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

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 ef...

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Main Authors:	Goudie, Robert J. B., Hovorka, Roman, Murphy, Helen R., Lunn, David
Format:	Online
Language:	English
Published:	John Wiley and Sons Inc. 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4736693/

id	pubmed-4736693
recordtype	oai_dc
spelling	pubmed-47366932016-02-11 Rapid model exploration for complex hierarchical data: application to pharmacokinetics of insulin aspart Goudie, Robert J. B. Hovorka, Roman Murphy, Helen R. Lunn, David Research Articles 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. John Wiley and Sons Inc. 2015-05-26 2015-10-15 /pmc/articles/PMC4736693/ /pubmed/26013427 http://dx.doi.org/10.1002/sim.6536 Text en © 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 (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Goudie, Robert J. B. Hovorka, Roman Murphy, Helen R. Lunn, David
spellingShingle	Goudie, Robert J. B. Hovorka, Roman Murphy, Helen R. Lunn, David Rapid model exploration for complex hierarchical data: application to pharmacokinetics of insulin aspart
author_facet	Goudie, Robert J. B. Hovorka, Roman Murphy, Helen R. Lunn, David
author_sort	Goudie, Robert J. B.
title	Rapid model exploration for complex hierarchical data: application to pharmacokinetics of insulin aspart
title_short	Rapid model exploration for complex hierarchical data: application to pharmacokinetics of insulin aspart
title_full	Rapid model exploration for complex hierarchical data: application to pharmacokinetics of insulin aspart
title_fullStr	Rapid model exploration for complex hierarchical data: application to pharmacokinetics of insulin aspart
title_full_unstemmed	Rapid model exploration for complex hierarchical data: application to pharmacokinetics of insulin aspart
title_sort	rapid model exploration for complex hierarchical data: application to pharmacokinetics of insulin aspart
description	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.
publisher	John Wiley and Sons Inc.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4736693/
_version_	1613532056675942400

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

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