Medicine and Health Sciences Commons^™

Analysis Of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model., Zhenyu Jiang, Chengan Du, Assen Jablensky, Hua Liang, Zudi Lu, Yang Ma, Kok Lay Teo

GW Biostatistics Center

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 …