2610 BOOK REVIEWS

3. META-ANALYSIS IN MEDICINE AND HEALTH POLICY.Dalene K. Stangl and Donald A. Berry (eds), Mar-cel Dekker, New York, 2000. No. of pages: 398.Price: $155.00. ISBN 0-8247-9030-8

The use of meta-analysis to combine informa-tion across sources in medicine continues toincrease. The editors recognize the important‘paradigm shift’ occurring over the last decadeas the focus has moved from �tting a com-mon estimate of e�ect to estimating the extentand investigating sources of variability amongstudies. Substantial emphasis is placed on thisissue of heterogeneity throughout much of thebook.The book is a collection of 15 individual papers

by di�erent authors. The alphabetical ordering ofthe chapters in the book is not followed in thiscontent review which aims to draw together papersaddressing similar issues. Chapter 13 reviews thepurposes of meta-analysis and usefully summa-rizes the important methodologic features to bearin mind when undertaking such analyses. Chapter 1gives a clear introduction to �xed- and random-e�ect models and to the frequentist and Bayesianperspectives, and provides an excellent source ofreferences for Bayesian meta-analyses. Chapters 3,4, 6, 8, 9, 10 and 11 discuss the issue of heterogene-ity in the context ofmodel uncertainty and selectionin di�erent examples. Chapter 3 examines the ef-�cacy of mammography screening by comparingthree models with increasing generalization re-garding heterogeneity. Chapter 4 presents an inter-esting example of heterogeneity in a meta-analysisof three mega-trials. Useful exploratory graphi-cal analyses for model selection are described inChapter 6 and applied to a meta-analysis of stud-ies assessing the e�ect of nicotine-replacementtherapy on smoking cessation. Chapter 8 consid-ers heterogeneity across epidemiological studiesin the e�ect of duration of oestrogen exposureon the incidence of endometrial cancer. Chapter9 illustrates the importance of examining re-sults for a range of plausible prior distributionsfor the between-study variance and proposes adefault half-normal prior for this parameter. Di-agnostics for examining the normality assumptionof study e�ects are also presented. A hierarchi-cal two-compartment model is �tted to individ-ual patient data from pharmacokinetic studiesin Chapter 10 to incorporate within-individual,between-individual and between-study variabil-ity. Chapter 11 presents several Cox proportionalhazards models that allow for random-e�ect meta-analyses of individual patient survival data. The

practical problem of heterogeneity of reporting isaddressed for change over baseline outcomes inChapter 2 and mixtures of continuous and binaryoutcomes in Chapter 5. This latter chapter con-siders a latent-variables approach but the examplepresented also involves the indirect comparisonof treatments. This problem of indirect compar-isons is also considered in Chapter 7 where a�xed-e�ect model is �tted to some hypotheticaldata. In Chapter 12 the authors apply a method toaccount for potential publication bias allowing forsuch bias to depend on the quality of the study.Chapter 14 describes the approach taken by theInstitute of Medicine of the National Academy ofSciences in its review of two ‘politically charged’policy issues, illustrating the di�culties of inter-preting evidence. Chapter 15 provides an infor-mative review of available software, includingidenti�cation of unique features of the packagesexamined.Almost all of the analyses presented are (fully)

Bayesian, allowing uncertainty in several modelparameters to be incorporated. Most authors em-ployed di�use prior distributions although datapriors were used in three examples. Severalauthors assessed sensitivity across a range ofprior distributions. In three chapters, the authorsalso presented the results from the classical ap-proach for comparison. Two chapters describeresults from a classical rather than Bayesianperspective.Generally the papers are well written. A strength

of this book is that nine chapters provide the datafrom the real examples used to illustrate the meth-ods. The examples cover a range of study de-signs, namely randomized controlled trials, epi-demiological and pharmacokinetic studies, and in-clude binary, continuous and time-to-event out-comes. One criticism is that in some chapters theclinical interpretation is minimal. A greater empha-sis on publication bias might also have been ex-pected given its threat to the validity of any meta-analysis.As the editors suggest, this book will be useful

for applied statisticians. The reader is assumed tohave taken an ‘undergraduate course in statisticaltheory and methods’, which is implicitly assumedto have included Bayesian inference. General fa-miliarity with methods for parameter estimation ina Bayesian analysis, in particular Gibbs sampling,is assumed, although the emphasis of the book ison application and interpretation and less so onimplementation.The knowledge assumed by the book limits

its accessibility but several chapters provide ex-

Copyright ? 2002 John Wiley & Sons, Ltd. Statist. Med. 2002; 21:2607–2612

BOOK REVIEWS 2611

planations to help the statistician put across theideas to those without a statistical background.The book will also be a useful reference forpostgraduate students and provides a number ofexamples which could form the basis for a studentproject.

