Regression models for covariance structures in longitudinal studies

Pan, Jianxin and MacKenzie, Gilbert (2006) Regression models for covariance structures in longitudinal studies. Statistical Modelling, 6 (1). pp. 43-57. ISSN 1471-082x

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Abstract

A convenient reparametrization of the marginal covariance matrix arising in longitudinal studies is discussed. The new parameters have transparent statistical interpretations, are unconstrained and may be modelled parsimoniously in terms of polynomials of time. We exploit this framework to model the dependence of the covariance structure on baseline covariates, time and their interaction. The rationale is based on the assumption that a homogeneous covariance structure with respect to the covariate space is a testable model choice. Accordingly, we provide methods for testing this assumption by incorporating covariates along with time into the model for the covariance structure. We also present new computational algorithms which can handle unbalanced longitudinal data, thereby extending existing methods. The new model is used to analyse Kenward's (1987) cattle data, and the findings are compared with published analyses of the same data set.

Item Type:	Article
Uncontrolled Keywords:	CHOLESKY DECOMPOSITION; COVARIATE DEPENDENT COVARIANCE MATRIX; JOINT MEAN-COVARIANCE MODELS; LONGITUDINAL TRIALS; UNBALANCED OBSERVATIONS; UNCONSTRAINED PARAMETRIZATION
Subjects:	MSC 2010, the AMS's Mathematics Subject Classification > 62 Statistics
Depositing User:	Ms Lucy van Russelt
Date Deposited:	18 Aug 2006
Last Modified:	20 Oct 2017 14:12
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/563

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