7 - Nonparametric Bayes applications to biostatistics

Summary

This chapter provides a brief review and motivation for the use of nonparametric Bayes methods in biostatistical applications. Clearly, the nonparametric Bayes biostatistical literature is increasingly vast, and it is not possible to present properly or even mention most of the approaches that have been proposed. Instead, the focus here is entirely on methods utilizing random probability measures, with the emphasis on a few approaches that seem particularly useful in addressing the considerable challenges faced in modern biostatistical research. In addition, the emphasis will be entirely on practical applications-motivated considerations, with the goal of moving the reader towards implementing related approaches for their own data. Readers interested in the theoretical motivation, which is certainly a fascinating area in itself, are referred to the cited papers and to Chapters 1–4.

Introduction

Biomedical research has clearly evolved at a dramatic rate in the past decade, with improvements in technology leading to a fundamental shift in the way in which data are collected and analyzed. Before this paradigm shift, studies were most commonly designed to be simple and to focus on relationships among a few variables of primary interest. For example, in a clinical trial, patients may be randomized to receive either the drug or placebo, with the analysis focusing on a comparison of means between the two groups. However, with emerging biotechnology tools, scientists are increasingly interested in studying how patients vary in their response to drug therapies, and what factors predict this variability.