Pharmaceutical Statistics: Practical and Clinical Applications - No Cost Library

Pharmaceutical Statistics: Practical and Clinical Applications - 5th Edition 

   Author(s): Bolton Sanford, Charles Bon  

                                    Publisher: Informa Healthcare, Year: 2010                                           

 Description: 

Using practical examples and solutions, Pharmaceutical Statistics: Practical and Clinical Applications, Fifth Edition provides the most comprehensive and comprehensive guide to the pharmaceutical industry's various statistical applications and research issues, particularly in clinical trials and bioequivalence studies.

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Book Review:

Many introductory statistical texts are available today, but if one is studying pharmacy or other health sciences, Bolton claims this  Text is 'the first predictive research textbook in the pharmaceutical sciences' (at least at the time of publication). I believe just the argument There are many introductory statistical texts available today, but if one is studying pharmacy or another health science, Bolton claims that this book is ’the only textbook on statistical applications in the pharmaceutical sciences’ (at least at the time of its publication). I assume his claim only applies to introductory texts since his book cites several texts dealing with pharmaceutical statistical applications. Although his claim might not be true today, five years since publication, his work certainly is a substantive contribution and worthy of consideration by its intended audience, ’pharmacists and health science-related scientists who want to learn statistics’. 

While reviewing this book, I tried to consider the perspective of such a reader. Even after just a short perusal, it became clear that this book is encyclopaedic in nature and probably intent. After the usual introductory material (i.e. definitions, graphics, probability concepts and sampling) with examples from the pharmaceutical sciences, Bolton covers statistical inference, sample size and power calculation, regression and ANOVA, experimental design and nonparametric methods. He also has separate chapters for transformations and outliers, quality control, process and assay validation, consumer testing and optimization techniques. Hence, he discusses practically the entire pharmaceutical development process and the essential statistical methods typically used during the process. Topics particularly noteworthy to this author include power analysis, multiple comparison procedures, various clinical triai designs (e.g. crossover, repeated measures) and consumer acceptance testing.

If encyclopaedic breadth is one of this book’s strengths, then the correlated lack of depth is a

weakness. Most of the more specialized or esoteric topics are covered quickly, and I felt rushed at

times while reading them (e.g. Mantel-Haenszel statistics, Winsorizing). Bolton’s apparent solution

to this is to provide references at the end of each chapter which should be a welcome sight for the

reader desiring more information on a certain topic. Another potential weakness, considering the

present level of computerization at schools and businesses, is the absence of any discussion of

software that can be of use to the reader. Bolton does recommend the use of such software in his

introduction, but his inclusion of ’shortcut’ formulae shows that he is expecting hand calculations

to be performed by at least some of his readership.

I believe formulae are useful insofar as they illustrate concepts; however, statistical software is so

widely available today that use of and emphasis on formulae should be limited. This practice should

decrease ’leamer’s anxiety’ among those statistics students who are relatively weak in mathematics

and serve to reinforce the important concepts rather than the laborious mechanics of statistics.

The layout of the book is very accommodating to the reader. Bolton makes frequent use of graphics,

drawings and tables to illustrate concepts and applications. Sections are of reasonable length, and

examples are used frequently. The exercises at the end of each chapter are interesting and relevant; some are rather challenging (and are labelled as such) and are used by Bolton to ’expand the scope

of the book’. Following the introduction and 16 chapters, there are some useful appendices which

cover variance properties, determination of relative potency via tests for linearity and parallelism, and

multiple regression. Some useful statistical tables follow, and the book is concluded with solutions to

most of the problems from each chapter and an adequate index.

I found this book (in its second edition) to be a welcome addition to the texts that one can recommend for the pharmaceutical scientist who would like to learn more about both basic and advanced

statistical applications. Bolton’s diverse experience in this field is obvious, and he shares his experience effectively in this text. If future editions are considered, I would recommend a discussion and use of statistical software throughout the book in place of laborious computations. I would also suggest a discussion of Bayesian probability theory and its many applications in pharmaceutical development to expose the statistical novice to some of the choices available in data-driven decision-making. In all, I believe Bolton has succeeded well at his

goal in this book’s second edition.

Reviewed by Nathan Enas, Statistical It refers to introductory texts as his book references many texts concerned with mathematical methods for pharmaceuticals. Whilst his argument may not be valid  Still, five years after its publication, his work is definitely a significant achievement and worthy of recognition by his target audience, 'pharmacists and scientists in the field of health who want to know statistics.' While reviewing this book, I was attempting to consider such a reader 's perspective.

Only after a quick perusal it became clear that this book is encyclopedic in nature and possibly deliberate. Bolton covers statistical inference, sample size and power calculation, regression and ANOVA, experimental design and nonparametric methods following the usual introductory material ( i.e. definitions, graphics, probability concepts and sampling), with examples from the pharmaceutical sciences. He also has separate chapters for transitions and outliers, quality assurance, evaluation of procedures and assays, monitoring of products and methods for optimisation. Therefore, he discusses virtually the whole process of pharmaceutical development and the essential statistical methods typically used during the process. Particularly notable topics for this author include strength analysis, several comparison methods, different clinical aspects

Triai prototypes (e.g. convergence, repetitive measures) and checks for market approval.

If encyclopedic breadth is one of the strengths of this book then a weakness is the correlated lack of depth. The better portion of the more technical or abstract

Topics are dealt with quickly, and I sometimes felt rushed while reading them (e.g. Mantel-Haenszel statistics, Winsorizing). Apparent solution for Bolton For that is to include references at the end of each chapter and will be a welcoming sight for the reader who wants to provide more details about a specific subject.

Another potential weakness, given the current level of computerization at schools and businesses, is the absence of any software discussion that could be of use to the reader. Bolton does advocate using such tools in his introduction, but his use of the 'shortcut' formulae suggests that he expects at least some of his readership to do hand calculations.

I assume that formulas are useful to the degree that they explain concepts; however, mathematical tools is so common today that they are used and stressed Should be limited to formulae. This practice should reduce the 'leamer 's anxiety amongst the statistics students who are relatively weak in mathematics. And serve to strengthen important concepts, rather than laborious statistical mechanics.

The book's style is really user friendly. Bolton often takes use of illustrations, sketches and tables to explain ideas and Submissions. Sections are relatively long, and examples are often seen. Throughout the end of each chapter, the exercises are informative and relevant; others are difficult (and classified as such) and are used by Bolton to 'expand the book's reach.' After the introduction, and 16

Chapters, some important appendices cover variance properties, relative potency evaluation by linearity and parallel checking, and Several losses. There are several useful statistical tables, and the book ends with solutions to most of the problems in each chapter and a Proper map.

I consider this book (in its second edition) to be a good addition to the texts that can be suggested to the pharmaceutical scientist seeking to know more about both simple and advanced statistical applications. The vast experience Bolton has in this area is evident and he shares his expertise

In effect, in this text. I would recommend a discussion and use of statistical software throughout the book if future editions are considered Of tedious equations. I would also propose a discussion of the Bayesian probability theory and its many applications in pharmaceutical development to expose the statistical novice to some of the options available in data-driven decision-making. In all, I believe that in the second edition of this book, Bolton has succeeded well in his goal.