Free PDF Essential Statistics for the Pharmaceutical Sciences, by Philip Rowe
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Essential Statistics for the Pharmaceutical Sciences, by Philip Rowe
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Essential Statistics for the Pharmaceutical Sciences is targeted at all those involved in research in pharmacology, pharmacy or other areas of pharmaceutical science; everybody from undergraduate project students to experienced researchers should find the material they need.
This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work, selecting appropriate statistical methodology and correctly interpreting the results. It deliberately avoids detailed calculation methodology. Its key features are friendliness and clarity. All methods are illustrated with realistic examples from within pharmaceutical science.
This edition now includes expanded coverage of some of the topics included in the first edition and adds some new topics relevant to pharmaceutical research.
- a clear, accessible introduction to the key statistical techniques used within the pharmaceutical sciences
- all examples set in relevant pharmaceutical contexts.
- key points emphasised in summary boxes and warnings of potential abuses in ‘pirate boxes’.
- supplementary material - full data sets and detailed instructions for carrying out analyses using packages such as SPSS or Minitab – provided at www.ljmu.ac.uk/pbs/rowestats/
An invaluable introduction to statistics for any science student and an essential text for all those involved in pharmaceutical research at whatever level.
- Sales Rank: #1869781 in eBooks
- Published on: 2015-07-20
- Released on: 2015-07-20
- Format: Kindle eBook
Most helpful customer reviews
0 of 0 people found the following review helpful.
Clear, Well-Written Text of Basics and Beyond, Aimed at Users, not Statisticians.
By mirasreviews
“Essential Statistics for the Pharmaceutical Sciences” by Philip Rowe is aimed at statistics users from the undergraduate level to experienced researchers, not at statisticians. In most cases, it does not explain how to do the math yourself. It explains what statistical procedures should be used for different types of data, why, how to enter the data into the statistical software package of your choice, how to interpret the results, pitfalls, limitations, and alternative procedures. The math itself is only explained when the author feels it would be beneficial to understanding the applications of the procedure. Rowe does not recommend any particular statistical package, but basic instructions are provided for using Minitab and SPSS, with more detailed instructions on the book’s companion web site.
Examples are taken from the pharmaceutical sciences, but the topics are applicable to similar disciplines in the biological, biomedical, and chemical sciences. The first six chapters are basic statistical concepts. Chapters 7-13 discuss various types of t-tests, which have broad applications. The remaining chapters address other topics in, generally, increasing order of complexity. These tend to be more specific to the pharmaceutical sciences or biomedical sciences. Explanations are clear, even for those with little background in statistics, though the later chapters take some concentration and re-reading if you’ve never heard of these procedures before. The second edition differs from the first (2007) edition in that the author has added analysis of covariance, logistic regression, measures of agreement, and survival analysis.
The book’s first part, chapters 1-2, introduces different types of data (continuous measurement, categorical, ordinal) and different ways of presenting data visually. The second part, chapters 3-16, discusses methods of analyzing interval-scale data (continuous measurement data), including six chapters about t-tests. This includes basic calculations of mean, median, mode, standard deviation, standard error of the mean, confidence intervals, how to identify non-normal distributions, to concepts of skewness, kurtosis, population, sample, and how to cope with a strong skew. Chapters on t-tests cover the two-sample test, how to determine if results are significant, statistical compared to practical significance, calculating sample size and P value, and identifying Type I and Type II errors. Beyond the chapters on t-tests, Rowe discusses correlation testing, regression analysis, and analysis of covariance (ANCOVA).
At this point, the book gets esoteric for readers with no statistical background. The third part, chapters 17-20, addresses nominal-scale data, or data that consists of categorizations rather than measurements, emphasizing that you need a lot of this type of data to be useful. Topics include the goodness of fit chi-square test as an alternative to confidence intervals, the contingency chi-square test, calculation of relative risk, odds ratio, and logistic regression to determine the influences of nominal or interval factors on the outcome. Part 4 addresses ordinal-scale data and is the only part of the book that addresses non-normal distributions of data. All other methods apply to normally distributed data only. A log transform to give the data a normal distribution was discussed in an earlier chapter. Various non-parametric tests are discussed, which transform the data into ranks, that can substitute for parametric tests in cases on non-normal distribution.
The book’s fifth part, chapters 23-25, addresses measures of agreement, survival analysis, multiple testing and its inherent problems, and difficulties with data from questionnaires. A few chapters have appendices, but these are located at the end of the chapter, not at the end of the book. For all statistical procedures that are discussed, the author tells the reader what data you need to employ the test, how to do it, and explains when the test should be used and why we do it this way. Philip Rowe is a good writer with a sharp sense of humor, which he does not eschew in his prose. Rowe includes “pirate boxes” throughout the text that explain how to lie with statistics. He points out that these are possible because scientific journals do not require submissions to record the main aspects of their trials in advance. All chapters include a brief, clear summary at the end.
0 of 0 people found the following review helpful.
Well written, conceptually-oriented stats text
By Zhimbo
An important passage to help you understand this book: "The book is aimed at those who have to use statistics, but have no ambition to become statisticians per se. It avoids getting bogged down in calculation methods and focuses instead on crucial issues that surround data generation and analysis".
The book is geared towards pharmaceutical sciences, primarily through the concrete examples it uses, and partly through the choice of more advanced topics to cover (e.g., Kaplan-Meier survival analysis is presented, a rarity in most basic stats books).
I got this not as a new learner, but as someone who learned some of this quite a while ago and could use an occasional refresher. Mostly, I'm very happy - the text is generally easy to understand and conceptually oriented (rather than focusing on deriving formulas or on the mechanics of calculation), is especially good and pointing out common pitfalls and misconceptions,
My biggest - and really only - gripe is the rather light coverage of multiple regression - only about 9 pages of material! (From the text: "This section is really only meant to give a taste of what it does. If you want to use it yourself, it would probably be a good idea to get some advice from a competent statistician.") Mmmm...Okay. Seems like a pretty important topic to skim over (although, as far as a 9 page introduction goes, it's pretty good and easy to follow).
Still, it's mostly successful and useful and probably the book the author intended, but it would have to go just a little bit further for me to give it five stars.
0 of 0 people found the following review helpful.
Covers the expected basics plus a number of arguably more advanced topics very well
By OnceMore
This book is very well written and organized. It's a bit different from the usual introductory statistics books in that it not only covers the expected basic topics such as measurement scales (nominal, ordinal, interval data types), descriptive statistics (e.g., measures of central tendency and variability), and inferential statistics (e.g., hypothesis testing, t-tests, analysis of variance, chi-square, correlation) very well, but also topics not typically expected of or usually included in an introductory statistics book but have special relevance to pharmaceutical and biomedical sciences, such as analysis of covariance, and survival analysis.
The companion website for the book contains relevant data sets and instructions on how to perform the various statistical methods using statistical software packages such as SPSS and Minitab, but not the increasingly popular open source and cost-free R programming language and statistical tool. The use of SPSS and / or Minitab may pose potential challenges to some readers (especially those considering the use of this book for self-guided study), such as access to and / or lack of familiarity with the tool(s), but they should still be able to follow the conceptual discussions without too much difficulty.
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