Szmaragd, Camille, Green, Laura E., Medley, Graham and Browne, William J.. (2012) Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis. PLoS ONE, Vol.7 (No.8). e43116. ISSN 1932-6203

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Abstract

Background Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study. Methodology/Principal Findings To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the ‘best’ model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable. Conclusions/Significance We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses.

Item Type:	Journal Article
Subjects:	S Agriculture > SF Animal culture
Divisions:	Faculty of Science > Life Sciences (2010- )
Library of Congress Subject Headings (LCSH):	Diseases -- Risk factors -- Mathematical models, Tuberculosis in cattle -- Risk factors -- Mathematical models, Diagnostic errors
Journal or Publication Title:	PLoS ONE
Publisher:	PLOS
ISSN:	1932-6203
Date:	2012
Volume:	Vol.7
Number:	No.8
Page Range:	e43116
Identification Number:	10.1371/journal.pone.0043116
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Great Britain. Dept. for Environment, Food & Rural Affairs (DEFRA)
Grant number:	SE3239 (DEFRA)
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URI:	http://wrap.warwick.ac.uk/id/eprint/49701

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