In psychiatric and behavioral research, about six out of ten people with a substance use disorder suffer from another form of mental illness as well, making it necessary to consider multiple conditions as we study the etiologies of these conditions. The occurrence of multiple disorders in the same patient is referred to as comorbidity. Identifying the risk factors for comorbidity is an important yet difficult topic in psychiatric research. The effort of studying the genetics for comorbidity can be traced back to a century ago. It is important to consider and develop inferential tools for multivariate outcomes, particularly when the outcomes are discrete. There is extensive literature on the statistical analysis of multivariate normal variables as well as on nonparametric tests for a single variable of non-normal distribution. However, few options are available for the inference when we have multiple non-normally distributed variables and potentially a hybrid of continuous and discre te variables. To overcome this challenge, we made use of several useful statistical techniques such as the rank-based U-statistics and the kernel-based weighted statistics to accommodate the mix of continuous and discrete outcomes and the presence of important covariates. We conducted thorough simulation and analytic evaluation to assess the control of the type I error and the power of our proposed test. Both empirical and theoretical results suggest that our proposed test increases the power of testing association when adjusting for the covariates. Applications of our test to real data sets also reveal novel insights.
This presentation includes a series of joint work with Xiang Chen, Kelly Cho, Yuan Jiang, Ching-Ti Liu, Xueqin Wang, and Wensheng Zhu.
More information on Heping Zhang may be found at http://publichealth.yale.edu/people/heping_zhang.profile
This Colloquium is sponsored jointly by the University of Georgia Department of Statistics and the University of Georgia Department of Epidemiology and Biostatistics.