Analysis of binary data with the possibility of wrong ascertainment

作者：Roy Surupa^*; Das Kalyan; Sarkar Angshuman

来源：Statistica Neerlandica, 2013, 67(3): 293-310.

DOI：10.1111/stan.12008

• binary regression
• random-effects model
• misclassified responses
• MCMC

Most genetic studies recruit high-risk families, and the discoveries are based on non-random selected groups. We must consider the consequences of this ascertainment process to apply the results of genetic research to the general population. In addition, in epidemiological studies, binary responses are often misclassified. We proposed a binary logistic regression model that provides a novel and flexible way to correct for misclassification in binary responses, taking into account the ascertainment issues. A hierarchical Bayesian analysis using Markov chain Monte Carlo method has been carried out to investigate the effect of covariates on disease status. The focus of this paper is to study the effect of classification errors and non-random ascertainment on the estimates of the model parameters. An extensive simulation study indicated that the proposed model results in substantial improvement of the estimates. Two data sets have been revisited to illustrate the methodology.

• 出版日期2013-8