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 16
... experiment to observe the boiling point of water and observes that the water boils at 100 ° C . The experi- ment might be repeated many times , each time showing that the water boils at about 100 ° C . By induction , the investigator ...
... experiment to observe the boiling point of water and observes that the water boils at 100 ° C . The experi- ment might be repeated many times , each time showing that the water boils at about 100 ° C . By induction , the investigator ...
Page 17
... experiments showing that water boils at 100 ° C corrob- orate the hypothesis that water boils at this temperature ... experiment at sea level . The asymmetrical implications of a refuting observation , on the one hand , and supporting ...
... experiments showing that water boils at 100 ° C corrob- orate the hypothesis that water boils at this temperature ... experiment at sea level . The asymmetrical implications of a refuting observation , on the one hand , and supporting ...
Page 19
... Experimental evidence 9. Analogy Problems with the criterion Strength depends on the prevalence of other causes and , thus , is not a biologic characteristic ; could be confounded Exceptions are understood best with hindsight A cause ...
... Experimental evidence 9. Analogy Problems with the criterion Strength depends on the prevalence of other causes and , thus , is not a biologic characteristic ; could be confounded Exceptions are understood best with hindsight A cause ...
Page 20
... ( experimental evidence ) . The only criterion on the list that is truly a causal criterion is temporality , which implies that the cause comes before the effect . This criterion , which is part of the definition of a cause , is useful to ...
... ( experimental evidence ) . The only criterion on the list that is truly a causal criterion is temporality , which implies that the cause comes before the effect . This criterion , which is part of the definition of a cause , is useful to ...
Page 27
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Contents
1 | |
8 | |
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