Bayesian Thinking in Biostatistics
- Available for pre-order. Item will ship after March 16, 2021
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.
- 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.
Table of Contents
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 11Survival 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 works in the Department of Biostatistics at the M.D. Anderson Cancer Center in Texas.