This self-contained account of the statistical basis of epidemiology has been written for those with a basic training in biology. No previous knowledge of the subject is assumed and mathematics is deliberately kept at a manageable level. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.
This self-contained account of the statistical basis of epidemiology has been written for those with a basic training in biology. No previous knowledge of the subject is assumed and mathematics is deliberately kept at a manageable level. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
David Clayton, Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research Michael Hills, London School of Hygiene and Tropical Medicine
Inhaltsangabe
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
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
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