Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation (Chapman & Hall/CRC Interdisciplinary Statistics)

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9781466573178: Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation (Chapman & Hall/CRC Interdisciplinary Statistics)

Presents the Terminology and Methods of Mendelian Randomization for Epidemiological Studies

Mendelian randomization uses genetic instrumental variables to make inferences about causal effects based on observational data. It, therefore, can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disease outcomes.

Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation provides thorough coverage of the methods and practical elements of Mendelian randomization analysis. It brings together diverse aspects of Mendelian randomization spanning epidemiology, statistics, genetics, and econometrics. Although the book mainly focuses on epidemiology, much of the material can be applied to other areas of research.

Through several examples, the first part of the book shows how to perform simple applied Mendelian randomization analyses and interpret their results. The second part addresses specific methodological issues, such as weak instruments, multiple instruments, power calculations, and meta-analysis, relevant to practical applications of Mendelian randomization. In this part, the authors draw on data from the C-reactive protein Coronary heart disease Genetics Collaboration (CCGC) to illustrate the analyses. They present the mathematics in an easy-to-understand way by using nontechnical language and reinforcing key points at the end of each chapter. The last part of the book examines the potential of Mendelian randomization in the future, exploring both methodological and applied developments.

This book gives statisticians, epidemiologists, and geneticists the foundation to understand issues concerning the use of genetic variants as instrumental variables. It will get them up to speed in undertaking and interpreting Mendelian randomization analyses. Chapter summaries, paper summaries, web-based applications, and software code for implementing the statistical techniques are available on a supplementary website.

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Review:

"The authors have aimed their book at epidemiologists and medical statisticians but anyone with a basic knowledge of regression will understand most of the contents, because the algebra is kept to a minimum and emphasis is on explaining the ideas that underlie MR...will serve as an excellent introduction to Mendelian randomization for anyone who wants to understand the underlying statistical issues..."
―John Thompson, University of Leicester, in Biometrics, March 2017

"Mendelian Randomization, by Stephen Burgess and Simon Thompson, represents a compact and accessible resource for Mendelian randomization, providing exactly what one needs to know in a logical, clear, very thorough, and yet pragmatic way. This book will appeal to applied researchers interested in learning more about Mendelian randomization as well as methodological researchers who work in the area. Those new to the field will find that this book covers everything they need to know, from designing the study (e.g., choosing the "instruments" and considering different options for pooling data across multiple studies) to investigating whether the assumptions may hold, analyzing the data, and interpreting the results... Researchers interested in methods for Mendelian randomization will find this book equally useful in bringing together methodological findings published across different disciplines (genetics, epidemiology, statistics, and econometrics), in an articulate and comprehensive way... Being targeted to epidemiologists and medical statisticians with diverse backgrounds, no prior knowledge of genetics is required, and the book explains in very simple terms the basic concepts needed to understand and apply Mendelian randomization... I really enjoyed reading this book and highly recommend it to anyone with an interest in Mendelian randomization."
―Cosetta Minelli, Imperial College London, in The American Statistician, August 2016

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