Detection of smoothly distributed spatial outliers, with applications to identifying the distribution of parenchymal hyperinflation following an airway challenge in asthmatics

Andrew L Thurman; Jiwoong Choi; Sanghun Choi; Ching-Long Lin; Eric A Hoffman; Chang Hyun Lee; Kung-Sik Chan

doi:10.1002/sim.7216

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Detection of smoothly distributed spatial outliers, with applications to identifying the distribution of parenchymal hyperinflation following an airway challenge in asthmatics

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Detection of smoothly distributed spatial outliers, with applications to identifying the distribution of parenchymal hyperinflation following an airway challenge in asthmatics

Andrew L Thurman, Jiwoong Choi, Sanghun Choi, Ching-Long Lin, Eric A Hoffman, Chang Hyun Lee and Kung-Sik Chan

Statistics in medicine, Vol.36(10), pp.1638-1654

05/10/2017

DOI: 10.1002/sim.7216

PMCID: PMC5842699

PMID: 28132419

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http://doi.org/10.1002/sim.7216View

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Abstract

Methacholine challenge tests are used to measure changes in pulmonary function that indicate symptoms of asthma. In addition to pulmonary function tests, which measure global changes in pulmonary function, computed tomography images taken at full inspiration before and after administration of methacholine provide local air volume changes (hyper-inflation post methacholine) at individual acinar units, indicating local airway hyperresponsiveness. Some of the acini may have extreme air volume changes relative to the global average, indicating hyperresponsiveness, and those extreme values may occur in clusters. We propose a Gaussian mixture model with a spatial smoothness penalty to improve prediction of hyperresponsive locations that occur in spatial clusters. A simulation study provides evidence that the spatial smoothness penalty improves prediction under different data-generating mechanisms. We apply this method to computed tomography data from Seoul National University Hospital on five healthy and ten asthmatic subjects. Copyright © 2017 John Wiley & Sons, Ltd.

Biostatistics

Asthma - physiopathology

Respiratory Function Tests - statistics & numerical data

Humans

Middle Aged

Male

Asthma - diagnostic imaging

Models, Statistical

Case-Control Studies

Normal Distribution

Bronchial Hyperreactivity

Likelihood Functions

Tomography, X-Ray Computed - statistics & numerical data

Bronchial Provocation Tests - statistics & numerical data

Computer Simulation

Adult

Female

Methacholine Chloride

Details

Title: Subtitle: Detection of smoothly distributed spatial outliers, with applications to identifying the distribution of parenchymal hyperinflation following an airway challenge in asthmatics
Creators: Andrew L Thurman - Department of Statistics and Actuarial Science, College of Liberal Arts and Sciences, The University of Iowa, Iowa City, 52242, IA, U.S.A
Jiwoong Choi - Department of Mechanical and Industrial Engineering, College of Engineering, The University of Iowa, Iowa City, 52242, IA, U.S.A
Sanghun Choi - Department of Mechanical and Industrial Engineering, College of Engineering, The University of Iowa, Iowa City, 52242, IA, U.S.A
Ching-Long Lin - Department of Mechanical and Industrial Engineering, College of Engineering, The University of Iowa, Iowa City, 52242, IA, U.S.A
Eric A Hoffman - Department of Biomedical Engineering, College of Engineering, The University of Iowa, Iowa City, 52242, IA, U.S.A
Chang Hyun Lee - Department of Radiology, Seoul National University Hospital, 28 Yeongeon-dong, Jongno-gu, Seoul, 110-744, Korea
Kung-Sik Chan - Department of Statistics and Actuarial Science, College of Liberal Arts and Sciences, The University of Iowa, Iowa City, 52242, IA, U.S.A
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.36(10), pp.1638-1654
DOI: 10.1002/sim.7216
PMID: 28132419
PMCID: PMC5842699
NLM abbreviation: Stat Med
ISSN: 0277-6715
eISSN: 1097-0258
Publisher: England
Grant note: U01 HL114494 / NHLBI NIH HHS P30 ES005605 / NIEHS NIH HHS R01 HL089897 / NHLBI NIH HHS R01 HL112986 / NHLBI NIH HHS
Language: English
Date published: 05/10/2017
Academic Unit: Statistics and Actuarial Science; Roy J. Carver Department of Biomedical Engineering; Radiology; Mechanical Engineering; Internal Medicine
Record Identifier: 9984051725202771

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