STATISTICS IN MEDICINEStatist. Med. 2004; 23:2151–2153

BOOKREVIEWSEditor: PetraMacaskill

1. Annette J. Dobson, An Introduction to Generalized Linear Models.

2. John Crowley (ed.), Statistics in Clinical Oncology.

1. AN INTRODUCTION TO GENERALIZED LINEARMODELS. 2nd edn. Annette J. Dobson, Chapman &Hall/CRC, London, 2002. No. of pages: IX+225.Price: $49.95. ISBN 1-58488-165-8

The second edition of this well-known text AnIntroduction to Generalized Linear Models byAnnette Dobson improves upon the already well-written and comprehensive overview of general-ized linear models presented in the �rst edition(1990). The updated text seeks to, and is suc-cessful in, �lling a void in the otherwise sparseliterature on the subject of generalized linear mod-els at the introductory level. The text is especiallydesigned for a reader unfamiliar with the subjectof linear and generalized linear models who hassome statistical background and understanding ofmatrix algebra, although my impression is that areader with only a minimal grasp of mathematicscould still bene�t to a lesser degree from readingthis text.The scope of the book, rearranged somewhat

from the �rst edition, covers topics includingexplanatory chapters on the exponential family,general model �tting, estimation and inference.Separate chapters are then devoted to di�erentgeneralized linear models, such as the normallinear model, logistic regression and Poisson re-gression, all very commonly seen generalizedlinear models. These chapters cover a fairly largenumber of topics very brie�y. The second editionhas expanded to incorporate chapters on rapidlydeveloping topics, including a very well-writtenchapter on survival analysis. A less clearly writtenchapter on cluster and longitudinal analysis is also

included, and could have been left out withoutcompromising the focus and relevance of the text.A current list of software that may be used to �tgeneralized linear models is provided which is veryhelpful.A skillful use of examples is a strength of this

text – a wide range of research applications arecovered and ample workings are also provided toaid the reader in statistical calculations. Manymoregraphs have also been included in the second edi-tion, enhancing the worded, explanations.In the quest to provide more explicit explana-

tions and a wider range of topics while keepingthe overall length of the text to a minimum, someof the simplicity and brevity of the �rst editionhas been lost, and on occasion, the written text ap-pears cramped. Also, while some mention is madeto other textbooks in many of the chapters, it mayhave been desirable to include more speci�c refer-ences within each section, given that the text cov-ers many topics very brie�y.Overall, I would highly recommend this text for

a reader interested in �nding out at an introductorylevel what the subject area of generalized linearmodels is all about, including the non-statistician,undergraduate and graduate-level student.

KERRIE NELSONDepartment of Statistics

LeConte CollegeUniversity of South Carolina

ColumbiaSouth Carolina 29208, U.S.A.

(DOI: 10.1002/sim.1493)

2. STATISTICS IN CLINICAL ONCOLOGY. John Crow-ley (ed.), Marcel Dekker, New York, 2001. Price:$175.00. ISBN: 0-8247-9025-1

This volume comprises contributions fromsome 43 biostatistical experts examining me-thodological issues arising in the design and

analysis of clinical trials as related to oncol-ogy. Traditionally, studies in oncology haveprovided a wealth of methodological ques-tions to challenge biostatisticians, especially inthe context of censored observations, multiplefailures and more recently, prognostic factormodelling.

Copyright ? 2004 John Wiley & Sons, Ltd.

2152 BOOK REVIEWS

The volume is organized in more or less achronological order of the development of can-cer treatments—phase I, phase II studies andphase III studies. Sections on the interpretation ofstudy results, prognostic factor development andpatient-based outcomes complete the discussion.The volume thus o�ers practising biostatisticiansand students a solid reference for the vast major-ity of biostatistical problems encountered whendesigning=analysing studies related to the man-agement of cancer.Phase I trials: These studies consider dose-

�nding designs to determine the optimum dose ofinterest. In oncology studies, phase I studies aregenerally con�ned to examining severe toxicitieswhilst at the same time determining dose levels oftreatments (either chemotherapy or radiotherapy)which may yield bene�t. Unlike some other dis-ease areas, where phase I studies are performedon ‘healthy’ volunteers, in oncology the patientschosen have usually failed �rst, second or thirdline treatment.The treatment of phase I studies in this context

is excellent providing both a rationale and detail ofhow such studies may be designed. A modi�cationof these designs is to introduce a prior distributionof the maximum tolerated dose (MTD) leadingto the so-called continual reassessment methoddeveloped within a Bayesian framework. Thesedesigns and extensions (two-stage and groupeddesigns) are then discussed in some detail. Thesection concludes with a discussion on the meritsof choosing a particular design (including dosespacing) and provides comparisons via simula-tions of the impact the di�erent designs wouldhave in clinical practice.Phase II trials: The second section concentrates

on issues with the design of phase II clinical trials.These studies are the natural progression from theresults of the phase I study. Having determinedthe MTD the question is now whether the exper-imental compound demonstrates suitable activityagainst the disease. Phase II studies provide alarger (but still highly selected) cohort of patientswhere careful monitoring of the disease regressionwould be performed. Treatment toxicity is also anissue as the patient population is now somewhatbroader.This section begins with an overview of such

