Book DetailAuthor/Editor(s): Yu-Kang Tu, Mark S. Gilthorpe
Publication Date: July 27, 2011
Publisher: Chapman and Hall/CRC
Size: 3.40 MB
Book DescriptionWhile 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.
… this book is enjoyable … it encourages readers to conceptualize statistical thinking in a graphically entertaining way. … one of the impressive works of the book lies in visualization of statistically important concepts.… I would recommend this book to diverse audiences. … the book provides novel insight on how one can develop the core concepts from scratch via graphical concepts, which will definitely be beneficial. Bearing in mind the geometrical concepts from this book, statistical thinking of more complicated models is readily welcomed.
--Journal of Agricultural, Biological, and Environmental Statistics
There are extensive references to the literature, both in statistics and in medicine. This is a demanding text, not mathematically but for the subtlety of the issues canvassed, some of which remain controversial. Should any reader come to this text thinking that the interpretation of regression results is a simple matter, they will be quickly disabused.
--International Statistical Review
The graphical explanations proposed are quite convincing and these tools should be more exploited in statistical classes.
--Sophie Donnet, Université Paris-Dauphine