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Statistical Thinking in Epidemiology
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Modern Methods for Epidemiology
Yu-Kang Tu, Darren C Greenwood
Routine applications of advanced statistical methods on real data have become possible in the last ten years because desktop computers have become much more powerful and cheaper. However, proper understanding of the challenging statistical theory ...
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..". 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, Volume 20, Number 2, 2015 "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, 2013 "The graphical explanations proposed are quite convincing and these tools should be more exploited in statistical classes." --Sophie Donnet, Universite Paris-Dauphine, CHANCE, 25.4
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Dr Yu-Kang Tu is a Senior Clinical Research Fellow in the Division of Biostatistics, School of Medicine, and in the Leeds Dental Institute, University of Leeds, Leeds, UK. He was a visiting Associate Professor to the National Taiwan University, Taipei, Taiwan. First trained as a dentist and then an epidemiologist, he has published extensively in dental, medical, epidemiological and statistical journals. He is interested in developing statistical methodologies to solve statistical and methodological problems such as mathematical coupling, regression to the mean, collinearity and the reversal paradox. His current research focuses on applying latent variables methods, e.g. structural equation modeling, latent growth curve modelling, and lifecourse epidemiology. More recently, he has been working on applying partial least squares regression to epidemiological data. Prof Mark S Gilthorpe is professor of Statistical Epidemiology, Division of Biostatistics, School of Medicine, University of Leeds, Leeds, UK. Having completed a single honours degree in mathematical Physics (University of Nottingham), he undertook a PhD in Mathematical Modelling (University of Aston in Birmingham), before initially embarking upon a career as self-employed Systems and Data Analyst and Computer Programmer, and eventually becoming an academic in biomedicine. Academic posts include systems and data analyst of UK regional routine hospital data in the Department of Public Health and Epidemiology, University of Birmingham; Head of Biostatistics at the Eastman Dental Institute, University College London; and founder and Head of the Division of Biostatistics, School of Medicine, University of Leeds. His research focus has persistently been that of the development and promotion of robust and sophisticated modelling methodologies for non-experimental (and sometimes large and complex) observational data within biomedicine, leading to extensive publications in
Introduction. Vector Geometry of Linear Models for Epidemiologists. Path Diagrams and Directed Acyclic Graphs. Mathematical Coupling and Regression to the Mean in the Relation between Change and Initial Value. Analysis of Change in Pre-/Post-Test Studies. Collinearity and Multicollinearity. Is `Reversal Paradox' a Paradox? Testing Statistical Interaction. Finding Growth Trajectories in Lifecourse Research. Partial Least Squares Regression for Lifecourse Research. Concluding Remarks. References. Index.