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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Page 10
... time , it is a good approximation . It may seem counterintuitive because most of the time we cannot manipulate many ... person is walking on an icy path . The fall results in a broken hip . Other factors playing a causal role for the broken ...
... time , it is a good approximation . It may seem counterintuitive because most of the time we cannot manipulate many ... person is walking on an icy path . The fall results in a broken hip . Other factors playing a causal role for the broken ...
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... time . Consider the example above of the person who sustained trauma to the head that resulted in an equilib- rium disturbance , which led years later to a fall on an icy path . The earlier head trauma played a causal role in the later ...
... time . Consider the example above of the person who sustained trauma to the head that resulted in an equilib- rium disturbance , which led years later to a fall on an icy path . The earlier head trauma played a causal role in the later ...
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... person - years ) according to smoking status and alcohol drinking Smoking Status Nonsmoker Smoker Nondrinker 1 4 ... Time Because the component causes in a given causal mechanism do not act simultaneously , there will usually be a period of ...
... person - years ) according to smoking status and alcohol drinking Smoking Status Nonsmoker Smoker Nondrinker 1 4 ... Time Because the component causes in a given causal mechanism do not act simultaneously , there will usually be a period of ...
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... time , and the time of occurrence is considered part of the definition of an event . Second , epilepsy will occur later only if the person survives an additional 8 years , which is not certain . Therefore , agent B determines when the ...
... time , and the time of occurrence is considered part of the definition of an event . Second , epilepsy will occur later only if the person survives an additional 8 years , which is not certain . Therefore , agent B determines when the ...
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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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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