Rapid model exploration for complex hierarchical data: Application to pharmacokinetics of insulin aspart

Goudie, Robert J. B., Hovorka, Roman, Murphy, Helen R. and Lunn, David (2015) Rapid model exploration for complex hierarchical data: Application to pharmacokinetics of insulin aspart. Statistics in Medicine, 34 (23). pp. 3144-3158. ISSN 0277-6715

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Abstract

We consider situations, which are common in medical statistics, where we have a number of sets of response data, from different individuals, say, potentially under different conditions. A parametric model is defined for each set of data, giving rise to a set of random effects. Our goal here is to efficiently explore a range of possible 'population' models for the random effects, to select the most appropriate model. The range of possible models is potentially vast, because the random effects may depend on observed covariates, and there may be multiple credible ways of partitioning their variability. Here, we consider pharmacokinetic (PK) data on insulin aspart, a fast acting insulin analogue used in the treatment of diabetes. PK models are typically nonlinear (in their parameters), often complex and sometimes only available as a set of differential equations, with no closed-form solution. Fitting such a model for just a single individual can be a challenging task. Fitting a joint model for all individuals can be even harder, even without the complication of an overarching model selection objective. We describe a two-stage approach that decouples the population model for the random effects from the PK model applied to the response data but nevertheless fits the full, joint, hierarchical model, accounting fully for uncertainty. This allows us to repeatedly reuse results from a single analysis of the response data to explore various population models for the random effects. This greatly expedites not only model exploration but also cross-validation for the purposes of model criticism. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Item Type:	Article
Additional Information:	© 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords:	bayesian hierarchical models,variable selection,markov chain monte carlo,pharmacokinetics,insulin,sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Groups > Cardiovascular and Metabolic Health Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health
Depositing User:	Pure Connector
Date Deposited:	15 Mar 2016 10:08
Last Modified:	19 Oct 2023 01:39
URI:	https://ueaeprints.uea.ac.uk/id/eprint/57454
DOI:	10.1002/sim.6536

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