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Statistical Models in Epidemiology, by David Clayton, Michael Hills
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This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily.
In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.
- Sales Rank: #1470050 in eBooks
- Published on: 2013-01-17
- Released on: 2013-01-17
- Format: Kindle eBook
Review
"Unlike many textbooks in epidemiology, there is no long wordy preamble. The characteristic style is set straight away. The book is also highly successful in presenting a unified approach. What is also striking, is that the authors have managed to say something useful and clear about many of the all too numerous minor problems that are inevitably encountered in practice. In my view this is simply an excellent text." --Andrew Pickles, Institute of Psychiatry, London, Statistical Methods in Medical Research 1994:3
"An excellent text which provides the simplest and most logical exposition that I have seen of the statistical foundations for current techniques for analysing epidemiological data, and provides an excellent preparation for more detailed treatments." --Australasian Epidemiological Association News, 12/94
"Provides probably the most coherent and logical exposition of the use of statistical models in epidemiology that is currently available ... an excellent text which provides the simplest and most logical exposition that I have seen of the statistical foundations for current techniques for analysing epidemiological data, and provides an excellent preparation for more detailed treatments." --AEA News 12/94
"Clayton and Hills have filled the gap with an interesting text which is based mainly on probability models and likelihood. This is an unusual approach. but is precisely what is missing in many other textbooks for epidemiologists ... this is an important text for those interested in understanding statistical reasoning in epidemiology." --Maria Blettner, International Journal of Epidemiology
"The authors have produced a text that will be extremely valuable to those teaching epidemiologic methods... Statistical Models in Epidemiology courageously cuts new paths into the traditional epidemiologic approach to statistical training." --Journal of the American Statistics Association
"This book gives some very clear explanations ... Each point is well illustrated with small examples and there are exercises throughout. It is pleasing to see full solution to all the exercises." --Public Health (1994) 108
About the Author
David Clayton, Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research
Michael Hills, London School of Hygiene and Tropical Medicine
Most helpful customer reviews
3 of 3 people found the following review helpful.
Outstanding mongraph for biostatisticians
By Stefan Ma
I find all other so-called biostatistics books are helpless
until I have read this book. This book is written based on
Clayton and Hills's past experience in applying statistics
in their studies. They can illustrate most difficult concepts
by very simple examples and words which are totally differnt
than other text and teaching books. For other books, you
can learn a lot of various statistical techniques, but you
cannot understand what is the core idea behind the statistics.
You may get it from this book.
1 of 1 people found the following review helpful.
Clearly illustrates challenging concepts
By Sunny
The purpose of this book is to illustrate fundamental concepts involved in statistical epidemiology. It is not a data analysis book. Very few texts in statistics are able to clearly explain difficult concepts (in my opinion). This book is a rare exception (and Agresti, 2002, 2007). For example, it made it easy to understand conditional likelihood. In addition, the text provides problems throughout each section, and provides detailed solutions for each one.
0 of 0 people found the following review helpful.
Excellent Text on Foundations
By Soluman B Culver
This is an excellent textbook which provides foundational principles for much of the mathematics of epidemiology. The book is fundamentally one about understanding concepts, rather than practical applications. Beginning with the concept of likelihood models, the authors explain parameter estimation, survival analysis, and regression models in a way that is simple and intuitive. Recommended for any researcher in epidemiology as well as interested scientists and clinicians.
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