BOOK REVIEWS: 9

Biometrics 61, 1129–1140December 2005

BOOK REVIEWS

EDITOR:I. PIGEOT

Modelling Forest Systems(A. Amaro, D. Reed, andP. Soares, eds) Jerry K. Vanclay

Ranked Set Sampling. Theory and Applications(Z. Chen, Z. Bai, andB. K. Sinha) Mohammad Fraiwan Al-Saleh

Multiple Analyses in Clinical Trials. Fundamentals forInvestigators(L. A. Moye) Joachim Roehmel

Association Schemes—Designed Experiments, Algebraand Combinatorics(R. A. Bailey) Aloke Dey

Nonparametric Statistical Methods for Complete andCensored Data(M. M. Desu and D. Raghavarao) Robert F. Woolson

Step-by-Step Basic Statistics Using SAS (StudentGuide and Exercises)(L. Hatcher) Ronald Cody

Applied Spatial Statistics for Public Health Data(L. A. Waller and C. A. Gotway) Olaf Berke

Constrained Statistical Inference. Inequality, Orderand Shape Restrictions(M. J. Silvapulle and P. K. Sen) Anthony Hayter

American Journal of Mathematical and ManagementSciences, Vol. 23 (3&4)(E. J. Dudewicz, B. L. Golden, andZ. Govindrajulu, eds) Jan Beirlant

Numerical Methods for Nonlinear EstimatingFunctions(C. G. Small and J. Wang) Richard Morton

The Statistical Evaluation of Medical Tests for Classi-fication and Prediction(M. S. Pepe) James A. Hanley

Linear Models with R(J. J. Faraway) Ronja Foraita

Statistical Analysis and Data Display. An Interme-diate Course with Examples in S-PLUS, R, andSAS(R. M. Heiberger and B. Holland) John S. J. Hsu

Brief Reports by the Editor

Testing Statistical Hypotheses, 3rd edition(E. L. Lehmann and J. P. Romano)

Recent Advances in Quantitative Methods in Cancerand Human Health Risk Assessment(L. Edler and C. P. Kitsos, eds)

Handbook of Statistics: Data Mining and DataVisualization(C. R. Rao, E. J. Wegman, and J. L. Solka, eds)

Selection Bias and Covariate Imbalances in Random-ized Clinical Trials(V. W. Berger)

Analysis of Clinical Trials Using SAS: A PracticalGuide(A. Dmitrienko, G. Molenberghs, C. Chuang-Stein, and W.Offen, eds)

Genetic Analysis of Complex Traits Using SAS(A. M. Saxton, ed)

A Handbook of Statistical Analyses Using Stata, 3rdedition(S. Rabe-Hasketh and B. Everitt)

AMARO, A., REED, D., and SOARES, P. (eds) ModellingForest Systems. CABI Publishing, Wallingford, U.K., 2003.432 pp. US$140.00/£75.00 (hardcover), ISBN 0-85199-693-0.

This book contains 34 papers from a workshop held inSesimbra, Portugal in 2002. The papers are grouped into fivesections: modeling strategies; mathematical approaches; esti-mation processes; validation; and metadata. Each section con-

tains several papers that illustrate the diversity of approachesentertained in forest modeling. The book is not a recipe bookshowing “how to build a model,” but rather offers a compre-hensive overview of the state of play in forest modeling, cov-ering a broad range of issues from broad scale mapping siteindex to the challenge of archiving models and their metadatafor future reference. This diversity makes the book a good re-source to provoke discussion among graduate students. For

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instance, the first chapter by Harold Burkhart dwells some-what on the importance of parsimony, but several subsequentchapters present models with not just tens, but scores of esti-mated parameters. Several papers are reflective, commentingon the development and evolution of methods and models(e.g., chapter 8 by Ralph Amateis), while others are moreforward looking, outlining plans and recommendations for fu-ture work (e.g., chapter 7 by Heyns Kotze). Some papersdocument traditional growth and yield models (e.g., chapter 9by Paula Soares and Margarida Tome, relating to pulpwoodproduction) while others focus on a broader range of goodsand services (e.g., chapter 26 by Paul van Gardingen). Manyof the papers deal with single-species plantations, but someaddress natural forests with many species (e.g., chapter 21 byNicolas Picard, Sylvie Gourlet-Fleury, and Plinio Sist relatingto tropical rainforest) and another deals with landscape-scalevisualization (chapter 30 by Falk-Juri Knauft). The book con-cludes with a series of papers examining the quality of dataand models (e.g., chapter 31 by David Reed and ElizabethJones) and examining our efforts to conserve and documentmodels so that they remain available for future researchersto examine and learn from (chapter 32 by Keith Rennolls).The book will be a useful resource for institutions offeringPhD programs or graduate-level courses in environmentalmodeling.

Jerry K. VanclaySchool of Environmental Science and Management

Southern Cross UniversityLismore, Australia

CHEN, Z., BAI, Z., and SINHA, B. K. Ranked Set Sam-pling. Theory and Applications. Springer, New York,2004. xii + 224 pp. US$59.95, ISBN 0-387-40263-2.

Ranked set sampling (RSS) was introduced by McIntyre in1952. In its simplest form, the RSS procedure consists of draw-ing k simple random samples (SRS) of size k each from thepopulation and ranking the elements within each sample, byjudgment, with respect to the characteristic of interest. Thenthe ith smallest observation from the ith sample is chosen forquantification. This procedure increases the chance of obtain-ing a more representative sample.

This book, written by three experts in RSS, is the first thataddresses most developments of RSS. A considerable part ofit is based on the authors’ own research. A description anda motivation of RSS are given in chapter 1. Chapters 2–4deal with inference; parametric as well as nonparametric in-ference is considered with balanced and unbalanced RSS. Re-sults of papers of some other authors are briefly outlined, butthe authors do not give enough attention to one importantmain result of Takahasi and Wakimoto (1968). Distribution-free tests are dealt with in chapter 5. Power comparisons arepresented for the two cases of perfect and imperfect ranking.RSS based on concomitant variables is discussed in chapter 6.The multi-layer RSS, a generalization of the bivariate RSSdeveloped earlier, as well as adaptive RSS, are outlined. Inchapter 7, which I consider the most interesting, the authorsexplore possibilities of using the so-called repeated RSS pro-cedure, another name for multi-stage RSS, for data reduction

especially in the case of huge data sets. Six case studies arethe content of the last chapter. Analysis of these data showsthe advantage of the RSS procedure over SRS.

The aim of the book is to give a systematic account ofthe theory and application of RSS; the intention was to covereach development of RSS since its birth. The object is some-how (but not completely) met; there are some developmentsin RSS that are either not mentioned or only cited with notenough details: e.g., the work on Bayesian RSS, and RSS andMonte Carlo integration; some variations and other applica-tions of RSS are missing. However, this is not a serious draw-back, given the huge number of papers on the topic.

The book is well laid out with concepts well explained.Each chapter (except 6 and 7) ends with a historical note ora bibliography, in which the authors cite some related refer-ences. Very few typos or minor inaccuracies have been foundby readers and fed back to the authors. The style is readable,with some examples throughout the book, but no exercises;this does not make it an ideal textbook. The lack of authorindex makes it hard to locate specific references in the text.

Besides for applied and theoretical statisticians, the bookcan be useful for users of statistics in various fields such asagricultural, environmental, and medical sciences, etc. Thosewho have recently become interested in the topic will find itan excellent start. In conclusion, I recommend this book asa reference book for researchers, for practitioners, and as atextbook for graduate/special topics course.

