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Autoregressive models for describing non-linear changes in biological parameters fitted using BUGS
A P Mander1, M D Hughes, S J Sharp
1MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, CB2 2SR, U.K.
Abstract:
Many biological processes give outcome data which show a curvilinear association with time which tends to an asymptote. We show how autoregressive models can be used to describe this association within individual subjects. We also present a Bayesian approach implemented using statistical software, BUGS, to fit these models in a multi-level (hierarchical) setting that describes variation in the association between subjects. Peak expiratory flow data from a clinical trial involving subjects with asthma are used to illustrate the methods.
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