BhGLM- Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology

School of Public Health

A team of researchers, including Dr. Nengjun Yi, Professor, Mr. Boyi Guo, doctoral student, and alum Dr. Xinyan Zhang (now an Assistant Professor at Georgia Southern University) all from the Department of Biostatistics from UAB’s School of Public Health, and Dr. Zaixiang Tang, an Associate Professor in the Department of Biostatistics, School of Public Health, Medical College of Soochow University, China who was a Visiting Scholar at UAB’s School of Public Health from 2017 –2018  worked collaboratively to develop a series of Bayesian hierarchical models for analyzing large-scale molecular and clinical data.

BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four types of prior distributions for coefficients, i.e. double-exponential, Student-t, mixture double-exponential and mixture Student-t. These functions adapt fast and stable algorithms to estimate parameters.

BhGLM also provides functions for summarizing results numerically and graphically and for evaluating predictive values. The package is particularly useful for analyzing large-scale molecular data, i.e. detecting disease-associated variables and predicting disease outcomes. We here describe the models, algorithms and associated features implemented in BhGLM.

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