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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 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