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dc.contributor.authorJiang, Z.
dc.contributor.authorDu, C.
dc.contributor.authorJablensky, A.
dc.contributor.authorLiang, H.
dc.contributor.authorLu, Z.
dc.contributor.authorMa, Y.
dc.contributor.authorTeo, Kok Lay
dc.identifier.citationJiang, Z. and Du, C. and Jablensky, A. and Liang, H. and Lu, Z. and Ma, Y. and Teo, K.L. 2014. Analysis of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model. PloS One. 9 (10): pp. e09454-1-e109454-11.

Genetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction. A maximum likelihood methodology is suggested to estimate the unknown parameters in the models. Simulation studies demonstrate that the proposed methodology works viably well for moderate-size samples. The suggested approach is therefore applied to the analysis of the Schizophrenia classification by using a real set of SNP data from Western Australian Family Study of Schizophrenia (WAFSS). Our empirical findings provide evidence that the proposed nonlinear models well outperform the widely used linear and tree based logistic regression models in class prediction of schizophrenia risk with SNP data in terms of both Types I/II error rates and ROC curves.

dc.titleAnalysis of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model
dc.typeJournal Article
dcterms.source.titlePloS One

This article is published under the Open Access publishing model and distributed under the terms of the Creative Commons Attribution License Please refer to the licence to obtain terms for any further reuse or distribution of this work.

curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusOpen access

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