Statistical Models in Epidemiology by David ClaytonStatistical Models in Epidemiology by David Clayton

Statistical Models in Epidemiology

byDavid Clayton, Michael Hills

Paperback | January 29, 2013

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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 statisticalanalysis 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 successfulcourse, this book explains the essential statistics for all epidemiologists.
David Clayton is at the Diabetes and Inflammation Laboratory at the Cambridge Institute for Medical Research. Michael Hills is at the London School of Hygiene and Tropical Medicine, UK.
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Title:Statistical Models in EpidemiologyFormat:PaperbackDimensions:384 pages, 9.21 × 6.14 × 0.03 inPublished:January 29, 2013Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199671184

ISBN - 13:9780199671182

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Table of Contents

I. Probability Models and Likelihood1. Probability models2. Conditional probability models3. Likelihood4. Consecutive follow-up intervals5. Rates6. Time7. Competing risks and selection8. The Gaussian probability model9. Approximate likelihoods10. Likelihood, probability, and confidence11. Null hypotheses and p-values12. Small studies13. Likelihoods for the rate ratio14. Confounding and standardization15. Comparison of rates within strata16. Case-control studies17. Likelihoods for the odds ratio18. Comparison of odds within strata19. Individually matched case-control studies20. Tests for trend21. The size of investigationsII. Regression Models22. Introduction to regression models23. Poission and logistic regression24. Testing hypotheses25. Models for dose-response26. More about interaction27. Choice and interpretation of models28. Additivity and synergism29. Conditional logistic regression30. Cox's regression analysis31. Time-varying explanatory variables32. Three examples33. Nested case-control studies34. Gaussian regression models35. PostscriptIII. AppendicesA. ExponentialsB. Some basic calculusC. Approximate profile likelihoodsD. Table of the Chi-squared distributionIndex

Editorial Reviews

"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