Reference

Takahasi, K. and Wakimoto, K. (1968). On unbiased esti-mates of the population mean based on the sample strat-ified by means of ordering. Annals of the Institute of Sta-tistical Mathematics 20, 1–30.

Mohammad Fraiwan Al-SalehDepartment of Statistics

Yarmouk UniversityIrbid, Jordan

MOYE, L. A. Multiple Analyses in Clinical Trials. Fun-damentals for Investigators. Springer, New York, 2003.xxiii + 436 pp. US$79.95, ISBN 0-387-00727.

In his introduction the author describes the target group ofreaders as clinical investigators at all levels, research groupswithin the pharmaceutical industry, medical students, publichealth students, health care researchers, physician-scientists,and regulators at the local, state, and federal level. Thisshould not preclude biostatisticians at all levels from read-ing this book, because there is a lot to learn for professionalbiostatisticians, e.g., from the excellent examples, from theanalytical skills to make severe problems in some of the stud-ies quite obvious, from the problems sections that come witheach of the 13 chapters, and from the sometimes surprisinglyfresh ideas and suggestions on how to cope with multiplicity.

Indeed, the book tries successfully to avoid complex math-ematical delineations, but this is by no means a drawback.On the contrary, it allows readers to get through the pagesfluently without the need to stop reading frequently to make

Book Reviews 1131

oneself more or less successfully familiar with abstract formu-las. The need for multiple analyses and the problems causedby this need are clearly motivated by numerous examples,often abstracted from clinical trials that have received world-wide attention. The many examples from well-known clinicaltrials are clearly one of the strengths of this book. It is alsofascinating to share the author’s experience with the Foodand Drug Administration (FDA) where he attended manymeetings of advisory committees.

It is a pity that views from other parts of the world arenot considered. Surely the author is not to blame for writ-ing down his special experience; however the time has passedwhen there was just one regulatory voice that raised concernor gave (possibly different) advice.

As an example, the book does not mention hierarchicalprocedures for controlling multiplicity. One can sympathizewith the author’s dislike regarding data-driven ordering ofhypotheses, but it is a completely different case if investiga-tors bring their hypotheses in an order that fits their researchinterests. In Europe, hierarchical ordering of hypotheses en-joys frequent applications, and at least for subgroup analysesit is by far the most often applied procedure. Generally, thefirst question to answer is which effects have the treatmentson the total study cohort, and whether the anticipated effectsare seen then to check consistency, i.e., to ask whether sim-ilar or different effects can be observed in some prespecifiedsubgroups. If, during the planning the investigator has soliddoubts in the effect of the experimental treatment on a partic-ular stratum of the total cohort, she should seriously considernot randomizing patients from this stratum. The additionaladvantage of hierarchical procedures (for subgroups, but alsoin analogy for composite endpoints) is that no sophisticatedsplits have to be calculated because always the same type Ierror applies.

The dependency measure that the author introduces tostudy and to make use of certain overlap in statistical hy-potheses will surely attract investigators. While the generalphilosophy behind the particular proposal to split the to-tal available amount of type I error for appropriate situa-tions is widely accepted, it does not seem to work sufficientlysatisfactorily.

For the issue of combined endpoints, the applicability ofthe dependency measure for splitting the type I error be-tween dependent hypotheses is at least as controversial, be-cause the calculation of dependency rests upon the anticipa-tion of the investigators at the planning stage, which can-not be confirmed easily once the data are observed. For thesituation described on page 309, where a subgroup makesup 85% of the total cohort, the total amount of 5% typeI error is split: 3% for the hypothesis test in the totalcohort and 3% in the subgroup. As the described situa-tion with 3311 observations per group in the total cohortcan easily be simulated, the presented results should be atleast close to the simulated results. But this is not thecase. Instead, one could choose a type I error level of 3.6%for each of the two hypotheses, or 4% for the total cohort and3% for the subgroup. For both situations, familywise type Ierror is controlled at 5%. In general, for many other casespresented in the book, simulation would be an attractive al-ternative.

An area with relevance for multiplicity is not covered: thenoninferiority clinical study. All null hypotheses presentedconcern the classical zero difference. Of course, results canoften be transferred easily from one situation to the other,but, for example, the two one-sided shifted hypotheses thatjointly describe equivalence are special and clearly of interestto clinical investigators. The opinion expressed in this bookthat the regulatory community continues to correctly rejectthe principle of one-tailed testing in clinical trials stands atleast on shaky ground, and many may even think it has beenabandoned since the publication of the ICH E9 guideline onStatistical Principles for Clinical Trials.

Sample size and appropriate sample size estimation consid-ering type I and type II errors during planning play a consid-erable role in this book. This is clearly justifiable because toooften investigators have spent little attention to this feature.It is, therefore, not helpful that the author does not educateinvestigators to use correct language. What is wrong when theauthor on page 250 writes that “the anticipated cumulativeincidence rate for this composite endpoint in the control groupis 35% and the investigators are interested in demonstratinga 20% reduction of this rate attributable to the therapy. As-suming a two-sided type I error level of 0.05 and 90% power,the sample size goal for this study is 1842 patients”? If theinvestigators really wished to demonstrate a 20% reduction, amuch higher number would be necessary. With 1842 patientsthe investigators just have 90% power to reject the null hy-pothesis of a zero difference (a ratio of 1), which is a quitedifferent target from demonstrating a 20% reduction.

Since there has been a review for this book in Statistics inMedicine (2004, 23, 3551–3552), I have concentrated on fea-tures that can be regarded complementary. As also mentionedthere, the examples and the problem sections are very educa-tive and enjoyable. Finally, the triage principle, as worked outin much detail, is very recommendable and should routinelybe employed in clinical studies.

Joachim RoehmelBerlin, Germany

BAILEY, R. A. Association Schemes—Designed Exper-iments, Algebra and Combinatorics. Cambridge Uni-versity Press, Cambridge, U.K., 2004. xviii + 387 pp.US$70.00/€63.90, ISBN 0-521-82446-X (hardcover).

The concept of association schemes was formally introducedby R. C. Bose and T. Shimamoto in 1952. These have sincebeen studied from different viewpoints by statisticians, com-binatorial design specialists, and group theorists. This booksuccessfully combines the many facets of association schemesin a unified manner. It nicely highlights that these schemesform the basic structure of many designed experiments.

The book comprises 13 chapters, including one on the pos-sible future of the subject and another on the history. Be-ginning with the basic definition of an association schemeand some examples of such schemes in chapter 1, the authorintroduces the Bose–Mesner algebra of association schemesin chapter 2. This algebra is fundamental in the study ofassociation schemes. The exposition is lucid and elaborate.Chapter 3 elaborates on various kinds of combinations of


association schemes, while chapter 4 deals with the analysisof incomplete block designs. In this chapter a general theoryis provided and includes the notion of efficiency factors, whichis central in the choice of a good design. Partially balanced in-complete block designs are studied in chapter 5, with empha-sis on cyclic and lattice designs. The reason for this choice ofemphasis is perhaps guided by the fact that these two classesof designs, apart from having nice structures, have been usedextensively in practice and continue to be used. Chapters 6and 7 deal with orthogonal block structure and their use inanalysis of designs with complex block structures. Chapters8–11 deal with certain advanced topics such as block designsbased on Abelian groups, partially ordered sets and posetblock structures, subschemes and quotient schemes of asso-ciation schemes, duals, etc. These topics form the buildingblocks of future research in this area.

The lucid style of writing makes the book interesting andeasy to read. There are many examples illustrating the theory.Another important feature of this book is the large number ofexercises appended to almost all the chapters. This feature isof great help to students and teachers alike. There are morethan 250 references at the end.

