Nonlinear hierarchical modeling of experimental infection data

Michael D Singleton; Patrick J Breheny

doi:10.1016/j.prevetmed.2016.06.006

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Nonlinear hierarchical modeling of experimental infection data

Journal article

Peer reviewed

Nonlinear hierarchical modeling of experimental infection data

Michael D Singleton and Patrick J Breheny

Preventive veterinary medicine, Vol.130, pp.129-136

08/01/2016

DOI: 10.1016/j.prevetmed.2016.06.006

PMID: 27435656

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Abstract

In this paper, we propose a nonlinear hierarchical model (NLHM) for analyzing longitudinal experimental infection (EI) data. The NLHM offers several improvements over commonly used alternatives such as repeated measures analysis of variance (RM-ANOVA) and the linear mixed model (LMM). It enables comparison of relevant biological properties of the course of infection including peak intensity, duration and time to peak, rather than simply comparing mean responses at each observation time. We illustrate the practical benefits of this model and the insights it yields using data from experimental infection studies on equine arteritis virus. Finally, we demonstrate via simulation studies that the NLHM substantially reduces bias and improves the power to detect differences in relevant features of the infection response between two populations. For example, to detect a 20% difference in response duration between two groups (n=15) in which the peak time and peak intensity were identical, the RM-ANOVA test had a power of just 11%, and LMM a power of just 12%. By comparison, the nonlinear model we propose had a power of 58% in the same scenario, while controlling the Type I error rate better than the other two methods.

Nonlinear hierarchical model

Infection response

Viral challenge

Monte Carlo simulation

Longitudinal data analysis

Details

Title: Subtitle: Nonlinear hierarchical modeling of experimental infection data
Creators: Michael D Singleton - Department of Biostatistics, University of Kentucky College of Public Health, Lexington, KY 40513, United States
Patrick J Breheny - Department of Biostatistics, University of Iowa College of Public Health, Iowa City, IA 52242, United States
Resource Type: Journal article
Publication Details: Preventive veterinary medicine, Vol.130, pp.129-136
Publisher: Elsevier B.V
DOI: 10.1016/j.prevetmed.2016.06.006
PMID: 27435656
ISSN: 0167-5877
eISSN: 1873-1716
Language: English
Date published: 08/01/2016
Academic Unit: Biostatistics
Record Identifier: 9983997367202771

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