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PubMed · 10521861

Autoregressive models for describing non-linear changes in biological parameters fitted using BUGS.

A P Mander·M D Hughes·S J Sharp·C J Lamm
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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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Anti-Inflammatory AgentsAsthmaBayes TheoremBudesonideComputer SimulationHumansLinear ModelsModels, BiologicalNumerical Analysis, Computer-AssistedPeak Expiratory Flow RateSoftwareTime FactorsTreatment OutcomeClinical TrialControlled Clinical TrialJournal Article

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A P Mander, M D Hughes, S J Sharp, C J Lamm. 1999-10-30. Autoregressive models for describing non-linear changes in biological parameters fitted using BUGS.. https://doi.org/10.1002/(sici)1097-0258(19991030)18%3A20%3C2709%3A%3Aaid-sim236%3E3.0.co%3B2-g

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A randomized comparison of dexamethasone 0.15 mg/kg versus 0.6 mg/kg for the treatment of moderate to severe croup.

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