Maximizing power in seroepidemiological studies through the use of the proportional odds model

Ana W Capuano; Jeffrey D Dawson; Gregory C Gray

doi:10.1111/j.1750-2659.2007.00014.x

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Maximizing power in seroepidemiological studies through the use of the proportional odds model

Journal article

Open access

Peer reviewed

Maximizing power in seroepidemiological studies through the use of the proportional odds model

Ana W Capuano, Jeffrey D Dawson and Gregory C Gray

Influenza and other respiratory viruses, Vol.1(3), pp.87-93

05/2007

DOI: 10.1111/j.1750-2659.2007.00014.x

PMCID: PMC2174695

PMID: 18176626

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Abstract

Epidemiological studies of zoonotic influenza and other infectious diseases often rely upon analysis of levels of antibody titer. In most of these studies, the antibody titer data are dichotomized based on a chosen cut-point and analyzed with a traditional binary logistic regression. However, cut-points are often arbitrary, particularly those selected for rare diseases or for infections for which serologic assays are imperfect. Alternatively,the data can be left in the original form, as ordinal levels of antibody titer, and analyzed using the proportional odds model. We show why this approach yields superior power to detect risk factors. Additionally, we illustrate the advantages of using the proportional odds model with the analyses of zoonotic influenza antibody titer data.

Seroepidemiologic Studies

Animals

Models, Statistical

Communicable Diseases - epidemiology

Humans

Risk Factors

Details

Title: Subtitle: Maximizing power in seroepidemiological studies through the use of the proportional odds model
Creators: Ana W Capuano - Center for Emerging Infectious Diseases, Department of Epidemiology, University of Iowa College of Public Health, 2501 Crosspark Road, Coralville, IA 52241, USA. ana-capuano@uiowa.edu
Jeffrey D Dawson
Gregory C Gray
Resource Type: Journal article
Publication Details: Influenza and other respiratory viruses, Vol.1(3), pp.87-93
DOI: 10.1111/j.1750-2659.2007.00014.x
PMID: 18176626
PMCID: PMC2174695
NLM abbreviation: Influenza Other Respir Viruses
ISSN: 1750-2640
eISSN: 1750-2659
Publisher: England
Grant note: R01 AI068803 / NIAID NIH HHS R21 AI 059214-01 / NIAID NIH HHS R21 AI059214 / NIAID NIH HHS R01 AI068803-01 / NIAID NIH HHS
Language: English
Date published: 05/2007
Academic Unit: Public Health Administration; Epidemiology; Biostatistics
Record Identifier: 9983997336002771

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