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Statistical Thinking in Epidemiology
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Statistical Thinking in Epidemiology

Book Details

Format Hardback or Cased Book
ISBN-10 1420099914
ISBN-13 9781420099911
Publisher Taylor & Francis Ltd
Imprint Chapman & Hall/CRC
Country of Manufacture US
Country of Publication GB
Publication Date Jul 27th, 2011
Print length 232 Pages
Weight 554 grams
Dimensions 24.50 x 16.60 x 2.10 cms
Ksh 21,600.00
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Provides insights into the problems in statistical modeling and offers alternative strategies to approach the problems. Emphasizing the importance of statistical thinking behind all analyses, this book uses examples in epidemiology to illustrate different model specifications that can imply different sets of causal relationships between variables.

While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction.



Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking.



Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling.


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