Applications of Bayesian Shrinkage Prior Models in Clinical Research with Categorical Responses

Background: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions like diabetes, colon cancer, and Alzheimer’s disease. Developing accurate prediction and classification methods have implications for personalized medicine. Building an excellent predictive model involves selecting features that are most significantly associated with the response at hand. These features can include several biological and demographic characteristics, such as genomic biomarkers and health history. Such variable selection becomes challenging when the number of potential predictors is large. Bayesian shrinkage models have emerged as popular and flexible methods of variable selection in regression settings. The article discusses variable selection with three shrinkage priors and illustrates its application to clinical data sets such as Pima Indians Diabetes, Colon cancer, ADNI, and OASIS Alzheimer’s data sets.