title: 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 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, but valid likelihood-based inference requires correct specification of the covariance structure. In both cases, flexible modeling approaches for the covariance structure are essential. In this paper, we develop a flexible approach to modeling the covariance structure for irregular continuous longitudinal data using the partial autocorrelation function and the variance function. In particular, we propose semiparametric non-stationary partial autocorrelation function models, which do not suffer from complex positive definiteness restrictions like the autocorrelation function. We describe a Bayesian approach, discuss computational issues, and apply the proposed methods to CD4 count data from a pediatric AIDS clinical trial.
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.
authors.forename: Li
authors.initials: L
authors.lastname: Su
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Daniels
mesh_terms: D000704:Analysis of Variance; D019380:Anti-HIV Agents / Q000008:administration & dosage; D001499:Bayes Theorem; D015982:Bias; D018791:CD4 Lymphocyte Count; D002648:Child; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D015658:HIV Infections / Q000188:drug therapy / Q000276:immunology; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012107:Research Design; D015215:Zidovudine / Q000008:administration & dosage
chemical_list: D019380:Anti-HIV Agents; D015215:Zidovudine
keywords: missing data; non-stationary covariance function; outcome-dependent follow-up; penalized splines; semiparametric covariance function