PAULA WILLIAMSONDivision of Statistics and Operational Research

Department of Mathematical SciencesUniversity of Liverpool

Liverpool L69 3BX, U.K.(DOI: 10.1002/sim.1071)

4. FUNDAMENTALS OF MODERN STATISTICAL METH-ODS. Rand R. Wilcox, Springer-Verlag, New York,2001. No. of pages: xiii+258. Price: $ 49.95. ISBN0-387-95157-1

This book provides a comprehensive view of mod-ern basic statistical methods aimed at overcom-ing practical problems arising when assumptionsunderlying conventional methods, foremost nor-mality, are violated. These ‘robust’ techniques aretypically not dealt with in standard courses orbooks on basic statistics and their use is still verylimited. The goal of the book is to bridge thegap between state-of-the-art in the developmentof these techniques and application in applied re-search. To this purpose, part I (chapters 2–7) of thebook highlights how and why standard methodsmay be misleading and provides a framework forintuitively understanding the practical advantagesof modern techniques, while part II (chapters 8–12) describes most basic methods and explains thestrategies they use.Chapter 1 gives a brief historical overview of

basic theoretical foundations of statistics as theywere developed during the last three centuries.Chapter 2 introduces the reader to practical prob-lems that might arise in basic statistics (mea-sures of location, dispersion and linear regres-sion) when standard techniques are used. Chap-ter 3 deals with issues and methods related to out-liers detection and with the practical implicationsof the central limit theorem. In chapter 4 the ac-curacy of the sample mean and median in the esti-mation of the population mean are compared anda non-technical description of the Gauss–Markovtheorem and of Laplace’s strategy for comput-ing a con�dence interval is given. The reader isalso introduced to practical implications, in termsof serious loss of accuracy, of violating the ho-moscedasticity assumption in the estimation ofthe linear regression slope. Chapter 5 focuses onthe factors that a�ect power in hypothesis test-ing and on practical problems arising with theStudent’s T -test when the underlying assumptions

(normality, homoscedasticity) are violated. Inchapter 6 the percentile bootstrap method is pre-sented and its practical value when making infer-ences about the slope of a regression line is shown.Chapter 7 shows the devastating implications ofthe so-called ‘contaminated normal distributions’in terms of dramatic reduction of power in conven-tional inferential methods about means, regressionslope and Pearson’s correlation coe�cient.In chapter 8, two robust estimators of loca-

tion are introduced: a trimmed mean and an M-estimator. The application of these estimators intesting hypothesis and computing con�dence in-tervals is dealt with in chapter 9 and their relativemerits in terms of bias, control over type I error andpower compared to the Student’s T -test are dis-cussed. Comparisons between groups are restrictedto the two-sample case and bibliographic notes onmore complex experimental designs are given atthe end of the book. Chapter 10 illustrates the Win-sorized correlation, Spearman’s rho and Kendall’stau approaches to reduce the e�ect of outliers in de-tecting associations between two variables. Globalmeasures of associations for the detection of out-liers are also proposed. Robust methods for linearregression are dealt with in chapter 11. Finally,chapter 12 focuses on contrasting robust methodsfor comparing two groups with ranked-based non-parametric methods.The book is clearly written and main theoret-

ical concepts are made easily accessible thanksto a great didactic e�ort. Technical details andmathematics are kept at minimum, while graphicalexplanations and examples are frequently given.A useful summary of key points is provided at theend of each chapter. Classical inferential methodsare refreshed, placing them within the historicaldevelopment of statistical theory, thus contributingto making reading more interesting and pleasant.All these features make the book well-tailored forthe targeted user, namely the applied researcherhaving a standard training in statistics.The major merit of the book is to open the

mind of the reader about potential problems, in

Copyright ? 2002 John Wiley & Sons, Ltd. Statist. Med. 2002; 21:2607–2612