Faculty and Alumnus Author Article in Journal of Data Science

Dr. Kesheng Wang, Associate Professor in the Department of Biostatistics and Epidemiology, is lead author of a paper in the Journal of Data Science. The paper, “Comparison of Cox regression and parametric models for survival analysis of genetic variants in HNF1B gene related to age at onset of cancer” aimed to compare the survival models in genetic association analysis of age at onset of cancer in patients. 

Co-authors includes Dr. Daniel Owusu, a Doctor of Public Health alumnus from the Department of Biostatistics and Epidemiology along with individuals from University of Miami, University of Michigan, and University of Texas Rio Grande Valley. 

Scientists have known for years that age is a leading risk factor for the development of many types of cancer, but why aging increases cancer risk remains unclear. Researchers suspect that DNA methylation, or the binding of chemical tags, called methyl groups, onto DNA, may be involved.  Semi-parametric Cox regression and parametric methods have been used to analyze survival data of cancer; however, no study has focused on the comparison of survival models in genetic association analysis of age of onset of cancer in patients. 

This study examined 23 single nucleotide polymorphisms within the HNF1B gene in the Marshfield sample with 716 cancer cases and 2,848 non-cancer controls. Cox proportional hazards models and parametric survival models were used to detect the genetic association of HNF1B gene with the analysis of age of onset of cancer. The Akaike information criterion and Bayesian information criterion were used to compare the Cox models and parametric survival models. 

This study concluded that the parametric Weibull distribution is the best model for the genetic association of analysis of age of onset of cancer and provides the first evidence of several genetic variants within the HNF1B gene associated with analysis of age of onset of cancer. 

The Journal of Data Science publishes research works on a wide range of topics that involving understanding and making effective use of field data.  This journal is devoted to applications of statistical methods at large. The goal of this journal is to enable scientists to do their research on applied science and through effective use of data.