The book is intended for a mathematically mature audi-ence. Though the author claims that no prior knowledge ineither group theory or design of experiments is necessary tofollow the contents, I would think that some familiarity withthese topics would make the reader more comfortable than ifshe/he did not have any background in these.

The book is a valuable addition to the existing literatureand is certainly going to be very useful to statisticians in-volved in design of experiments as well as to combinatorial-ists. This reviewer is not too sure though whether it is goingto be attractive to all readers of Biometrics.

The layout of the book is excellent and I could not (yet)find any typographical errors. To the best of my knowledge, Ido not think there is any book available at the moment whichcovers so much ground around the central theme of associ-ation schemes. I have enjoyed reading the book and wouldrecommend it strongly.

Reference

Bose, R. C. and Shimamoto, T. (1952). Classification andanalysis of partially balanced incomplete block designswith two associate classes. Journal of the American Sta-tistical Association 47, 151–184.

Aloke DeyIndian Statistical Institute

New Delhi, India

DESU, M. M. and RAGHAVARAO, D. Nonparametric Sta-tistical Methods for Complete and Censored Data.Chapman & Hall/CRC, Boca Raton, Florida, 2003. xiv +367 pp. US$79.95/£59.99, ISBN 1-58488-319-7.

This is a very readable book authored by two leading re-searchers in nonparametric statistical methods. The book isintended to fill a gap between nonparametric statistical meth-ods for complete data and nonparametric statistical methodsfor censored data. Indeed, many of the available textbooks

in nonparametric methods leave a gap between the methodsfor complete data samples and the modification of these non-parametric methods in the presence of censoring.

This book is targeted as a one-semester junior or seniorcourse or as a first-year graduate student course. It is alsodescribed as a useful reference book for researchers who areanalyzing censored data, or complete data, and wish to utilizenonparametric methods.

The book is neither cookbook nor heavily laden in atheorem-proof format. It is clearly written and has an impres-sive topic coverage. The book is organized into seven chap-ters; each chapter covers methods for binary data followed bymethods for continuous responses. Following presentation ofmethods for complete data, the authors go on to cover the cen-sored data situation; in each case the censoring is the usualbiomedical right censoring. Chapters conclude with mathe-matical supplements to provide additional details required toderive results, and also include a supplement of computer pro-grams, primarily SAS, which can be utilized to implement thetechniques provided. The seven chapters are: Procedures fora Single Sample; Procedures for Two Independent Samples;Procedures for Paired Samples; Procedures for Several Inde-pendent Samples; Analysis of Blocked Designs; IndependenceCorrelation and Regression; and finally, Computer IntensiveMethods.

The problems at the end of each chapter are relatively fewin number, but do represent a mixture of theoretical exercisesas well as applications of the methodology.

The topic coverage is comprehensive, and what is partic-ularly valuable is the focus on the transition from completedata methods to censored data methods. Appropriate cen-sored data extensions of the sign test and rank/scores testsare given. The nonparametric test coverage includes testsfor location as well as scale. References to the literature areup to date.

The book will be a challenging read for most junior-seniorundergraduates. However, first-year graduate students with asolid theoretical background will gain a good understanding ofnonparametric methods for complete and right-censored data.There is also little doubt that the book will fill an importantneed as a reference book for nonparametric methods. Thebook will be a useful addition to texts available on these topicsand it will be especially helpful to those individuals seeking agrounding in nonparametric methods.

Robert F. WoolsonDepartment of Biostatistics, Bioinformatics and

EpidemiologyMedical University of South Carolina

Charleston, South Carolina, U.S.A.

HATCHER, L. Step-by-Step Basic Statistics Using SAS(Student Guide and Exercises). SAS Institute, Cary,North Carolina, 2003. viii + 346 pp. US$54.95, ISBN 1-59047-149-0.

Overall impression: The best and worst feature of this bookis the amount of detail describing how to create and run SASprograms and how to interpret SAS output. If the intendedaudience consists of students in high school or a two-year

Book Reviews 1133

college, who have never taken a statistics class, this bookwould be just the thing. It spells out every keystroke andmouse function necessary to create and run a SAS program.It also spends pages discussing elementary statistical conceptssuch as a mean and median. For students with some experi-ence in using a Windows operating system and a very basicknowledge of statistics, reading this book could quickly be-come quite tedious.

Looking at the other SAS books on the market, especiallythe books published in the Books by Users series, this bookis the only one targeted to teaching statistics on an entrylevel to students who have little or no computer experience.Even books such as “The Little SAS Book” (Delwiche andSlaughter, SAS Institute, Cary, North Carolina) do not gointo the detail displayed in this book. Other books on usingSAS for statistical analysis also do not start from such a basiclevel.

SAS output is thoroughly explained with the use of call outs(circled numbers on the listing) and a complete description ofevery term in the listing. The descriptions are very clear andeasy to read.

Since the only way to learn SAS programming is to writeSAS programs, a separate book of exercises is available as well.Again, this book is at a very basic level. It could be used alongwith the Student Guide, alone, or with another book on SASprogramming. The assignments are very detailed and concise.Using this book could save an instructor considerable time.Making up one’s own problem sets is a very time-consumingtask.

There are a few serious SAS programming errors in thebook. The most prevalent is the confusion of character andnumeric missing values. Quote–period–quote (’.’) does not re-sult in a SAS character missing value. Also statements such asIF AGE LT 25 THEN . . . is true when AGE is a missing value.This is a serious programming error that does not result inany messages to the SAS log.

A listing of the chapters is shown below:

Chapter 1. An introduction to SAS and the Student Guide.Chapter 2. A nice introduction to hypothesis testing and to

basic SAS concepts (definition of terms such as observation,variable).

Chapter 3. A step-by-step tutorial in running a program in awindowing environment (hence the book title). Perhaps toodetailed. It does not assume that the reader knows even themost basic windows functions such as saving a file. This isone of the reasons this book is almost 700 pages (and quiteheavy). Several pages are spent in telling the student howto insert or delete lines from the editor. This is probablyunnecessary, even for a student who is not well versed inusing computers.

Chapter 4. Creating SAS data sets from raw data and us-ing simple procedures such as PROC PRINT and PROCFREQ. I like the approach of using list input (blank de-limited data values) to introduce students to the INPUTstatement.

Chapter 5. A case study using a data set on political donationsand computing frequency distributions.

Chapter 6. Introduction to PROC CHART.Chapter 7. Basic measures of central tendency and variability.

Chapter 8. Modifying variables and creating new variablesfrom an existing SAS data set.

Chapter 9. Discussion of z-scores and standardization.Chapter 10. Bivariate analysis. A discussion of parametric

and nonparametric correlation. A useful discussion on theproblem associated with a type I error when correlating alarge number of variables is included.

Chapter 11. Bivariate regression is discussed using PROCREG.

Chapter 12. Single sample t-tests as discussed here. A separatechapter is devoted to two sample t-tests.

Chapter 13. Two sample t-tests for unpaired data.Chapter 14. Two sample t-tests with paired data. This chap-

ter includes the PAIRED option of PROC TTEST ratherthan the older method of computing difference scores andusing PROC MEANS (with the options t = and prt =).

Chapter 15. One-way ANOVA. A basic introduction toANOVA including LSMEANS for unbalanced designs.

Chapter 16. This chapter covers two-way ANOVA and con-tains a very nice explanation of significant interactions.

Chapter 17. Chi-square tests, including how to use SAS onfrequency counts rather than raw data.

