Epidemiology: An IntroductionIn the past thirty years epidemiology has matured from a fledgling scientific field into a vibrant discipline that brings together the biological and social sciences, and in doing so draws upon disciplines ranging from statistics and survey sampling to the philosophy of science. These areas of knowledge have converged into a modern theory of epidemiology that has been slow to penetrate into textbooks, particularly at the introductory level. Epidemiology: An Introduction closes the gap. It begins with a brief, lucid discussion of causal thinking and causal inference and then takes the reader through the elements of epidemiology, focusing on the measures of disease occurrence and causal effects. With these building blocks in place, the reader learns how to design, analyze and interpret problems that epidemiologists face, including confounding, the role of chance, and the exploration of interactions. All these topics are layered on the foundation of basic principles presented in simple language, with numerous examples and questions for further thought. |
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... study in Whickham , there was a trend for increasing proportions of young women to become smokers . The oldest women ... epidemiologic studies , but it is by no means the only issue that bedevils epidemiologic infer- ences . One day ...
... study in Whickham , there was a trend for increasing proportions of young women to become smokers . The oldest women ... epidemiologic studies , but it is by no means the only issue that bedevils epidemiologic infer- ences . One day ...
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... studied . Here is another example that makes this point clearly . Suppose that we study two groups of people and look at the average age at death among those who die . In group A , the average age at death is 4 years ; in group B , it ...
... studied . Here is another example that makes this point clearly . Suppose that we study two groups of people and look at the average age at death among those who die . In group A , the average age at death is 4 years ; in group B , it ...
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... study of factory workers , an investigator inferred that the factory work ... epidemiologic comparisons in later chapters . The point of these examples is ... epidemiologic princi- ples , it is possible to refine our common sense to avoid ...
... study of factory workers , an investigator inferred that the factory work ... epidemiologic comparisons in later chapters . The point of these examples is ... epidemiologic princi- ples , it is possible to refine our common sense to avoid ...
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... epidemiologic research is aimed at uncovering the causes of disease . Now that we have a conceptual model for causes , how do we go about determining whether a given relation is causal ? Some scientists refer to checklists for causal ...
... epidemiologic research is aimed at uncovering the causes of disease . Now that we have a conceptual model for causes , how do we go about determining whether a given relation is causal ? Some scientists refer to checklists for causal ...
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Contents
1 | |
8 | |
3 Measuring Disease Occurrence and Causal Effects | 24 |
4 Types of Epidemiologic Study | 57 |
5 Biases in Study Design | 94 |
6 Random Error and the Role of Statistics | 113 |
7 Analyzing Simple Epidemiologic Data | 130 |
8 Controlling Confounding by Stratifying Data | 144 |
9 Measuring Interactions | 168 |
10 Using Regression Models in Epidemiologic Analysis | 181 |
11 Epidemiology in Clinical Settings | 198 |
Appendix | 218 |
Index | 221 |
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Common terms and phrases
age categories asbestos attributable fraction average baseline bias biologic interaction birth order breast cancer calculate case-control data case-control study causal causal mechanism Chapter cigarette smoking clozapine cohort study compared component causes confidence interval confidence limits control confounding control series crude data curve data in Table dence rate denominator described distribution drug epidemic epidemiologic epidemiologic study evaluation example experiment exposed and unexposed exposed group Figure flutamide follow-up formula imbalance inci incidence proportion incidence rate ratio inference laryngeal cancer leukemia lung cancer measure misclassification mortality rate myocardial infarction nonsmokers null hypothesis obtain occur odds ratio outcome patients person person-time person-years placebo pooled estimate population at risk predict prevalence random error randomized trial rate difference relation result risk data risk difference risk factor risk of death risk ratio Rothman sampling screening source population specific standard strata stratified analysis Suppose syndrome tamoxifen tion tolbutamide treatment value function variable women