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Modern Methods for Epidemiology

  • Book
  • © 2012

Overview

Editors:
  1. Yu-Kang Tu

    1. Leeds Institute of Genetics, University of Leeds, Division of Biostatistics, Leeds, United Kingdom

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    You can also search for this editor in PubMed   Google Scholar

  2. Darren C. Greenwood

    1. Leeds Institute of Genetics, University of Leeds, Division of Biostatistics, Leeds, United Kingdom

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    You can also search for this editor in PubMed   Google Scholar

  • Discussion of modern statistical methods for epidemiology and biomedical research that cannot be found in most textbooks, e.g. latent growth curve models, latent class analysis, growth mixture models, regression trees, generalised additive models, and G-estimation etc.
  • Written by experts in accessible language for upper-level undergraduate and postgraduate students in epidemiology and biostatistics
  • Real life data sets are used extensively in the book to illustrate how these method may be applied in real research
  • Each chapter contains a section for further reading list for readers who wish to pursue these methods in greater details

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xii
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  2. Confounding and Causal Path Diagrams

    • Graham R. Law, Rosie Green, George T. H. Ellison
    Pages 1-13

  3. Statistical Modelling of Partially Observed Data Using Multiple Imputation: Principles and Practice

    • James R. Carpenter, Harvey Goldstein, Michael G. Kenward
    Pages 15-31

  4. Measurement Errors in Epidemiology

    • Darren C. Greenwood
    Pages 33-55

  5. Selection Bias in Epidemiologic Studies

    • Graham R. Law, Paul D. Baxter, Mark S. Gilthorpe
    Pages 57-71

  6. Multilevel Modelling

    • Andrew Blance
    Pages 73-91

  7. Modelling Data That Exhibit an Excess Number of Zeros: Zero-Inflated Models and Generic Mixture Models

    • Mark S. Gilthorpe, Morten Frydenberg, Yaping Cheng, Vibeke Baelum
    Pages 93-115

  8. Multilevel Latent Class Modelling

    • Wendy Harrison, Robert M. West, Amy Downing, Mark S. Gilthorpe
    Pages 117-140

  9. Bayesian Bivariate Disease Mapping

    • Richard G. Feltbower, Samuel O. M. Manda
    Pages 141-155

  10. A Multivariate Random Frailty Effects Model for Multiple Spatially Dependent Survival Data

    • Samuel O. M. Manda, Richard G. Feltbower, Mark S. Gilthorpe
    Pages 157-172

  11. Meta-analysis of Observational Studies

    • Darren C. Greenwood
    Pages 173-189

  12. Directed Acyclic Graphs and Structural Equation Modelling

    • Yu-Kang Tu
    Pages 191-203

  13. Latent Growth Curve Models

    • Yu-Kang Tu, Francesco D’Auito
    Pages 205-221

  14. Growth Mixture Modelling for Life Course Epidemiology

    • Darren L. Dahly
    Pages 223-241

  15. G-estimation for Accelerated Failure Time Models

    • Kate Tilling, Jonathan A. C. Sterne, Vanessa Didelez
    Pages 243-260

  16. Generalised Additive Models

    • Robert M. West
    Pages 261-278

  17. Regression and Classification Trees

    • Robert M. West
    Pages 279-290

  18. Statistical Interactions and Gene-Environment Joint Effects

    • Mark S. Gilthorpe, David G. Clayton
    Pages 291-311

  19. Back Matter

    Pages 313-314
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Keywords

About this book

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 behind those methods remains essential for correct application and interpretation, and rarely seen in the medical literature. Modern Methods for Epidemiology provides a concise introduction to recent development in statistical methodologies for epidemiological and biomedical researchers. Many of these methods have become indispensible tools for researchers working in epidemiology and medicine but are rarely discussed in details by standard textbooks of biostatistics or epidemiology. Contributors of this book are experienced researchers and experts in their respective fields. This textbook provides a solid starting point for those who are new to epidemiology, and for those looking for guidance in more modern statistical approaches to observational epidemiology. Epidemiological and biomedical researchers who wish to overcome the mathematical barrier of applying those methods to their research will find this book an accessible and helpful reference for self-learning and research. This book is also a good source for teaching postgraduate students in medical statistics or epidemiology.

Editors and Affiliations

  • Leeds Institute of Genetics, University of Leeds, Division of Biostatistics, Leeds, United Kingdom

    Yu-Kang Tu, Darren C. Greenwood

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