The recommendation for using this book (along with theexercises) is strongly dependent on the audience level. Forstudents in high school, or a two-year or community college,with no prior statistical experience, this book would be ap-propriate (as long as the few technical errors were addressed).This book clearly fills a void in the SAS/statistics literature.

For four-year college students or graduate students, I feelthis book is too elementary and students will find it tedious.

Ronald CodyRobert Wood Johnson Medical School

UMDNJ School of Public HealthPiscataway, New Jersey, U.S.A.

WALLER, L. A. and GOTWAY, C. A. Applied SpatialStatistics for Public Health Data. Wiley, New York, 2004.xviii + 494 pp. €79.90/US$94.95, ISBN 0-47138-771-1.

Spatial statistical analysis has a long history and the lastdecade has seen numerous publications especially in the fieldof spatial epidemiology and disease mapping. Even so, I wasmissing a comprehensive textbook covering the diverse as-pects of applying spatial statistical methods to public healthdata. Lance Waller and Carol Gotway now give a very wel-come and thorough overview on applied spatial epidemiologi-cal data analysis. In this book, the authors combine an intro-duction to epidemiological research with an introduction toissues of geographical information systems (GIS) to discussthe application of spatial statistical methods along with thepresentation of appropriate methods.

Organization of the text differs from previous presentationsof spatial statistics, i.e., the distinction between methods fordifferent types of data (compare with Cressie, 1993). Thetext is structured according to particular questions of spa-tial epidemiologic interest similar to the one in Elliott et al.(2000), but expanding on that concept and thus resulting ina fresh approach to the topic. The reader gets answers to the


following questions: What are the key elements of epidemio-logic research (chapter 2)? What are, and how to manage spa-tial data (chapter 3)? How to map spatial data and commu-nicate results (chapter 4)? How to model spatial data (chap-ter 5)? How to test for spatial clustering and detect clus-ters of disease (chapters 6 and 7)? How to interpolate spatialexposure measurements (chapter 8)? How to quantify spa-tial associations between health outcomes and exposure data(chapter 9)?

The text gives clear explanations and is rich in all the littledetails encountered by years of active research of these twoauthors. For example, Waller and Gotway discuss the issue ofwhy maps of spatial data are different from any other statisti-cal plots. They also discuss issues related to data quality andits relation to map perception, especially in the cases whereGIS are used to produce high-quality maps based on unreli-able data. Furthermore, the text presents a fine comparisonand discussion of diverse modeling approaches, e.g., no lessthan seven modeling approaches to the classical Scottish lipcancer data are used. While most textbooks present only theBayesian (e.g., Banerjee et al., 2003) or likelihood approachto the modeling and analysis of GLMMs, here the reader findsa side-by-side introduction and discussion of both concepts asthey apply to spatial health data.

Some of the chapters end with a selection of exercises.A related homepage on the internet provides the reader ac-cess to example data and programming code using the R,WINBUGS, and SAS software packages. This homepage isstill under construction but several updates have appeared inthe first month of its publication.

The book is written to serve as a reference text for re-searchers and an introduction to those making their first stepsin the field of statistical analysis of spatially referenced publichealth data. The authors do not attempt to push the method-ological frontiers but to consolidate the notation, ideas, meth-ods, and techniques needed in daily, applied research work.In summary, I am confident that the book will be a success-ful addition to existing literature and foster the applicationof spatial statistical methods to topics in epidemiology andpublic health.

References

Banerjee, S., Carlin, B., and Gelfand, A. (2003). Hierarchi-cal Modelling and Analysis for Spatial Data. Boca Raton,Florida: Chapman & Hall/CRC.

Elliot, P., Wakefield, J. C., Best, N. G., and Briggs, D. J., eds.(2000). Spatial Epidemiology: Methods and Applications.Oxford: Oxford University Press.

Cressie, N. (1993). Statistics for Spatial Data, revised edition.New York: Wiley.

Olaf BerkeDepartment of Population Medicine

University of GuelphGuelph, Canada

andDepartment of Biometry, Epidemiology and

Information ProcessingSchool of Veterinary Medicine

Hannover, Germany

SILVAPULLE, M. J. and SEN, P. K. Constrained Sta-tistical Inference. Inequality, Order and Shape Re-strictions. Wiley, Chichester, U.K., 2004. xvii + 532 pp.€79.20/£52.95, ISBN 0-471-20827-2.

Professors Silvapulle and Sen are to be highly congratulatedon this valuable and timely addition to the literature on con-strained statistical inference. While most researchers will befamiliar with the classic works of Barlow et al. (1972) andRobertson, Wright, and Dykstra (1988), they may be sur-prised, as I was, by the wealth of new material related to con-strained statistical inference, which Silvapulle and Sen presentin this new book. Indeed, the true merit of this book can onlybe measured once the reader realizes how far the area of con-strained statistical inference has grown from its genesis ofone-sided tests and order constraints among the parametersin a one-way layout. As the authors explain in the preface andin their description of the book’s layout at the end of chap-ter 1, the theme of their book incorporates both parametricrestrictions as well as constraints on the data space, and alsoextends to semiparametric and nonparametric analysis.

As an impetus for their work, Silvapulle and Sen pointout that constrained statistical inference has been gaining at-tention steadily in the statistical literature over the past 20years, particularly in applied fields where the advantages ofthe constrained approach are appreciated. The authors men-tion clinical trials, bioassays, biomedical sciences, genetics,and bioinformatics as some of these areas, and one nice fea-ture of the book is that the first 21 pages contain an abun-dance of interesting and motivating examples from these ar-eas. The standard theory extending to generalized linear mod-els is presented in detail in the main part of the book togetherwith plenty of diagrams to aid the discussion, and the writ-ing is concise and easy to follow. Toward the end of the bookthere are also sections directed toward inferences on mono-tone and unimodal density functions, a whole chapter onBayesian perspectives, and a final chapter of miscellaneoustopics which illustrate the growth in research into constrainedstatistical inference problems that has occurred over the past20 years.

In the preface, the authors express the hope that their bookwill make the full scope of constrained statistical inferenceknown to upper undergraduate and graduate students. Thereare a reasonable number of problems included at the end ofeach chapter, which will add to its appeal as a textbook, al-though a fair amount of mathematical ability will be requiredof the students. It is also to be hoped that this book mayencourage the inclusion of some of the more basic constrainedstatistical inference methodologies in standard statistical soft-ware packages so that their use may become more widespread.In addition, it should be mentioned that a noteworthy as-set of the book is the inclusion of a 56-page bibliography atthe end, which in itself may be considered worth the pur-chase price. Almost all of the references are for the periodafter 1988, so that they are not included in Robertson et al.(1988).

In summary, this book is an invaluable resource for anyresearcher with interests in constrained problems, or with therelated theory, and since the authors have illustrated how thetheme of their book touches so many different areas, it is easy

Book Reviews 1135

to conclude that any statistical library would be incompletewithout it.

References

Barlow, R. E., Bartholomew, D. J., Bremner, J. M., andBrunk, H. D. (1972). Statistical Inference under OrderRestrictions. New York: Wiley.

Robertson, T., Wright, T., and Dykstra, R. L. (1988). OrderRestricted Statistical Inference. New York: Wiley.

Anthony HayterSchool of Industrial and Systems Engineering

Georgia Institute of TechnologyAtlanta, Georgia, U.S.A.

DUDEWICZ, E. J., GOLDEN, B. L., and GOVINDRA-JULU, Z. (eds) American Journal of Mathematical andManagement Sciences, Vol. 23 (3&4). American SciencesPress, Syracuse, New York, 2003. 197 pp. ISSN 0196-6324.

