Statistical Models in Epidemiology
1st Edition
Published on 17. January 2013
376 pages
978-0-19-165090-1 (ISBN)
Description
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.
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David Clayton | Michael Hills
Statistical Models in Epidemiology
Book
01/2013
Oxford University Press
€84.10
Shipment within 15-20 days
Persons
David Clayton, Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research
Michael Hills, London School of Hygiene and Tropical Medicine
Content
- 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
