Statistical Models in Epidemiology

• 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

'Unlike many textbooks in epidemiology, there is no long wordy preamble. The characteristic style is set straight away. The book is also highly successful in presenting a unified approach. What is also striking, is that the authors have managed to say something useful and clear about many of the all too numerous minor problems that are inevitably encountered in practice. In my view this is simply an excellent text.' Andrew Pickles, Institute of Psychiatry, London, Statistical Methods in Medical Research 1994:3 an excellent text which provides the simplest and most logical exposition that I have seen of the statistical foundations for current techniques for analysing epidemiological data, and provides an excellent preparation for more detailed treatments Australasian Epidemiological Association News, 12/94 provides probably the most coherent and logical exposition of the use of statistical models in epidemiology that is currently available ... an excellent text which provides the simplest and most logical exposition that I have seen of the statistical foundations for current techniques for analysing epidemiological data, and provides an excellent preparation for more detailed treatments. AEA News 12/94 Clayton and Hill have filled the gap with an interesting text which is based mainly on probability models and likelihood. This is an unusual approach. but is precisely what is missing in many other textbooks for epidemiologists ... this is an important text for those interested in understanding statistical reasoning in epidemiology. Maria Blettner, International Journal of Epidemiology The book covers virtually the whole field of basic epidemiological techniques. They are summarized clearly and succinctly ... Reading this book made me feel that I was sitting listening to someone who has a wide grasp of practical modelling, a firm basis in theory and a gift for clear explanation. It is difficult to recommend it too highly. The authors have produced a text that will be extremely valuable to those teaching epidemiologic methods... Statistical Models in Epidemiology courageously cuts new paths into the traditional epidemiologic approach to statistical training. Journal of the American Statistics Association this book gives some very clear explanations ... Each point is well illustrated with small examples and there are exercises throughout. It is pleasing to see full solution to all the exercises. Public Health (1994) 108