This title is Volume 50 in a book series entitled American Se-ries in Mathematical and Management Sciences. It coincideswith an issue of the American Journal of Mathematical andManagement Sciences (Volume 23, issue 3 and 4). This par-ticular volume is named Modern Mathematical, Managementand Statistical Sciences, III: Advances in Theory and Appli-cation.

As such there is not a general subject that connects thedifferent contributions. The reader will find selected paperson

� subset selection procedures,� standby system availability,� detection of outliers in multivariate data when the errors

are autocorrelated,� the use of the log-gamma distribution in reliability anal-

ysis of highly reliable systems,� nearest neighbor estimates of entropy,� confidence intervals for the ratios of ranked scale param-

eters for censored data,� transient analysis of a redundant system with additional

repairmen, and� a generalized integer gamma distribution.

Given the large spread in the subjects covered in this vol-ume, without any general theme behind it, it is hard to con-sider this edition as a book or monograph.

Jan BeirlantDepartment of Mathematics

Katholieke Universiteit LeuvenLeuven, Belgium

SMALL, C. G. and WANG, J. Numerical Methods forNonlinear Estimating Functions. Oxford University Press,Oxford, 2003. xii + 309 pp. £60.00, ISBN 0-19-850688-0.

Estimating equations is particularly useful for parameter es-timation where the likelihood cannot be fully specified or isintractable. When the equations are nonlinear, problems of-

ten arise in their numerical solution and the multiplicity oftheir roots, and this is the focus of much of the book.

After a brief introduction, chapter 2 reviews the basic con-cepts of estimating equations, and gives a glimpse of the widerange of techniques and applications. Chapter 3 gives a usefulsurvey of numerical algorithms and their merits for findingthe roots of equations.

Chapter 4 presents examples that illustrate some notoriousdifficulties of nonlinear equations, including mixture models,correlation coefficients with fixed marginal distribution, sta-ble laws, measurement error models, and an example wheremaximum likelihood estimation is inconsistent.

Chapter 5 is mainly concerned with selection from multi-ple roots. A modification of the Newton–Raphson algorithmensures that the iterative substitution can only converge to alocal maximum. An illustration of the nonexistence of rootsis given. Described as a Bayesian logistic regression model, itconcerns 24 binomial data whose proportions are specified viaa linear model in seven parameters on the logit link scale. TheBayesian prior assumes that the 24 proportions are indepen-dent, which seems most unrealistic in view of the dimensionof the linear model. Surely one should put the prior on theseven parameters? The chapter ends with 29 pages on boot-strap methods.

Where data are correlated, the quasi-score equations arenot conservative. Estimation in generalized linear models ismentioned in passing as having equations that are approx-imately conservative in that the expectation of the Hessianis symmetric. This is an important area of statistics whereonly the first two moments (not the likelihood) are known;I would have liked to see more detail and an example here.In such cases, there are no objective functions whose deriva-tives give the equations. The construction of artificial like-lihoods in chapter 6 attempts to deal with this; they assistin the numerical solutions, selecting roots and can providetools for inference. For example, the projection of the likeli-hood score onto the space of functions linear in the data givesthe quasi-likelihood. Projected likelihood ratios are developedand generalized, and there is a section on quadratic artifi-cial likelihoods. Quadratic functions with iteratively updatedweights provide another approach. Although such functionsproduce the desired estimating equations, the artificial likeli-hoods can be very different from the true likelihood. Neverthe-less, analogues of familiar inference, such as the Wald test, areavailable.

Chapter 7 explores the dynamical systems mentionedbriefly in chapters 5 and 6. Their connection with estimat-ing equations is delayed until section 7.4; I would have likedto see this explained earlier so that the approach would havebeen clearer. Dynamical systems give valuable insight intothe solution of equations that are nonconservative. Appropri-ate roots correspond to the points of attraction in the sys-tem, and are connected to a modification of the Newton–Raphson algorithm that finds such roots. Chapter 7 con-cludes with a digression into complex roots and Julia sets.It ends abruptly, leaving one wondering where the theorymight lead. I would have liked more data-driven exampleshere.

Bayesian estimating equations utilize the posterior ex-pectation and variance of the estimating functions. If the


functions are linear, the method is “robust” in the sense thatonly the first two moments of the prior for the parametersneed to be specified. Chapter 8 is too short and is not appliedto data.

This book is aimed at researchers and postgraduate read-ers. Many results are stated without proof, supplemented bya list of almost 200 references. This makes for a fluent style sothat the reader can rapidly progress from basic ideas to areasof recent research. At times the explanation is informal, butthe ideas come across clearly, illustrated by examples that arediscussed in detail. There are no further examples for read-ers to work through. Problems of solving nonlinear estimatingequations are often specific to the case in hand, so that onemust be prepared to choose between different techniques. Tothis end, the narrative is interspersed with much wisdom andadvice. A lot of material has been brought together that istouched upon too briefly. However, this whets the appetiteand will stimulate research in an area that is currently active.I thoroughly enjoyed this book, and recommend it to readersof Biometrics.

Richard MortonCSIRO Mathematical and Information Sciences

Canberra, Australia

PEPE, M. S. The Statistical Evaluation of Medical Testsfor Classification and Prediction. Oxford University Press,Oxford, 2003 (hardcover)/2004 (paperback). xvi + 302 pp.,£65.00 (hardcover)/£39.50 (paperback), ISBN 0-19-850984-7(hardcover), 0-19-856582-8 (paperback).

Over the last 30 years, health care institutions and agen-cies have increasingly demanded quantitative evaluationsof the performance of the ever more sophisticated—andmore costly—devices used in diagnostic medicine. This hasprompted a greater emphasis on rigorous study designs to gen-erate appropriate performance data, and more sophisticatedstatistical methods to analyze them. The growth is evident inthe rate of publication of new methods in biostatistics jour-nals, the availability of modern software, and methodologicalsessions at biostatistical conferences. This coming of age isfurther illustrated by the publication of two textbooks, one in2002 by Zhou, Obuchowski, and McClish and in 2003 the oneby Pepe, reviewed here.

Pepe’s interest in this area began just 10 years ago; sincethen she has published several methodological papers, no-tably in Biometrika, this journal, Biostatistics, and Statisticsin Medicine. Her hopes are that her textbook will “stimulatefurther development of statistical methods and place themfirmly within the realm of mainstream biostatistical research,”where she wishes these methods will be developed “with thesame rigor as (. . .) those in therapeutic and epidemiologic re-search.”

As per the author’s website (http://fhcrc.org/labs/pepe/book), this book describes statistical concepts and tech-niques for evaluating medical diagnostic tests and biomark-ers for detecting disease. Measures for quantifying test accu-racy are described including sensitivity, specificity, predictive

values, diagnostic likelihood ratios, and the receiver operat-ing characteristic (ROC) curve that is commonly used forcontinuous and ordinal valued tests. Statistical proceduresare presented for estimating and comparing them. Regressionframeworks for assessing factors that influence test accuracyand for comparing tests while adjusting for such factors arepresented.

The book benefits from the author’s strong methodologicalwork in statistical modeling in allied areas, such as in survivalanalysis, longitudinal data, and incomplete data.

The focus is on tests that yield a numerical value, but testsyielding binary and ordinal results are covered too. To pro-vide a unified approach, regression is introduced early on.Also central are “placement values,” a concept she considersto be “fundamental to ROC analysis.” These generalizationsof U-statistics were exploited in the ROC context by Delongin 1988; in a subsequent radiology article, they were given amore descriptive name. Delong used them to provide pointand interval estimates of the area under a ROC curve of un-specified shape. Pepe has now taken them much further, usingthem to fit the entire curve, and has extended their use furthervia generalized regression models.

