United States
£ GBP
With a focus on incorporating sensible prior distributions and discussions on many recent developments in Bayesian methodologies, Bayesian Thinking in Biostatistics considers statistical issues in biomedical research. The book emphasizes greater collaboration between biostatisticians and biomedical researchers. The text includes an overview of Bayesian statistics, a discussion of many of the methods biostatisticians frequently, such as rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests.
Key Features:
- Applies a Bayesian perspective to applications in biomedical science.
- Reviews Bayesian statistics and methods for Bayesian analysis.
- Highlights advances in clinical trial design.
- Provides methods for evaluating diagnostic tests.
The intended audience includes graduate students in biostatistics, epidemiology, and biomedical researchers.
1. Scientific Data Analysis
2. Fundamentals I: Bayes Theorem, Knowledge Distributions, Prediction
3. Fundamentals II: Models for Exchangeable Observations
4. Computational Methods for Bayesian Analysis
5. Comparing Populations
6. Specifying Prior Distributions
7. Linear Regression
8. Binary Response Regression
9. Poisson and Non-linear Regression
10. Model Assessment
11. Survival Modeling I: Models for Exchangeable Observations
12. Survival Modeling 2: Time-to-Event Regression Models
13. Clinical Trial Designs
14. Hierarchical Models and Longitudinal Data
15. Diagnostic Tests
Gary L. Rosner is the Eli Kennerly Marshall, Jr., Professor of Oncology at the Johns Hopkins School of Medicine and Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Purushottam (Prakash) W. Laud is a Professor in the Division of Biostatistics, and Director of the Biostatistics Shared Resource for the Cancer Center, at the Medical College of Wisconsin. Wesley O. Johnson is Professor Emeritus in the Department of Statistics at the University of California, Irvine.
"This thoroughly modern Bayesian book [...] is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of illuminating applications. These are activated by excellent coverage of computing methods and provision of code. Their content on model assessment, robustness, data-analytic approaches and predictive assessments [...] are essential to valid practice. The numerous exercises and professional advice make the book ideal as a text for an intermediate-level course [...] "
– Thomas Louis, Johns Hopkins University
"The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes it a complete self-contained introduction to Bayesian inference for biomedical problems [...] .Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems."
– Peter Mueller, University of Texas
