Bayesian Biostatistics

With the rapid increase in biomedical technology and the accompanying generation of complex and high-dimensional data sets, Bayesian statistical methods have become much more widely used. One reason is that the Bayesian probability modeling machinery provides a natural framework for integration of data and information from multiple sources, while accounting for uncertainty in model specifications. This chapter briefly reviews some of the recent areas in which Bayesian biostatistical research has had the greatest impact. Particular areas of focus include correlated and longitudinal data analysis, event time data, nonlinear modeling, model averaging, and bioinformatics. The reader is referred elsewhere for recent Bayesian developments in other important areas, such as clinical trials and analysis of spatially correlated data. Certainly the many practical and conceptual advantages of the Bayesian paradigm will lead to an increasing impact in future biomedical research, particularly in areas such as genomics. © 2005 Elsevier B.V. All rights reserved.

Duke Scholars

Author David B. Dunson Statistical Science