One is impressed by just how recent the work in the text-book is, and how much of it is by her and her students. A fullone fourth of the 250 articles referenced were published since1999, one half since 1996, and three fourths since 1989.

Of these two recent books, which is more suited to whomand to what? While they overlap in the topics covered, thetwo differ in their emphasis and their audience. The multi-author text includes “clinicians who conduct diagnostic stud-ies, statisticians who either analyze data from such studies orconduct statistical research in diagnostic medicine.” Pepe, bycontrast, tells us that “most of her book is written for thepractising statistician (. . .) with some chapters aimed specif-ically at academic research biostatisticians.” Thus, one canunderstand why the title of her book uses the more genericstatistical terms “classification and prediction” whereas theother, aimed more at clinicians, uses the term “diagnosis.”Unlike the “modern scientific physician,” Pepe—presumablybecause she considers that there is no loss of statisticalgenerality—treats prognosis as a “special type of diagnosis,”and screening for disease as another special case.

The two texts also differ in the areas of application. Withone co-author cross-appointed in a department of radiology,the multi-author text gives greater attention to imaging tests,and to the attendant special logistical and analytical issues,such as multiple readers, multiple loci of disease, variations inreader thresholds and intrinsic accuracy, etc. Pepe, acknowl-edging that her emphasis on certain topics is “colored by herexperience with applications, and with her research interests,”gives greater attention to tests yielding numerical results, tothe unity of the statistical methods, and in particular to thebenefits of a regression approach. Naturally, the one-authortext also has more continuity of content and style.

The book is typeset using LateX, and easy on the eye. Thewebsite provides access to the data sets and Stata programsused in the text.

This textbook, from a “thoroughly modern statistician,”succeeds in its stated aims and is a good starting place for

Book Reviews 1137

statisticians wishing to become familiar with, and wishing toadvance, this subdomain of biostatistical methods.

James A. HanleyMcGill UniversityMontreal, Canada

FARAWAY, J. J. Linear Models with R. Chapman &Hall/CRC, Boca Raton, Florida, 2005. x + 229 pp. US$69.95,ISBN 1-58488-425-8.

Linear Models with R uses the open-source software R to teachregression analysis techniques. The book is aimed at peoplewho “want to focus on the practice of regression and analy-sis of variance.” As the preface states, the reader needs basicknowledge in R, in data analysis, in matrix algebra, and instatistical inference. In fact, there is a quick introduction toR in the appendix, but it would not help much for a begin-ner to become acquainted with the programming language.The book is very comprehensibly written and can thereforebe recommended for beginners in linear models. It is clearlyand simply explained how to use R and which packages arenecessary to analyze linear models.

The book comprises 16 chapters that can be organized intothree sections. The first section covers the fundamental con-cepts of linear models including a general introduction, esti-mation, and inference.

The second section discusses model diagnostics, model im-provement, and model uncertainty. These topics are seldomdescribed in such detail (they cover about 50% of the book).Besides checking for model assumptions and model structure,the book presents methods to identify and remedy problemswith the error or predictor, different transformation tech-niques, and shrinkage methods. This part of the book alsogives a review on testing- and criterion-based model selec-tion strategies and provides solutions to basic missing dataproblems.

The third section focuses on different designs such as anal-ysis of covariance, one-way analysis of variance (only fixed-effects), factorial design, and block design. It covers standardmethodology including diagnostics and various contrast cod-ing schemes, and has also incorporated solutions for multipletesting problems. Throughout the book, the author stressesthe importance of thinking critically about one’s model.

In books like the present one where computer software andits applications are emphasized, theoretical parts are often re-duced to a listing of formulas or, even worse, theory is not ex-plained at all. This is not the case here. The author’s objectiveis to “take a wider view of statistical theory.” In my opinionhe managed this excellently. The book concentrates on the es-sential formulas and places more emphasis on the statisticalconcept beyond it. This provides the reader with an adequatetheoretical background and helps to incorporate theory into amore general setting. For one problem, the author introducesvarious methods and clarifies in which situation they can beapplied.

The computational data analysis parts demonstrate howtheory can be realized in practice, what conclusions can bedrawn from the output, and furthermore, how the initialmodel can be improved. This is supported by the example

data sets. They are well chosen to illustrate the substantialproblems behind the data.

If one likes to follow the data examples by typing the com-mands into R, the R package faraway will be necessary, whichincludes additional functions and the data sets used in thisbook. Especially for lecturers it may be helpful that eachchapter provides additional exercises.

Nevertheless, since theory is captured in a compact mannerand the chapters give none or just a few references, I misseda more extensive bibliography at the end of each chapter forfurther reading. Moreover, the layout of the book could beimproved, i.e., by stronger highlighting of keywords. Thereare serious printing errors in chapter 15, a few typographicalerrors, some misplaced page breaks, and an incorrect graphic.Errors as well as software modifications that occur by newreleases of R are listed in the errata that can be downloadedfrom the book’s webpage.

All in all, this book is recommendable as a textbook forcomputational linear regression courses and therefore for stu-dents and lecturers, but also for applied statisticians who wantto get started on regression analysis using the software R.

Ronja ForaitaBremen Institute for Prevention Research

and Social Medicine (BIPS)University of Bremen

Bremen, Germany

HEIBERGER, R. M. and HOLLAND, B. Statistical Anal-ysis and Data Display. An Intermediate Course withExamples in S-PLUS, R, and SAS. Springer, New York,2004. xxiv + 729 pp. US$79.95, ISBN 0-387-40270-5.

It has been difficult for instructors to choose a textbook fora year-long Master’s class in Statistical Methods. Quite of-ten, people choose four to five textbooks on different top-ics. However, different approaches, as well as different nota-tions, are used in different books, hence the lack of consistencywhich causes students to become confused. Traditional books,such as Statistical Methods, written by Snedecor and Cochran,which covered a broad range of topics and was widely used inthe 1970s, will not satisfy today’s needs because of many ad-vances in computing made over the past decades which offerenhanced graphic capability for exploring data and high-speedcomputing capability for summarizing data.

This book, authored by Heiberger and Holland, covers ma-terial on the topics of Analysis of Variance, Regression, Exper-imental Designs, Contingency Tables, Nonparametrics, Logis-tic Regression, and Time Series. Computer software packagesS-Plus, R, and SAS are used for graphic displays and dataanalysis. The authors present the instructions to constructthe graphs as well as the interpretations of the graphs. Com-puter codes are given and explained in the examples. Com-puter outputs are provided to explain the data analysis andto make conclusions.

There are a total of 18 chapters in this book.Chapter 1 presents the general concepts of statistics.

Practical examples are presented to illustrate how statisticsare used. Chapters 2–5 are the fundamentals in statistics,which include data presentations, sampling techniques, basic


graphical displays, estimations, hypothesis testing, and modelchecking techniques. Topics of experimental designs and anal-ysis of variance are discussed in chapters 6, 7, 12, 13, and14. They include completely randomized single-factor experi-ments in chapter 6 and multiple-comparisons procedures inchapter 7. Multifactor experiments are discussed in chap-ters 12–14, which include designs such as randomized com-plete block designs, Latin square designs, factorial designs,nested designs, split-plot designs, fractional factorial designs,and crossover designs. Following each design model is anexample with a complete data analysis. Graphical displaysand outputs from S-Plus and SAS are also included in eachexample.

