Analysis of schizophrenia data using a nonlinear threshold index logistic model

Z. Jiang, C. Du, Assen Jablensky, H. Liang, Z. Lu, M. Yang, K.L. Teo

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    © 2014 Jiang et al. 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.
    Original languageEnglish
    Article numbere109454
    Pages (from-to)1-11
    Number of pages11
    JournalPLoS One
    Volume9
    Issue number10
    DOIs
    Publication statusPublished - 17 Oct 2014

    Fingerprint Dive into the research topics of 'Analysis of schizophrenia data using a nonlinear threshold index logistic model'. Together they form a unique fingerprint.

    Cite this