Discrete Distributions: Applications in the Health Sciences

Zelterman D (2004)

ISBN 0470868880; 306 pages; £55.00, h82.50, $110.00

Wiley; http:/www.wiley.com/

This book grew out of the author’s work with applications of

statistical modelling in studying cancer, demography, epidemio-

logy and related disciplines. They gave rise to the study of several

discrete probability distributions: binomial and negative binomial,

minimum and maximum negative binomial, hypergeometric

and negative hypergeometric, minimum and maximum negative

hypergeometric, and what the author calls the ‘twins and family

frequency distributions’. These distributions correspond to differ-

ent sampling schemes when sampling from an urn containing balls

with two different colours, and are all treated in the book.

The aim of the book is to put together the results of these

applications and distributions, which have been published

previously in various journals and books, and give a more

detailed description than has been given before. The intended

audience includes students on advanced graduate-level courses

and researchers in the field.

The book has nine chapters, with a total of 56 subsections.

Chapter 1 gives a nice description of univariate and multivariate

discrete distributions in general, and a more thorough overview

of the binomial, multinomial, Poisson, negative binomial and

hypergeometric distributions. This gives the background for the

derivations of the other distributions in the subsequent chapters.

Chapters 2 and 3 derive the maximum negative binomial

distribution and its finite-sample analogue, the maximum

negative hypergeometric distribution, motivated by applica-

tions in genetics and Noah’s collection of animals in the Ark

before the flood. Their connections with other similar discrete

distributions form the basis for deriving and proving their

properties and describing their characteristics. Estimation

methods for the distributions are also derived.

Chapters 4 and 5 are motivated by studies of genetic

components for longevity, where the data consist of the joint

lifespans of Danish female twin pairs, with no censoring.

Chapter 4 gives a univariate distribution for twins, specified

for modelling the number of twin pairs where both are alive

at a certain age, to try to detect if there is a positive association

in longevity for twins. This is applied to the Danish Twin

Registry, where the genetic component of longevity is

estimated. Chapter 5 extends the univariate setting of Chapter

4 to the multivariate case, with simultaneous and conditional

distributions for twin pairs. This is again applied to the Danish

Twin Registry.

Chapters 6–9 are concerned with the question of family

disease clusters. Chapter 6 describes a frequency model and

develops inference methods. These are then applied to, among

others, data on childhood cancer and childhood mortality.

Chapter 7 continues with modelling dependence in family

disease clusters using discrete distributions for sums of

dependent Bernoulli-distributed random variables, while Chap-

ter 8 models the dependence using weighted binomial distribu-

tions. Finally, Chapter 9 contains applications of the methods

in Chapters 7 and 8 to teratology experiments.

Several chapters contain program code for SAS, and there is

also code written in S-Plus/R. These are very helpful for

applying the methods. There are also two programs written in

Fortran. To use Fortran for a book published in 2004 feels

somewhat antiquated, and the code fills 18 pages. Code for

Matlab would have been easier and shorter, without losing

much in computational speed.

The book is well written and easy to read, but the

distributions it treats are quite specialized. It would have

benefited from also using more common discrete distributions

and their applications in the health sciences. This would have

made it more interesting to a wider audience: the back cover

claim that the book ‘Provides an overview of discrete

distributions and their applications in the health sciences’ does

not ring entirely true.

Andreas Karlsson

Uppsala University, Sweden

(DOI: 10.1002/pst.178)

Advances in Clinical Trial Biostatistics

Geller NL (ed.) (2003)

ISBN 0824790324; 271 pages; $99.95

Marcel Dekker; http://www.dekker.com/

I highly recommend this book to clinical trial statisticians who

read Pharmaceutical Statistics. In some ways, the title of the

book is a bit misleading: the word ‘advances’ in the title

obviously begs the question of what is the baseline. I had

assumed that the baseline was pretty advanced and that this

book would, therefore, be a collection of very highly advanced

(for which possibly read ‘difficult’ or ‘incomprehensible’)

methods. Statisticians working in the pharmaceutical industry

are busy people who typically do not have time to work through

the most challenging and cutting-edge research papers. The 12

chapters in this book are not aimed at such a high level, and are

written in a style that explains the methods rather than one that

simply tries to impress you with how clever the authors are.

The book is divided into three (slightly) arbitrary sections.

Part I has just two chapters covering ‘Methods for early stage

trials’ – one on Bayesian methods in Phase I and one on a

hybrid of frequentist and Bayesian approaches. Part II has

seven chapters on ‘Methods for randomized trials’. It covers

equivalence trials, adaptive two-stage trials, cluster-randomized

trials, multiple endpoints, subgroups and interactions, permu-

tation tests for survival analysis and Bayesian reporting (not so

much design) of Phase III trials. Part III covers some ‘More

Book reviews226

Copyright # 2005 John Wiley & Sons, Ltd. Pharmaceut. Statist. 2005; 4: 225–228

complex problems’ and has a chapter on incorporating

compliance data, one on problems with missing data, and one

on statistical issues emerging from trials in HIV infection. At

285 pages, it is quite a short book, so that all of the chapters are

quite short and to the point.