In chapters 8–11, simple and multiple regression modelsare discussed. Basic elements of regression such as estimationmethods (least squares and maximum likelihood), confidenceand prediction intervals, diagnostics, data transformations,polynomial regression models, the use of indicator variables,and search procedures for explanatory variables reduction areall covered. Many examples are given with detailed analy-sis, including graphical displays and computer commands andoutputs. Contingency tables are discussed in chapter 15. Ba-sic topics such as chi-square analysis, Fisher’s exact test for2 × 2 tables, Simpson’s paradox, and the Mantel–Haenszeltest are discussed. This chapter is somewhat short and pro-vides only a brief discussion of two-way contingency tables.The chapter can be extended to multiway tables and to in-clude discussions of commonly used models such as logit mod-els, log-linear models, and multinomial response models. Non-parametrics are briefly discussed in chapter 16. Basic nonpara-metric tests such as the sign test, Wilcoxon signed-ranks test,

Mann–Whitney test, and Kruskal–Wallis test are provided inthe chapter. Logistic regression model is discussed in chap-ter 17. Many examples are provided, along with computercodes, outputs, and graphic displays. A discussion of time-series analysis is provided in chapter 18. Basic ideas of au-toregression (AR), moving average (MA), and autoregressiveintegrated moving average (ARIMA) models are discussed.The factor of seasonality is also discussed. An example ofmonthly CO2 data is analyzed in detail. The example, alongwith graphical displays and computer outputs, is used to illus-trate model identification, parameter estimation, diagnosticchecking, and forecasting.

Throughout the book, statistical theory is kept to a min-imum, and statistical concepts and methods are well stated,without lengthy derivations of formulas, but with the aidof graphical displays. Many examples are provided to il-lustrate the use of statistical methods. Exercise problemsare well written and will be good practice for readers. Itcomes complete with an online resource containing data sets,and sample code in S-Plus, R, and SAS for all examples.The coverage of topics in this book is broad. This bookcan serve as a stand-alone textbook in the subject of ap-plied statistics for statistics majors at the Master’s level,and for statistics-related interdisciplinary majors at the Ph.D.level. The book can also serve as a reference book for otherresearchers.

John S. J. HsuDepartment of Statistics and Applied Probability

University of CaliforniaSanta Barbara, California, U.S.A.

BRIEF REPORTS BY THE EDITOR

LEHMANN, E. L. and ROMANO, J. P. Testing Statisti-cal Hypotheses, 3rd edition. Springer Science + BusinessMedia, New York, 2005. xiv + 784 pp. US$89.95, ISBN 0-387-98864-5 (hardcover).

The former editions of this book and the companion vol-ume on point estimation were my favorite sources for sta-tistical testing and estimation theory when I studied statis-tics and during the preparation of my PhD thesis. Lateron I used these books to prepare my lectures on statisticaltheory.

Nearly 20 years after the second edition the revised thirdedition consists of two parts focusing on small-sample the-ory (Part I) and large-sample theory (Part II). The majorchanges to the second edition result from a new chapter (chap-ter 13) on asymptotic optimality that fills a gap left in thesecond edition and parallels this topic to the book on pointestimation. In addition, large-sample tools provided in chap-ters 11 and 12 together with chapter 13 make a more com-prehensive treatment of goodness-of-fit tests possible such asthe Kolmogorov–Smirnov test and Pearson’s χ2 test (chap-ter 14). They are also used in chapter 15 to introduce permu-tation and randomization tests, large-sample approximations,and the bootstrap. The second major addition can be found

in chapter 9, where multiple testing is treated in more de-tail including a discussion on optimality. Here, an even morecomprehensive treatment would have been desirable. In to-tal, the book comprises 15 chapters, an appendix giving somebasic theory as, e.g., on convergence of functions or on domi-nated families of distributions, and an exhaustive list of refer-ences. The 10 chapters of Part I introduce the general decisionproblem (chapter 1) and the probability background (chap-ter 2). Uniformly most powerful tests are treated in chapter 3,whereas chapters 4–6 focus on unbiasedness and invariance.Chapter 7 deals with linear hypotheses. The minimax prin-ciple is introduced in chapter 8 and, as already mentioned,chapter 9 considers multiple testing. Conditional inference istreated in chapter 10. The five chapters of Part II are devotedto basic large-sample theory (chapter 11), quadratic meandifferentiable families (chapter 12), large-sample optimality(chapter 13), goodness-of-fit tests (chapter 14), and generallarge-sample methods (chapter 15). Each chapter ends withnumerous problems that have already been indicated in thetext and some complementary notes on the topic covered inthe chapter.

What I like a great deal about this book is its illus-trative language and the numerous examples that make iteasier to understand the complex matter presented. The

Book Reviews 1139

comprehensible notation and the excellent structure furtheradd to the readability of this book.

EDLER, L. and KITSOS, C. P. (eds) Recent Advances inQuantitative Methods in Cancer and Human HealthRisk Assessment. John Wiley & Sons, Chichester, U.K.,2005. xxviii + 463 pp. €105.00/£70.00, ISBN 0-470-85756-0(hardcover).

This edited volume is devoted to new developments in can-cer research and human risk assessment where, as the edi-tors themselves state in the preface, “without hiding (their)our common origin in mathematical sciences, (their) our ap-proach to risk assessment has been undeniably influencedby the desire to quantify risks and thus to use mathemat-ical and statistical methods.” It is, therefore, not surprisingthat the book emphasizes mathematical, statistical, and com-putational methods for exposure assessment, hazard identifi-cation, dose-response modeling, and hazard characterization.Although quantitative methods play the major role, the bookalso consists of the fact that new biological data and principleshave to be accounted for, such as genomic data and the notionof biomarkers. For building the bridge between new biologicaland medical concepts on the one hand and new biostatisticalmethods on the other hand, the book covers six major areasin a total of 25 chapters. Each of these parts is briefly intro-duced by the editors who give some guidance and additionalinformation on the topic of the corresponding part. These in-troductions also bring the different parts together and helpthe reader follow the purpose of the book and its structure.

Part I on cancer and human health risk assessment onlyconsists of one chapter introducing the principles of cancerrisk assessment, i.e., the risk assessment paradigm. Part IIemphasizes the biological aspects of carcinogenesis. The fivechapters of this part deal among other, topics with molecu-lar epidemiology in cancer research, genetic polymorphismsin metabolizing enzymes as a risk factor for lung cancer,and with theory and models of biological carcinogenesis andmultistage carcinogenesis. A bibliographic review on risk as-sessment and chemical and radiation hormesis completes thispart. The five chapters of Part III are devoted to modelingfor cancer risk assessment where among other topics stochas-tic carcinogenesis models and models for cancer screening arediscussed. Part IV deals with frequentist and Bayesian sta-tistical approaches in the field of carcinogenesis studies. Thefive chapters cover survival analysis with nonproportional haz-ards, dose-response modeling, a benchmark dose approach, aBayesian approach for uncertainty analysis, and optimal de-signs for bioassays. Three chapters in Part V then discuss spe-cific modeling approaches for health risk assessment dealingamong other topics with models for mixtures. Part VI bringstogether theory and practice by introducing six case studieswhere each needs sophisticated approaches for its analysis.The studies consider molecular data, clinical data on lungfunction, the neurophysiological system, and data on child-hood leukemia, melanoma, and thyroid cancer.

The book can be highly recommended not only to all re-searchers in various fields interested in risk assessment, butalso to lecturers in biomathematics and biostatistics.

RAO, C. R., WEGMAN, E. J., and SOLKA, J. L. (eds)Handbook of Statistics: Data Mining and Data Vi-sualization. Elsevier, Amsterdam, 2005. xiv + 643 pp.US$204/£128, ISBN 0-444-51141-5 (hardcover).

