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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2017
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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.
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thurman2017detectionstatistics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Thurman, Andrew L;Choi, Jiwoong;Choi, Sanghun;Lin, Ching-Long;Hoffman, Eric A;Lee, Chang Hyun;Chan, Kung-Sik;
Journal Statistics in Medicine
Year 2017
DOI
10.1002/sim.7216
URL
Keywords
asthma generalized additive model methacholine challenge mixture panel of random fields

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