Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
Yi Liu of the Department of Biostatistics defends her dissertation on "Novel Test Procedures for Large-scale Bivariate Time-to-event Data on Single and Gene-based Markers, with an Application to a Genetic Study of AMD Progression"
Graduate faculty of the University and all other interested parties are invited to attend
Motivated by a genome-wide association study (GWAS) to discover risk variants for the progression of Age-related Macular degeneration (AMD), I develop a computationally efficient copula-based score test, in which the association between bivariate progression times is explicitly modeled. Specifically, a two-step estimation approach with numerical derivatives to approximate the score function and information matrix is proposed. Both parametric and weakly parametric marginal distributions under the proportional hazards assumption are considered. Further I extend this work to gene-based tests through the functional linear smoothing approach, which models the variants (within the same gene region) as a function of their physical positions. A robust variance approach under functional linear model framework for bivariate variance adjustment is also been proposed. In both works, simulation studies were conducted to evaluate the Type I error control and power performance of the proposed method. Finally, we apply our method on a large randomized trial data, Age-related Eye Disease Study (AREDS), to identify susceptible risk variants and gene regions for AMD progression. The top variants identified in CFH and ARMS2 gene on Chromosome 1 and 10 show differential progression profiles for different genetic groups, which are useful in characterizing and predicting the risk of progression for patients with moderate AMD.
Last Updated On Monday, October 23, 2017 by Valenti, Renee Nerozzi
Created On Friday, June 09, 2017