This edited volume focuses on methods to handle large andnonstandard statistical data including text data, internet traf-fic data, and geographic data. The 17 chapters written byleading experts cover a broad range of approaches for datamining and data visualization that can be roughly dividedinto three major parts. The first part consists of six chaptersand presents techniques for data mining both from a statis-tical and a more computer science–based perspective. Theseinclude for instance an overview of data-mining strategies,data-mining techniques based on machine learning emphasiz-ing computational intelligence and knowledge mining, meth-ods for mining computer security data with the crucial aspectof cyber security, and text mining. The second part containsseven chapters and focuses on statistical methods addressingamong other topics dimension reduction, pattern recognition,multivariate density estimation, multivariate outliers, classi-fication and regression trees, and genetic algorithms. The lastpart, consisting of four chapters, is devoted to data visualiza-tion where grand tour methods, newest approaches for show-ing statistical summaries, interactive statistical graphics, andmethods for data visualization with focus on virtual realityare presented. Each chapter contains a large number of illus-trative figures supplemented by numerous color figures givenin an appendix, which even further enhance the understand-ing of the presented complex techniques.

This book offers a comprehensive and exciting source for“data centric statisticians” in contrast to “methodology cen-tric statisticians” and for those curious to learn about tech-niques that enable the researcher to analyze data that do notsatisfy the classical assumptions of, e.g., independence, iden-tical distribution, and stationarity.

BERGER, V. W. Selection Bias and Covariate Imbal-ances in Randomized Clinical Trials. John Wiley & Sons,Chichester, U.K., 2005. xii + 205 pp. €94,50/£45.00, ISBN0-470-86362-5 (hardcover).

This book is not concerned with the type of selection biasthat might impact the external validity of a clinical trial butwith a selection bias that makes the internal validity ques-tionable despite a proper randomization. That means “thateven in a properly randomized trial confounding can be in-duced to create a covariance imbalance that leads to a type ofselection bias that can comprise internal validity.” The bookfocuses on the practical issues and implications of selectionbias without dwelling into mathematical derivations. For thispurpose, the book is divided into two parts. Part I consist-ing of four chapters asks whether there is a problem withreliability in medical studies. Chapter 1 presents various pos-sible study designs for clinical trials in a hierarchical orderwhere the next design presented eliminates some of the prob-lems related to the former. Chapter 2, which is recommendedto all readers, discusses susceptibility of randomized trials tosubversion and selection bias. This chapter is important for


following the methods introduced in subsequent chapters formanaging selection bias. Chapter 3 presents a large number ofreal clinical trials where selection bias has occurred. Chapter4 deals with possible impacts of this bias on the results of arandomized clinical trial. After having described the problemof selection bias in principle, Part II is devoted to actions tobe taken to improve the reliability of medical studies. Chapter5 deals with techniques to prevent selection bias while chap-ter 6 introduces techniques to detect selection bias where theBerger–Exner test based on the reverse propensity score isemphasized. Its application is illustrated in a real data exam-ple on lung carcinoma. Chapter 7 provides careful methodsfor correcting for selection bias if it is present. Putting all theaspects together addressed in the former chapters leads to aset of recommendations given in chapter 8 that can be usedto manage selection bias in randomized clinical trials.

In the preface, the author gives some guidance to workthrough the book depending on the interest of each reader,e.g., a reader writing a protocol should read chapter 5 rightafter chapter 2, while a reader reviewing clinical trials mightprefer chapter 6 instead of chapter 5. In my opinion, the bookis a valuable help in understanding that a randomized clinicaltrial might fail and why it may fail. Thus, the book is to berecommended to all practitioners and researchers in the fieldof clinical trials although I found it at least partly not easyto read.

DMITRIENKO, A., MOLENBERGHS, G., CHUANG-STEIN, C., and OFFEN, W. (eds) Analysis of Clinical Tri-als Using SAS: A Practical Guide. SAS Institute, Cary,North Carolina, 2005. vii + 420 pp. $69.95/€65.50, ISBN 1-59047-504-6.

In the context of clinical trials, the book focuses on the anal-ysis of stratified data, multiple inferences, incomplete data,and issues arising in safety and efficacy monitoring. Addition-ally other statistical problems addressed encompass referenceintervals for safety and diagnostic measurements. Each chap-ter briefly discusses the theory and gives practical solutionsto problems arising in practice in the form of SAS programsthat are also available at a website for download. Referencesto more extensive literature dealing with the theory of therespective topic are given. Apart from well-known proceduresgiven in the SAS/STAT package, three partly experimentalprocedures of SAS/STAT 8.2 are used. PROC MI and PROCMIANALYZE (both available in SAS/STAT 8.2 or higher) al-lows the possibility of multiple imputations for missing data.PROG TPHREG (introduced in SAS/STAT 9.0) is a moreconvenient version of PROC PHREG. SAS macros are usedquite extensively throughout the book.

The book is clearly addressing the advanced SAS user whodeals with the analysis of clinical trials. It is not so much anintroduction to the topic for beginners nor does it cover the

whole spectrum of methods used in clinical trials but ratherdiscusses current developments and sophisticated statisticalmethods in a very intelligent and enthralling manner. Theuse of real data for the examples and the choice of authorsand editors ensure the practical relevance of the presentedmaterial.

SAXTON, A. M. (ed) Genetic Analysis of Complex TraitsUsing SAS. SAS Institute, Cary, North Carolina, 2004.xii + 292 pp. $49.95/€46.90, ISBN 1-59047-507-0.

This very well structured book deals with quantitative ge-netics and its implementation into SAS. It is divided intotwo parts: Part 1, comprising chapters 2–7, covers classicalquantitative genetics, and Part 2, in chapters 8–11, is de-voted to molecular genetics. Among the topics covered inthe classical genetics parts are: estimation of genetic param-eters, such as variances, correlations, heritability, inbreeding,crossbreeding, etc., genetic selection, gene–environment inter-action, growth and lactation curves, and empirical Bayes ap-proaches to mixed model inference. The molecular geneticspart deals with gene frequencies and linkage disequilibrium,the extended sib-pair method for mapping quantitative traitloci, Bayesian mapping methodology, and analysis of gene ex-pression profiles on two-colored microarrays. Most of the pro-cedures used in the book can be found in the SAS/STAT pack-age, including the procedures PROC MIXED, PROC REG,PROC NLIN, PROC GLM, and PROC INBREED; Apartfrom classical SAS, SAS macros are used. A few applicationsrequire SAS/ETS or SAS/IML.

A large selection of example programs is given and canbe downloaded from a free website. Thus, the reader canprofit directly by using these programs and adapting themto his/her needs. All of the respective genetic theory is brieflyreviewed in the book and references are given for additionalreadings. This book is extremely helpful for anyone dealingwith the analysis of genetic data in complex traits. However,basic SAS knowledge should be present.

RABE-HASKETH, S. and EVERITT, B. A Handbook ofStatistical Analyses Using Stata, 3rd edition. Chapman& Hall/CRC, Boca Raton, Florida, 2003. xiii + 308 pp.US$49.95/£29.99, ISBN 1-58488-404-5 (paperback).

The first two editions of this book, describing features of StataVersions 5 and 6, were reviewed in the June 2000 and March2001 issues of Biometrics. The third edition describes featuresof Stata Version 8. The present volume has 100 additionalpages over its predecessors, owing to the inclusion of new ma-terial on random effects models, generalized estimating equa-tions, and a new chapter on cluster analysis. New and helpfulis the inclusion of graphs, illustrating Stata’s improved graph-ics features.

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