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rather than in a separate chapter, but the index provides a sensible guide to their location. Propor- tional hazards regression can be tracked down under that name or Cox’s regression. Medical topics are also included. Perhaps the best indicator of the comprehensiveness of the index is its length - 23 pages!

So can this superb book be faulted? A major omission is any reference to Bayesian approaches to statistics. In the next few years I expect to see Bayesian methods becoming more widely used and reported in medical research. An important func- tion of a textbook at this level is as a reference for new or unfamiliar techniques. I do not expect a book such as Altman to describe to the average doctor how to carry out a Bayesian analysis. But for the doctor who has just read a report of a clinical trial which mentions the possibility of a Bayesian approach to estimation of effect after stopping the trial early, or the researcher who has been to a meeting and seen an expert system demonstrated which utilizes a Bayesian frame- work, what does Altman offer for their illumina- tion? The index refers them to Bayes theorem, which is described in the context of sensitivity and specificity, very properly, but that is it. Even a page or two outlining the general approach would have been useful. I predict that by the time this book goes to a second edition Bayesian methods will warrant at least a chapter to themselves.

This book is an invaluable aid to the design analysis and interpretation of medical research,

and will quickly take its place as a standard refer- ence. Who should buy it? For undergraduate medical students the book’s place is in the section of the course notes marked ‘Further reading’. For postgraduate students and researchers it is a book to which many will need good access. For those prepared to buy a personal copy, it will be an investment, and it is certainly a book that depart- ments and libraries should be encouraged to buy. For statisticians studying medical statistics at postgraduate level it is essential. For practising medical statisticians, especially those involved in teaching and consulting, go and buy your own. Unless, like me, you have the privilege of being given a copy for review.

DEBORAH ASHBY Department of Public Health

University of Liverpool P.O. Box 147, Liverpool L69 3BX

U. K .

REFERENCES

1. Bland, M. An lntroduction to Medical Statistics, Oxford University Press, Oxford, 1987.

2. Campbell, M. J. and Machin, D. Medical Statistics: A Commonsense Approach, Wiley, Chichester, 1990.

3. Armitage, P. and Berry, G. Statistical Methods in Medical Research, 2nd edition, Blackwell, Oxford. 1987.

BASIC AND CLINICAL BIOSTATISTICS. Beth Dawson- Saunders and Robert G. Trapp, Appleton and Lange, Connecticut, 1990. No. of pages: ix + 329. Price: f21.35. ISBN: C83854541-4

It is unusual for a book on basic medical statistics to be authored, as this one is, jointly by a statis- tician and a clinician. The advantage is that the intended readership (medical students, researchers and practitioners) is not subjected to unnecessary mathematical detail; moreover the development is based on real medical examples throughout.

The book moves from the simpler areas of study designs, data description, comparing means and proportions, correlation and regression, through to the more complex ones of survival data and multiple regression. Helpful exercises are given at the end of each chapter, and solutions provided. I thought the sections on the distinction between population and sample, and on estimation and

inferences from samples, well written - they would be a useful place to refer someone confused about these general concepts. More unusually, but no doubt reflecting the ‘clinical’ in the title, the book also contains extensive chapters on diagnostic tests, clinical decision making, and reading the medical literature.

I found the book rather verbose - an editorial demand for a cut to two-thirds of the present length would have reaped considerable benefits. Too much space i s devoted at the beginning of each chapter to explaining and justifying what material will be covered, and at the end to sum- marizing what has been achieved. I can imagine that only the most motivated of medical students would persevere with reading this book chapter by chapter.

With a medical audience in mind, each chapter is introduced by way of ‘presenting problems’ - real examples from medical studies which are then

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used in the ensuing text. I must admit that I found this idea distracting rather than helpful; it would have been easier to introduce each example as necessary in the main development so that con- tinuity of presentation was not lost. A much better idea was to include examples and interpretation of computing output for some of the statistical tech- niques. These are taken from a selection of soft- ware such as SYSTAT, SPSS, SAS, MINITAB and STATISTIX. These are rightly presented in the knowledge that nearly all routine statistical calcu- lations will now be performed using such packages. However, a complete omission from the book is how data should be organized and got onto a computer, including for example the design of data recording forms and the performing of initial data checking.

The book’s stated aim is to emphasize interpret- ative issues; however, on occasions this laudable objective seems to falter. For example, the chapter on comparing means degenerates into step-by-step instructions for calculating the relevant t-statistics. Also, whereas most biostatisticians (and even some medical journals) now advocate presenting exact P-values, the text relies on a priori ‘choosing an a-level’. Similarly the interpretation of non-signi- ficance is woefully inadequate. In the following example from page 153 the authors seem to get in a real mess. For comparing the three proportions 11/13, 6/12 and 3/10, the x : statistic of 7.26 (P = 0.03) was declared ‘non-significant’ using a a

priori but apparently arbitrarily chosen a = 0.01. The text reveals that ‘the null hypothesis of equal proportions . . . is not rejected. We conclude that the proportions . . . do not differ’.

Of course I have my personal set of technical quibbles with the book. For non-parametric tests, the approximate approach of Conover and Iman is adopted in which the data are replaced by ranks and analysed using parametric methods. The main advantage is stated to be computational, but since software packages will be used in practice, the standard non-parametric tests have no disadvant- age. It is also surprising that the standard error for the difference between two proportions is based on only the pooled proportion (page 147). Not many would agree either with ‘the correlation between two sets of . . . measurements will provide a meas- ure of how reliable the. . . measurements are’ (page 59).

I am conscious that the book’s intended reader- ship may well enjoy it more than I did. However, it would not generally be my first choice when deciding on an appropriate book to which to refer a medical colleague.

SIMON THOMPSON London School of Hygiene and Tropical Medicine

Keppel Street London, WCIE 7HT

U . K .

PRESCRIPTIONS FOR WORKING STATISTICIANS. Albert Madansky, Springer-Verlag, New York, 1988. No. of pages: xviii + 295. Price: DM 74

According to the author, this book attempts to carry on where a notional first course in applied statistics leaves off. The main thrust of the eight chapters is how to check the assumptions behind standard analyses, and how to fix things if the assumptions turn out to be violated. The analyses considered are principally ANOVA and multiple linear regression. The final chapter is devoted to analysis of cross-classified data.

Chapters 1 to 4 cover testing for normality, testing for homoscedasticity, testing for independ- ence of observations, and identification of outliers. Many procedures are given for each. Copious worked examples, tabulations of results and tables of percentage points of test statistics are presented. The first two chapters also contain a comparative

evaluation of the techniques. In general these chap- ters are unusual (such material will certainly not be found together anywhere else) and provide a useful practical summary. Unfortunately, not only do the evaluation sections inexplicably cease to appear after Chapter 2, I also found them less helpful than they might be. A clear statement of a ‘best buy’ in each case would be desirable, provided of course that the drawbacks of the preferred technique were also explained; the reader is not given this luxury. A small quibble: Chapter 1 should be called ‘Testing for non-normality’, since one usually asserts normality (null hypothesis) and tries to ‘disprove’ it, not the other way round.

Chapters 5 and 6 treat transformations and independent variable selection in multiple regres- sion. Much of this material is covered (and of course done more extensively, and in my opinion, better) by well-known texts such as Atkinson’ and Carroll and Ruppert.* Chapter 5 mainly deals with