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Sample size calculations in clinical research

Sample size calculation is an essential step in the design of any study. Chow et al. recommend that we should all use a sample size calculation that matches the statistical test we will use at the end of the study, rather than just approximating it with a t-test or a chi-squared test. To fulfill this aim, this book provides formulae for calculating the sample size for a wide range of study designs.Where possible, formulae are provided for tests for difference, non-inferiority, superiority and equivalence (although confusingly, and very misleadingly, tests for difference are referred to consistently as tests for equality). Unfortunately, I could not find an easy way of assessing how the second edition of the book differs from the first edition. The book is primarily aimed at those involved with pharmaceutical drug development, and makes particular reference to the regulatory requirements of the United States Food and Drug Administration. There is no discussion of any of the many software packages that are available to calculate sample size. However, it is still very useful as a reference. When faced with an unusual study design, I prefer to use two ways of calculating the sample size, to check that I have not made a major error. Using an equation from the book is a handy way of checking the output from computer programmes. Initially, the book is hard to dip into and use, as there is no glossary of terminology and notation. Thus, one is forced to read the initial sections to find the definitions. Having to do this once is not a bad thing, but might prove irritating if I don’t use the book regularly enough to remember the various definitions. There are also inconsistencies in the notation through the book. The general style is one of dense prose interspersed with formulae. Some of the ideas, such as the difference between tests for non-inferiority and equivalence might have been more succinctly described using a graph. In some places there are basic grammatical errors. For instance, in page 20 the authors state ‘Basically, sample size re-estimation involves either unblinding or without unblinding of the treatment codes’. There are omissions from the index; there were no entries under repeated measures, ordinal data, or non-inferiority. This text is useful as a reference for experienced biostatisticians and researchers, particularly those in the pharmaceutical industry. It isn’t the best written text I’ve ever read, but it is packed full of useful information.