Statistical Thinking in Epidemiology

Yu-Kang Tu, Mark S. Gilthorpe

July 27, 2011 by Chapman and Hall/CRC
Reference - 231 Pages - 52 B/W Illustrations
ISBN 9781420099911 - CAT# K10018


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    • Provides an alternative and intuitive understanding of statistical modeling using vector geometry
    • Uses vector geometry extensively to explain the problems with collinearity in linear models and other complex statistical models
    • Introduces a wide range of statistical models as analytical tools: from simple regression, analysis of covariance, multilevel models, latent growth models, growth mixture models to partial least square regression
    • Examples come from real research settings and are discussed in great detail without over-simplification
    • Discusses vital but often poorly understood statistical concepts, such as mathematical coupling, regression to the mean, co-linearity, reversal paradox and statistical interaction


    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.