Statistical Tools for Environmental Quality Measurement
It would be unfair to review a book without considering how well it serves its target readers. At first, I was slightly disappointed with the second edition of this book, because I was expecting more new materials presented since the first edition of 1997. However, after reading the newly added materials and thinking about how the book is used, I started appreciating the authors’ efforts. Their motivation for the first edition is reemphasized in the new edition. There have been substantial advances in the statistical methodology for censored and truncated data, and the authors “attempt to make these complex methods more accessible to applied researchers without an advanced mathematical background by presenting the essence of the statistical methods and illustrating these results in an applied framework” (p. vi). The new materials in the second edition help affirm the authors’ goal. The authors envision two complementary uses for the book: “The first is a reference book for investigators who find the need to analyze censored or truncated life time data. The second use is as a textbook for a graduate level in survival analysis” (p. vi). The most noticeable addition is solutions to the odd-numbered revised exercises, which facilitates self-study. As for the survival analysis technique content, the essential new material is the Lin and Ying additive hazards model. This is the reason for my slight disappointment. There are other additions, including tests of equality of survival curves at a fixed point in time in Chapter 7 and coding covariate and discretizing a continuous covariate in Chapter 8. I suppose that the authors added these sections based on the feedback from some users of the first edition. To some extent, these additions reveal the authors’ expectations about the level of the readership, and perhaps can help an instructor determine whether this book is a good choice for a textbook on survival analysis. If many students in the class need a review on coding covariate in regression, then this book may be a good choice. The book’s most significant and possibly controversial feature is that the materials are carefully presented with little technical difficulty involved. Technical details are sketched in a series of theoretical notes at the end of each section or are separated into their own sections, which can be skipped without a loss of continuity. This is designed to fulfill the authors’ goal of making complex methods accessible to applied researchers without a strong mathematical background. The authors obviously have a lot of experience in teaching at this level and in consulting with various investigators. The book may serve the claimed purposes well in their practice. On the other side of the coin, readers who want to know more than just how to apply those methods may find the book less useful. Those who have more mathematical training and are interested in learning the whys may find the text hard to follow. For example, the variance estimator of the product-limit estimator is given on page 92, after a passing mention of Greenwood’s formula. I was uncomfortable the first time I read it. I wished that the text or the theoretical appendix had provided more clues; a few lines like those on page 17 of Kalbfleisch and Prentice (2002) would have made me feel much better. However, there are people who do not want to worry too much about the whys and just want to apply the methods. This may be the case for students from agricultural and life sciences. A colleague once related that the classroom temperature seemed to drop when a derivation was being developed on the board in an introductory-level biostatistics course. For these audiences, I may use this book as a textbook. People who want more technical details may find other books more suitable. [I would recommend Kalbfleisch and Prentice (2002) for statistics/biostatistics majors.] This book puts more weight on hypothesis testing of survival curves than most other texts. Its 13 chapters can be grouped into five themes: basic concepts and terminology, estimation of summary survival statistics, hypothesis testing, regression analysis, and multivariate models. Chapter 7 is the single chapter on the theme of hypothesis testing. The authors must have found a frequent demand for comparing survival curves in their experience. Actually, I have learned from consulting that there really is much need for comparing survival curves across two or more groups. Regression with covariate effects is more interesting and challenging; nevertheless, many researchers face the problem of comparing survival curves for two treatment groups. The text is not bound to any particular software in general, except some SAS macros mentioned in the practical notes. This software independence is a feature that I like, although others may disagree. The recently published third edition of the text by Lee and Wang (2003), for example, provides codes for software packages BPMD, SAS, and SPSS. In my opinion, computing should be treated as a skill just like reading and writing. Including codes for certain software may expedite the learning process if a reader has access to the software used in the book. However, the binding of methods and software may bias the reader’s preferences and distract attention from understanding the methods. I wish that the second edition had included more recent topics of survival analysis, for example, recurrent-event data analysis and accelerated failure time models with error distribution unspecified. Some of the existing topics could have been presented in a more concise manner. Nevertheless, this book has plenty to offer for a oneor two-semester course for nonstatistics majors. Overall, I think this book does a reasonably good job for the target audience—applied researchers without an advanced mathematical background—who are more interested in applying the techniques of survival analysis than in understanding the theories.