The technical difficulty is a little varied, being partly

influenced by the individual chapter authors’ own style (20

authors from North America and Europe have contributed)

and the inherent complexity of the subject. My own areas of

existing expertise (and lack of it) also influence which chapters

offer a harder read. But I repeat my comment that the style of

writing is very much to explain issues, not just present them in

as terse a style as possible. Also, in most cases, the material

offered is not ‘cutting edge’ but rather more at a level such that

most statisticians who work in clinical trials will learn some new

ideas and consolidate others.

Some chapters are likely to be of less relevance to trialists in

the pharmaceutical industry than others. Perhaps the one on

cluster-randomized trials may be least useful – although I am

sure not completely irrelevant. Maybe some topics or chapters

are less applicable to trials for regulatory submission; but much

of the material is still of great value in Phase II and for helping

sponsors to understand how drugs are actually working. The

chapters on adaptive two-stage designs and on incorporating

compliance data are, perhaps, such examples. But none of the

content seems completely irrelevant to pharmaceutical industry

trialists.

Real examples of major published trials are used throughout

the book to illustrate the ideas presented. There is a good

subject index; there is also an index of abbreviations and an

index to the trials used as examples.

My one serious concern is the number of production errors:

typos, missed words, duplicated words, etc. Appropriate and

consistent use of italics for designating variables is poor, and its

inconsistent use (even within sentences, let alone between

sentences) gets quite irritating. These types of mistake in the text

can all be worked around (although some took two or three reads

before I could figure out what a sentence was supposed to say). I

did not notice any typographical errors in reproduction of

formulae, but given the unusually large number of textual errors

it seems unlikely that all the formulae will have avoided this

problem. So if you are planning to use formulae for your own

work, I suggest a careful follow-through of the background to

work up the correct expressions yourself. Whilst some of this

quality stuff should be down to the publisher to get right, the

variability in quality between chapters suggests variable quality of

proof-reading between different authors – an inevitable problem

with a contributed and edited compilation. Perhaps these errors

could be sorted out in a reprint, if not an updated second edition.

To sum up, I simply repeat my opening comment: I highly

recommend this book to clinical trial statisticians who read

Pharmaceutical Statistics.

Simon Day

Medicines and Healthcare products

Regulatory Agency, UK

(DOI: 10.1002/pst.179)

Leading Pharmaceutical Innovation: Trends and Drivers for

Growth in the Pharmaceutical Industry

Gassmann O, Reepmeyer G, Von Zedtwitz M (2004)

ISBN 3540407170; 178 pages; £38.50, h53.45, $79.95

Springer-Verlag; http://www.springeronline.com/

Chapter 1, ‘Innovation as a key success factor in the

pharmaceutical industry’ (22 pages), lays out the basic structure

of the industry and the nature of pharmaceutical development,

as if for new investors in the area. There is very little wastage,

with statistics falling thick and fast. Topics covered include

blockbusters, competition, generic products, regulation, mer-

gers and so on. A startling chart shows that for two of the

largest mergers (GlaxoSmithKline and AstraZeneca) the

average number of NCEs produced per year fell from 4.5 to

less than 1.0 after merger. It is apparently all to do with the

balance between defensive and aggressive strategies.

Chapter 2, ‘Pharmaceutical innovation: The case of

Switzerland’ (30 pages), could have been subtitled ‘In praise

of percentages’. The chapter is bursting with them, with market

growth-rate forecasts, drug sales by therapeutic area, imports,

exports, investment, production, and distribution channels,

and a general reader will have difficulty seeing the wood for

the trees.

The central core of the book is laid out in three chapters,

each addressing a broad industry challenge. Chapter 3, ‘The

science and technology challenge: How to find new drugs’

(18 pages), states only 24 out of 3000 biotechnology companies

were profitable in the year 2000, and that 381 genomics

deals (alliances) were reported in 1999. Whilst the large

integrated companies have most of the cash, much of the raw

knowledge is in the specialist biotech companies and insti-

tutions. Hence the importance of fostering the organiza-

tional skills needed to form productive alliances. This theme

tends to overshadow the many other critical areas for biotech

products. High-throughput screening and proteomics are

mentioned, but not important obstacles such as safety and

drug delivery.

Chapter 4, ‘The pipeline management challenge: How to

shape the innovation-flow’ (16 pages), is rather insubstantial,

stretching to barely eight pages of text when boxes and charts

have been removed. ‘Drug discovery is a complex process’ is the

basic message. The main case study, Novartis’s Gleevec (for

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