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An Introduction to Medical Statistics, by Martin Bland
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Now in its Fourth Edition, An Introduction to Medical Statistics continues to be a 'must-have' textbook for anyone who needs a clear logical guide to the subject. Written in an easy-to-understand style and packed with real life examples, the text clearly explains the statistical principles used in the medical literature.
Taking readers through the common statistical methods seen in published research and guidelines, the text focuses on how to interpret and analyse statistics for clinical practice. Using extracts from real studies, the author illustrates how data can be employed correctly and incorrectly in medical research helping readers to evaluate the statistics they encounter and appropriately implement findings in clinical practice.
End of chapter exercises, case studies and multiple choice questions help readers to apply their learning and develop their own interpretative skills. This thoroughly revised edition includes new chapters on meta-analysis, missing data, and survival analysis.
- Sales Rank: #861551 in eBooks
- Published on: 2015-07-23
- Released on: 2015-07-23
- Format: Kindle eBook
Review
Review from previous edition: "The coverage may not be very different from that of other introductory texts, but in my view the style and content are, and they alone make this text one of the best of its kind. The approach is very data driven, and the use of real data makes this even more appealing...this is an excellent book -- in my view it should be the first choice for any student wanting a serious introduction to the practice of medical statistics." --The Statistician
"If you want to understand some of the statistical ideas important to medicine but fear being overwhelmed by mathematics you will welcome An Introduction to Medical Statistics." --British Medical Journal
"At last I have a book on medical statistics that I can safely recommend to my students!...One of the pleasures of the book is that it contains real data." --Journal of the Royal Statistical Society
"It is a book which I think anyone teaching an introductory course in medical statistics should seriously consider as the main text." --Statistics in Medicine
About the Author
Martin Bland is Professor of Medical Statistics in the Department of Public Health Sciences, St George's Hospital Medical School, London, UK
Most helpful customer reviews
2 of 2 people found the following review helpful.
I really enjoyed this book
By Amazon Customer
I really enjoyed this book. Bland shed light on many issues for me, including non-parametric statistics and number needed to treat - there's more going on there mathematically than meets the eye.
0 of 0 people found the following review helpful.
Fair balance of conceptual explanations vs. mathematical rigor
By Ravindra Arya
Till date I have not found a single best book for applications of statistics and probability to healthcare, but this book is probably the closest. A majority of books on "biostatistics" shy away from mathematics and will focus only on preliminary concepts. Also, I have noticed that many of them have gaps in the presented information and do not build up the subject in a logical order.
Prof. Martin Bland is a recognized authority in the field. He has explained the concepts extremely clearly using real world examples. This book does a very good job at covering nearly the entire spectrum of statistical method used in medical research with emphasis particularly on core things. In the appendices that follow (for example: in the chapter on regression) he has also discussed the mathematical basis for the presented material in a little bit more detail for the interested reader (continuing the same example: the least squares technique).
I guess there is no universal optimal point for conceptual detail vs. mathematical rigor, and I would have personally liked to see more details about different distributions, their CDFs, moment functions, and so on. I believe that the book should assume at least high school level in mathematics i.e. logarithms, calculus, differential equations in one variable, and elementary real analysis. However, this remains a lacuna of the book, in my personal opinion which will not be shared by others. Secondly, in the chapters on Bayesian methods and sample size, again I have felt that the author was torn between the need for comprehensive coverage vs. detail. Though I would have voted for a little bit more detail, I can't argue with issues of having a reasonable size and target audience of the book.
Overall, perhaps one of the best books in the field, thought I could have done with some more mathematical rigor.
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