Statistical Models in Epidemiology

Overview


This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily.

In showing how to use models in epidemiology the authors have chosen to emphasize the...

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Overview


This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily.

In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.

This book contains black-and-white illustrations.

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Editorial Reviews

Laura A. Schieve
This book provides a statistical foundation for performing epidemiologic analyses, beginning with a basic explanation of probability and likelihood function in early chapters and expanding on these concepts in later chapters to illustrate their application in epidemiology. The authors' stated objective is to present a basis for understanding statistical models commonly used in epidemiology. This goal is certainly laudable, because epidemiology students often have difficulty incorporating their knowledge of statistical theory with the more applied concepts taught in epidemiology courses. However, if viewed alone, this book falls somewhat short of achieving this goal, because both relevant theoretical and applied concepts are excluded as a result of combining the two disciplines. The authors certainly seem credible authorities on the subject matter; both have affiliations with respected institutions and together they have taught numerous courses in epidemiology. However, although the book is intended for masters degree students in epidemiology or biostatistics with no previous statistical knowledge, the ideas presented become increasingly complex throughout the book and would seem arduous for a student at a beginning level. This book contains many diagrams that are very useful in illustrating complicated ideas. The table of contents and index are complete and accurate. There are numerous short chapters that focus on one key concept, a style more effective than combining many intricate and often confusing ideas together into a single chapter. The ordering of topics, however, is better suited for a statistical than for an epidemiological mind set. Important concepts are not always introducedin a sequence consistent with planning and undertaking an epidemiologic analysis. For example, the concept of statistical interaction should always be considered when evaluating any stratified analysis, yet it is not presented until much later in the regression analysis section. This book is most useful for an intermediate course in epidemiology. This book has several shortcomings that could be overcome by using this book as a supplement to other epidemiology texts rather than as the sole text. Additionally, this book does not replace a solid background in statistical theory. It does, however, help to bridge the gap between statistics and epidemiology, and thus provides a unique perspective.
Doody's Review Service
Reviewer: Laura A. Schieve, MS (University of Illinois at Chicago)
Description: This book provides a statistical foundation for performing epidemiologic analyses, beginning with a basic explanation of probability and likelihood function in early chapters and expanding on these concepts in later chapters to illustrate their application in epidemiology.
Purpose: The authors' stated objective is to present a basis for understanding statistical models commonly used in epidemiology. This goal is certainly laudable, because epidemiology students often have difficulty incorporating their knowledge of statistical theory with the more applied concepts taught in epidemiology courses. However, if viewed alone, this book falls somewhat short of achieving this goal, because both relevant theoretical and applied concepts are excluded as a result of combining the two disciplines.
Audience: The authors certainly seem credible authorities on the subject matter; both have affiliations with respected institutions and together they have taught numerous courses in epidemiology. However, although the book is intended for masters degree students in epidemiology or biostatistics with no previous statistical knowledge, the ideas presented become increasingly complex throughout the book and would seem arduous for a student at a beginning level.
Features: This book contains many diagrams that are very useful in illustrating complicated ideas. The table of contents and index are complete and accurate. There are numerous short chapters that focus on one key concept, a style more effective than combining many intricate and often confusing ideas together into a single chapter. The ordering of topics, however, is better suited for a statistical than for an epidemiological mind set. Important concepts are not always introduced in a sequence consistent with planning and undertaking an epidemiologic analysis. For example, the concept of statistical interaction should always be considered when evaluating any stratified analysis, yet it is not presented until much later in the regression analysis section.
Assessment: This book is most useful for an intermediate course in epidemiology. This book has several shortcomings that could be overcome by using this book as a supplement to other epidemiology texts rather than as the sole text. Additionally, this book does not replace a solid background in statistical theory. It does, however, help to bridge the gap between statistics and epidemiology, and thus provides a unique perspective.

3 Stars from Doody
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Product Details

  • ISBN-13: 9780199671182
  • Publisher: Oxford University Press, USA
  • Publication date: 3/1/2013
  • Edition description: Reprint
  • Pages: 384
  • Sales rank: 1,120,150
  • Product dimensions: 6.10 (w) x 9.10 (h) x 0.90 (d)

Meet the Author

David Clayton, Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research

Michael Hills, London School of Hygiene and Tropical Medicine

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Table of Contents

I. Probability Models and Likelihood
1. Probability models
2. Conditional probability models
3. Likelihood
4. Consecutive follow-up intervals
5. Rates
6. Time
7. Competing risks and selection
8. The Gaussian probability model
9. Approximate likelihoods
10. Likelihood, probability, and confidence
11. Null hypotheses and p-values
12. Small studies
13. Likelihoods for the rate ratio
14. Confounding and standardization
15. Comparison of rates within strata
16. Case-control studies
17. Likelihoods for the odds ratio
18. Comparison of odds within strata
19. Individually matched case-control studies
20. Tests for trend
21. The size of investigations
II. Regression Models
22. Introduction to regression models
23. Poission and logistic regression
24. Testing hypotheses
25. Models for dose-response
26. More about interaction
27. Choice and interpretation of models
28. Additivity and synergism
29. Conditional logistic regression
30. Cox's regression analysis
31. Time-varying explanatory variables
32. Three examples
33. Nested case-control studies
34. Gaussian regression models
35. Postscript
III. Appendices
A. Exponentials
B. Some basic calculus
C. Approximate profile likelihoods
D. Table of the Chi-squared distribution
Index

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