counting the total number of spots on two dice,which goes from 2 to 12 with a maximum probabil-ity of getting 7.

The author introduces the possibility of one-tailed testing, and, although he warns againstbiased decisions, he gives an example which refersto blood pressure lowering with two differentdrugs. I believe it would be hard to find manyexperienced clinical researchers who would ap-prove of this use of one-tailed testing.

In an example on diagnostic testing it is as-sumed that 5 per cent of a population has a disease.The author then constructs a sample of 1000 pa-tients and says that the disease prevalence is 5 percent in this sample. However, the disease preva-lence in actual patients is almost never the same asin a population. It may approach 100 per cent ifonly the most severely affected patients, which areeasy to diagnose with any reasonable test, seeka doctor. Therefore, the clinical value of diagnostictests depends not only on the disease prevalencebut also on which part of the disease spectrum thedoctor sees. Thus, a test may work well in a hospi-tal department and poorly in general practice, des-pite similar disease prevalence. A short discussionof this problem and of interobserver variationwould have been relevant, but it is probably de-manding too much from such a short book.

It is more problematic that the author arguesthat controls in case-control studies may be pa-tients hospitalized at the same hospital as thecases, but with different and non-related diseases.Such comparisons will be expected to be biased,and the author should not have introduced thisdesign without mentioning Berkson’s fallacy.

The introduction to clinical trials is somewhatconfusing. It says that ‘the exposure of interest is

a treatment group’ and that ‘at the start of theclinical trial, everyone is comparable in terms ofillness’. ‘Everyone’ is certainly not comparable andit would have been better to say that the exposureof interest is a treatment and that the randomiz-ation process intends to distribute known andunknown prognostic factors equally on the twocompared groups. Cohorts are defined as groupsof people who are free from disease at the start ofthe project, thus excluding cohorts which startwith a disease or an intervention for a disease.

The author interprets the placebo effect as theeffect which has been measured in a group treatedwith placebo, thereby ignoring regression towardsthe mean effects and the natural course of thedisease. Further, he postulates that the placeboeffect is a measurable, objective physiological ef-fect. This is quite controversial, however. Mostclaimed placebo effects have been subjective andthe objective ones have often been based on poortrials. This illustrates what I miss most in the book:references. In these days of evidence based every-thing, books on research methodology shouldhave references, allowing readers to judge thevalidity of authors’ opinions and to expand theirknowledge beyond the book by consulting thesources. The readers get some help, however, sincethe book ends with short comments on elevenbooks for additional reading.

PETER C. G"TZSCHE

The Nordic Cochrane CentreRigshospitalet, Dept. 7112

18B TagensvejDK-2200 Copenhagen N

Denmark

3. PRINCIPLES OF STATISTICAL INFERENCE FROM

A NEO-FISHERIAN PERSPECTIVE. Luigi Pace andAlessandra Salvan, World Scientific PublishingCo., Singapore, 1997. No of pages: xvii#556.Price: £47. ISBN 9-8102-3066-4

This advanced graduate-level text is concernedprimarily with mathematical methods associatedwith neo-Fisherian, meaning modern non-Bayesian, theories of statistical inference. Althoughthere is some discussion of philosophical principlesin the early chapters, the principal emphasis is onmethods for asymptotic approximation of distri-

butions, conditional distributions, likelihoods andconditional likelihoods. There is an extensive dis-cussion of marginal likelihood, profile likelihood,partial likelihood, quasi-likelihood and empiricallikelihood as methods for coping with nuisanceparameters. Although the emphasis is on methodsrather than principles, this is a theoretical treatise,not a manual for applications.

Fundamental material such as parametric mod-els, likelihood, sufficiency, completeness, ancillar-ity and parameterization invariance is coveredin the first two chapters. Chapter 3 is a surveyof elementary mathematical methods such as

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generating functions, cumulants, orders of magni-tude of sequences, and asymptotic expansions. Thenext four chapters cover pseudo, or modified, like-lihood, exponential families, exponential disper-sion models and group transformation models.The remaining four chapters and appendices dealwith asymptotic approximation and asymptoticexpansions, including Edgeworth and saddlepointapproximation.

The selection of topics reflects the research activ-ity that has taken place in the past 20—25 years inthe area of conditional frequency-theory inferenceand asymptotic approximation. For anyonewishing to get acquainted with these topics, this isa good comprehensive introduction, possibly des-tined to become the definitive text in the area.I found no misprints and very few errors or

omissions. The authors cover a very wide range oftopics in an authoritative manner, from a carefuldiscussion of orders of asymptotic approximationto partial likelihood, curved exponential families,saddlepoint approximation, Berry—Esseen bounds,Bartlett adjustment, and the p* formula. I thor-oughly recommend the book for advanced grad-uate students and for all research workers in theareas of asymptotic approximation and condi-tional inference.

PETER MCCULLAGH

Department of StatisticsUniversity of Chicago5734 S University Ave

Chicago, IL 60637-1546U.S.A.

4. COMPUTER-AIDED MULTIVARIATE ANALYSIS.Third edition. A. A. Afifi & V. Clark, Chapman& Hall, U.K. 1996 (reprinted 1998). No. of pages:xxi#455. Price: £45. ISBN 0-4127-3060-X

The book by A. A. Afifi and V. Clark was writtenabout 15 years ago for investigators, especiallybehavioural scientists, biomedical scientists andindustrial users, assuming that the readers havetaken a basic course in statistics but do not haveany knowledge of mathematics beyond high-school level. The basis of the choice of topics in-cluded has been the authors’ long experience asconsulting statisticians. The version of the bookreviewed here is the third one (a reprinting of thesecond edition of 1996). During its lifetime, thebook has developed along with the rapid develop-ment of the methodology of statistical data analy-sis and has kept pace with the speedy growth of thestatistical software.

The last version differs from previous onesmainly in Part One ‘Preparation for Analysis’,containing Chapters 1—5. This part describes theprocedures of multivariate analysis using practicalexamples (Chapter 1) and introduces several sys-tems of characterization (typologies) of variables(Chapter 2). Preparation of data for analysishas been considered in Chapter 3, and datamanagement (handling missing values, detection ofoutliers, but also data screening, for example, as-sessing and testing normality, assessing and testingindependence) and selecting and performing trans-

formations have been introduced in Chapter 4.The key to successful analysis of real data is theselection of the appropriate method of analysis.Several rules for selection using the typology ofvariables and statistical assumptions of themethod have been explained in Chapter 5.

One of the new additions is Chapter 3, where thepossibilities of using different hardware and soft-ware have been explained, including a very briefoverview of six statistical packages. Also, mecha-nisms of data entering and combining data setshave been reviewed there.

Overall, Part One is in good concordance withthe modern concept of data analysis where the keypoint is dealing with real data that are assumed tobe dirty, that is, contain several random and non-random errors and have missing values, so that noreliable result can be obtained without data check-ing, cleaning, editing and quality assessment.

At the same time, several sections of Part One,especially Chapter 3, are already outdated, forexample, the paragraph ‘Choice of computer forstatistical analysis’. The list of most popular stat-istical software has changed since the time of writ-ing the book, for example, SYSTAT and BMDPhave almost disappeared, and S# has appearedinstead.

The remaining two parts of the book — Part Two‘Applied regression’ and Part Three ‘Multivariateanalysis’ — have a more traditional look. The ma-terial here has been selected with a good sense ofbalance.

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