given that both the mean and covariance structures of the irregular longitudinal data are correctly specified and no model for the follow-up time process is required. For 'ignorable missing data', the missing data mechanism does not need to be specified, bu...Continue Reading

References

Feb 9, 2002·BMJ : British Medical Journal·Ruth Little
Sep 17, 2002·Biometrics·Stuart R LipsitzSteven Lipshultz
Oct 23, 2002·Archives of Disease in Childhood·L Diggle, J Deeks
Apr 30, 2003·Statistics in Medicine·Michael J Daniels, Yan D Zhao
Jan 24, 2006·Biostatistics·Garrett M FitzmauriceSteven Lipshultz
Jun 1, 2007·Journal of the American Statistical Association·Jianqing FanRunze Li
Feb 18, 2010·Journal of Multivariate Analysis·M J Daniels, M Pourahmadi

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Citations

Aug 2, 2017·Biometrics·Yuping Zhang, Zhengqing Ouyang

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Software Mentioned

Linux
R
MATLAB
QtOctave

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