designs including suggested analysis methods cov-ering issues of multiple arms and multiple end-points. Study designs addressing both the role ofresponse (activity) and toxicity are presented in-cluding strategies allowing for trade-o�s betweenthe two. Discussion on the issues underlying the

selection of the various phase II designs based onthe outcomes of interest (response rate, time toevent, outcomes on an ordinal scale) and possiblesample sizes required to detect clinical worthwhiletreatment activity concludes this section.Phase III trials: Phase III studies are introduced

via the most challenging statistical concept in thedesign of the trial—that of the appropriate sam-ple size. Concepts covered include calculating thesample size for di�erent outcome types (binary,time to event, piecewise exponential) as well as adiscussion of the power implications if continualmonitoring were performed throughout the study.The problem of multiple outcomes is then dis-cussed along with implications for the sample sizeif studies contain more than two treatment arms.The discussion is extended to the multiple com-parisons arising from factorial designs where thee�ect of interaction is also considered.A growing area of importance is the concept of

equivalence trials, which in the past decade havegained enormous popularity as generic medica-tions provide competition to their patented coun-terparts. Concepts of design and analysis of thesestudies are discussed. The concept of equivalenceis not taught in mainstream biostatistics, and an ex-tra section detailing some of the more salient dif-ferences between e�cacy (familiar to most) andequivalence designs would have been helpful.Aspects of interim analyses and potential early

stopping are key components in the design of mostclinical trials in cancer. They were originally moti-vated to identify potential bene�cial treatment reg-imens quickly. Aspects of interim analyses suchas early bene�t and/or futility (stochastic curtail-ment) are described. Interim analyses may alsobe considered as analysis triggers in the classical(Wald) sequential analysis framework. Triangulartests popularized by Armitage in the 1950s and ex-tended by Whitehead in the 1980s are developedin the cancer trial setting.Outcomes other than survival or its surrogates:

Quality of life (QoL), (termed complementary)outcomes and their extension to survival toxic-ity (quality adjusted survival) are introduced asincorporating patient perceptions of the e�ect(on the patient) of treatment in terms of boththe toxicity and bene�t from the individual’sperspective. The Q-TwiST concept is discussedas a vehicle to determine utilities (individualtrade-o�s) of treatment, which can then be in-corporated into a cost–bene�t analysis. Issuesin measuring complementary outcome are ex-tended to more general QoL scales with associ-ated problems of missing data and complexities

Copyright ? 2004 John Wiley & Sons, Ltd. Statist. Med. 2004; 23:2151–2153

BOOK REVIEWS 2153

of analysis using repeated measures (using meth-ods such as ANOVA, MANOVA and generalizedestimating equations). There is some discussionof the various types of missing data and howsuch problems may be addressed in subsequentanalyses.This section concludes with an introduction

to performing a cost-e�ectiveness analysis ofthe study. Such an economic analysis brings to-gether the potential bene�t of treatment, the utilityof treatment from the patient’s perspective andthe overall resource utilization of the care. Aframework for the synthesis of these measures isdetailed.Prognostic factors: Once a study has been com-

pleted, using this information to identify and esti-mate the potential risk of future patients providesadded usefulness of the generalizability of the re-sults. One tool gaining popularity is the prognosticfactor model where risk factors for outcome areincorporated into a scoring scheme allowing forclassifying an individual’s risk and optimizingtreatment. Methods for developing prognosticmodels including regression modelling, classi�ca-tionregression trees (CART) and arti�cial neural net-works are discussed as well as validation of someof these approaches and issues of sample sizesrequired to introduce extra prognostic factors intothe model. The statistical developments of prog-nostic factors, together with the CART approach,are discussed within the framework of an exam-ple of surgery for stomach cancer. The CARTapproach is then examined in some detail, withdiscussions on once a ‘tree’ is established, howvarious ‘branches’ may be pruned to come up witha simpler model. Also discussed are aspects ofthe properties of dividing a continuous prognosticvariable and the impact of such a division.

In studies of cancer trials, a key outcome issurvival and/or progression/disease free survival.In developing prognostic models based on thisoutcome, time-to-event methods, especially pro-portional hazards (Cox) regression models play apivotal role. Methods of measuring ‘goodnessof �t’ for such models are introduced and theirproperties discussed. This is extended to examinegraphically the e�ects of individual prognosticfactors to ascertain their prognostic signi�cance.Methods for determining whether transforma-tion of prognostic factors improve interpretationare introduced by examining their behaviour onmartingale residuals. The ideas are extended toincorporate nonproportional hazards and strati�edanalyses.Generalizability and validity: The volume con-

cludes with discussions of both study design issues(appropriateness of endpoints), extent of statisticalanalysis and suboptimal statistical analysis meth-ods. Much of these discussions centre around thepoints detailed in the CONSORT statement whichoutlines areas/issues to be addressed when report-ing a clinical study. In cancer studies, failure toaccount for competing risks commonly producesan under-estimate of the underlying e�ect.This volume is comprehensive and well

presented—addressing issues commonly onlyaccessible in original research papers. It bringstogether a statistical cycle of the design and anal-ysis of cancer studies and will be welcomed bybiostatisticians in this �eld.

VAL GEBSKINHMRC Clinical Trials Centre

University of Sydney, Locked Bag 77Camperdown NSW 2050 Australia

(DOI:10.1002/sim.1803)

Copyright ? 2004 John Wiley & Sons, Ltd. Statist. Med. 2004; 23:2151–2153