The Design and Analysis of Computer Experiments
of nonnested models, and only slight modifications needed in the presence of missing data. While reading the book, I applied the Bayesian method to a frailty model that I had been thinking about, and was impressed to get some preliminary results using the BUGS software with little developmental effort. I am still working on the problem in a frequentist way, but the quick Bayesian approach serves as an exploratory analysis and may also serve as a benchmark for comparison purposes. As a researcher with interest in survival analysis but not much serious experience in Bayesian methods, I feel that the authors do a great job demonstrating these advantages and showing that “the analysis of survival data can be carried out under the Bayesian paradigm in a straightforward and unified way” (p. 27). An apparent feature of the book is its thorough coverage of Bayesian survival analysis. As implied by the word “Bayesian” in the title, this book is different from classic survival books such as the text by Kalbfleisch and Prentice (2003). Parametric models (Chap. 2), semiparametric models (Chap. 3), and frailty models (Chap. 4) are presented first and account for about one-third of the book. Frequentist treatments of these topics can be found in any survival textbook. In the remainder of the book, cure-rate models (Chap. 5), joint models for longitudinal and survival data (Chap. 7), missing covariate data (Chap. 8), and design and monitoring of randomized clinical trials (Chap. 9), are covered in detail. These topics are usually not covered (at least in much detail) in a classical survival book or in a traditional survival course, but they are important in the general field of survival analysis, and the number of articles on them in statistical journals is rapidly growing. A researcher who has been deep in his or her (possibly narrow) area of expertise may use this book to quickly grasp the nature of these important problems and get a better picture of the broader field of survival analysis. Model comparison in the Bayesian way, a unique feature of this book, is presented in Chapter 6. Few survival books discuss model comparison, particularly for nonnested models. Few Bayesian books discuss model comparison in the particular field of survival analysis. This chapter presents a good survey of Bayesian model-comparison methods. Various other topics of Bayesian survival analysis and future research topics are discussed in Chapter 10. It would be worthwhile to review these topics in a seminar course with advanced graduate students who are in search of interesting thesis topics, Bayesian or otherwise. The authors’ goal is to present a balance between theory and applications. For a reader like me, they have succeeded. Detailed examples and data analyses remind the reader of what problems are to be solved and help prevent the reader from getting lost in the development of likelihoods and posterior distributions. For more advanced topics, particularly those in Chapter 10, computation algorithms are presented in steps. These features make the book more accessible to a wider audience and distinguish it from the more theoretical ones like the text by Ghosh and Ramamoorthi (2003), which contains two chapters on Bayesian nonparametrics for survival data. On the other hand, this book is not a cookbook of recipes for Bayesian survival analysis. For each problem, there is sufficient discussion to make the line of reasoning visible and continuous. The methodology is presented in the main body of each chapter, with rigorous proofs and derivations relegated to appendixes. The book has a website, linked from the publisher’s website, which provides computer code and datasets. Statistical research should be reproducible, and datasets should be made available to anyone wishing to examine them. Electronically available code and data is a more reasonable request for books than for journal articles. Nowadays, books on data analysis usually provide their code/datasets on a compact disc or from a companion website, which is much easier to keep updated. I am glad that this book is one of them. My attention to the book was actually first drawn by its website. I found it very useful, and have used their code as a starting point for learning. According to the Preface, “the book is intended as a graduate textbook or a reference book for a oneor two-semester course at the advanced masters or PhD level.” The authors suggest that the book be used after mid-level courses in statistical inference and Bayesian theory. But from my experience, a user may appreciate the book more after a first course in survival analysis. A student not only can learn how to fit the classic survival models in a Bayesian way, but also can gain exposure to other various important problems and their Bayesian solution in the field of survival analysis. These other topics would otherwise have to be studied from reading articles scattered in the literature. Many problems pointed out in the book may lead to thesis research. It well qualifies as a valuable reference “for applied or theoretical researchers as well as practitioners” (p. viii). The book is well organized and presented. The materials are grouped cleanly into chapters based on the different model types and different problems. After the first four chapters on basic survival models, Chapters 5–10 discuss relatively independent topics and hence can be read separately. The table of contents can be used as a site map of Bayesian survival analysis. I have only minor criticisms of this book. In later sections in the book (e.g., p. 50), G(α,λ) refers to a gamma distribution, with λ meaning the scale parameter. But when I went back to check the density of the gamma on page 40, G(α,λ) is parameterized such that λ is the logarithm of the scale parameter instead of the scale parameter itself, as in other textbooks. Both notations are locally well defined. I understand that the purpose of changing the parameterization is to incorporate a covariate into λ, but I was confused for a little while. These minor inconsistencies do not affect the integrity and readability of the book. I enjoyed reading this book and will keep it on my shelf for reference.