Statistical Models in Epidemiology [NOOK Book]

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 ...

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Statistical Models in Epidemiology

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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: 9780191650918
  • Publisher: OUP Oxford
  • Publication date: 7/8/1993
  • Sold by: Barnes & Noble
  • Format: eBook
  • File size: 12 MB
  • Note: This product may take a few minutes to download.

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

1 Probability models 3
2 Conditional probability models 10
3 Likelihood 18
4 Consecutive follow-up intervals 27
5 Rates 40
6 Time 53
7 Competing risks and selection 63
8 The Gaussian probability model 71
9 Approximate likelihoods 78
10 Likelihood, probability, and confidence 89
11 Null hypotheses and p-values 96
12 Small studies 110
13 Likelihoods for the rate ratio 122
14 Confounding and standardization 133
15 Comparison of rates within strata 141
16 Case-control studies 153
17 Likelihoods for the odds ratio 166
18 Comparison of odds within strata 175
19 Individually matched case-control studies 186
20 Tests for trend 197
21 The size of investigations 205
22 Introduction to regression models 217
23 Poisson and logistic regression 227
24 Testing hypotheses 237
25 Models for dose-response 249
26 More about interaction 261
27 Choice and interpretation of models 271
28 Additivity and synergism 282
29 Conditional logistic regression 290
30 Cox's regression analysis 298
31 Time-varying explanatory variables 307
32 Three examples 319
33 Nested case-control studies 329
34 Gaussian regression models 336
35 Postscript 346
A. Exponentials and logarithms 351
B. Some basic calculus 354
C. Approximate profile likelihoods 357
D. Table of the chi-squared distribution 363
Index 365
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