Design and Analysis of Bioavailability and Bioequivalence Studies

It has been over 9 years since the second edition of this book came out. In that time there has been a lot of new research and developments in regulatory guidelines regarding individual and population bioequivalence. I was particularly interested in the bootstrap confidence interval approaches to individual bioequivalence and for a time the FDA 1997 Guidance recommended the bootstrap approach. But in recent years the old standby, average bioequivalence is back in favor. All these events are chronicled in this book. The book is greatly expanded and contains 4 new chapters and around 100 new references. Most topics are covered in great detail and the text appears to be highly detailed.One thing that did bother me though was the large number of references to the 1993 paper by Schall and Luus in Statistics in Medicine. In the paper they provide a bootstrap approach that Shao and Pigeot proved was not consistent. It surprises me that Pigeot's survey articles in the DIJ and the joint work of Shao and Pigeot are not referenced and I did not see any disclaimers about the Schall and Luus procedure. I was also disappointed that there was no discussion of an adaptive design for bioequivalence. I did such a two-stage design while working at Auxilium. It involved sample size reestimation at an interim time point using an AB/BA crossover design.Nevertheless this book is a fantastic reference for bioavailability and bioequivalence and is definitely worth having. To every method there are numerical examples given with real data. Methods for individual and population bioequivalence are complicated and the authors cover all the complications in detail.Now even if there were no updates in the first 16 chapters I would still buy the book because Chapters 17-20 are totally new chapters and are very enlightening. I particularly enjoyed Chapter 19. The book seems to be guided by the FDA research and a lot of emphasis is placed on the regulatory guidance and guidelines related to the various types of bioequivalence studies by the FDA and the EMEA. But in Chapter 19 the authors take an independent look and are critical of some of the guidances based on statistical research in the literature. For instance the use of log transformations is brought into question for AUC and Cmax and back transforming to the original scale can create biases when the analysis is properly done on the log scale.Another nice feature of the book is that all the latest statistical methods that have a place here are mentioned. This includes mixed effects linear and non-linear models, generalized estimating equations, the bootstrap and other nonparametric approaches. Also the complications of ocnfoundiong in crossover trials and the many subtle issues associated with equivalence testing and crossover designs are covered. Nothing is avoided deliberately.Topics that I was not familiar with that interested me were generalized p-values, the linearization approach of Hyslop et al. for parametric estimation of individual bioequivalence, bioequivalence meta-analyses,and in vivo drug interaction studies for nasal sprays.