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Bayesian model selection for logistic regression with misclassified outcomes

Richard Gerlach, Discipline of Econometrics and Business Statistics, Faculty of Economics and Business, University of Sydney, H04, Sydney, NSW, Australia, 2006. E-mail: r.gerlach{at}econ.usyd.edu.au

James Stamey

James Stamey is a Baylor University, Texas, US

We consider the problem of variable selection for logistic regression when the dependent variable is measured imperfectly, under both differential and non-differential misclassification. An MCMC sampling scheme is designed, incorporating uncertainty about which explanatory variables affect the dependent variable and which affect the probability of misclassification. We assume that a small gold standard perfectly measured sample is available to augment the imperfectly measured sample, under the differential misclassification framework. A simulation study illustrates favourable results both in terms of variable selection and parameter estimation. Examples analysing the risk of violence against young women by their partner and the risk of injury in highway motor accidents are considered.

Key Words: logistic regression • Metropolis Hastings • misclassification • model uncertainty

Statistical Modelling, Vol. 7, No. 3, 255-273 (2007)
DOI: 10.1177/1471082X0700700303


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