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Questions About This Book?
What version or edition is this?
This is the Reprint edition with a publication date of 3/1/2013.
What is included with this book?
- The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc.
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.
Table of Contents
I. Probability Models and Likelihood
1. Probability models
2. Conditional probability models
4. Consecutive follow-up intervals
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
B. Some basic calculus
C. Approximate profile likelihoods
D. Table of the Chi-squared distribution