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

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 repeate

SUBMITTER: Su L 

PROVIDER: S-EPMC4420715 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature