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. |
From inside the book
Results 1-5 of 58
Page 5
... Suppose that the reporter had identified all orchestra conductors who worked in the United States during the past 100 years and studied their longevity . This approach would avoid relying on hand - picked examples , but it still suf ...
... Suppose that the reporter had identified all orchestra conductors who worked in the United States during the past 100 years and studied their longevity . This approach would avoid relying on hand - picked examples , but it still suf ...
Page 8
... Suppose the electric lines to the building are down in a storm . Turning on the switch will have no effect . Suppose the bulb is burned out . Again , the switch will have no effect . One cause of the light going on is having the switch ...
... Suppose the electric lines to the building are down in a storm . Turning on the switch will have no effect . Suppose the bulb is burned out . Again , the switch will have no effect . One cause of the light going on is having the switch ...
Page 10
... Suppose that someone experi- ences a traumatic injury to the head that leads to a permanent distur- bance in equilibrium . Many years later , the faulty equilibrium plays a causal role in a fall that occurs while the person is walking ...
... Suppose that someone experi- ences a traumatic injury to the head that leads to a permanent distur- bance in equilibrium . Many years later , the faulty equilibrium plays a causal role in a fall that occurs while the person is walking ...
Page 11
... suppose we say that smoking is a strong cause of lung cancer because it plays a causal role in a large proportion of cases . Exposure to ambient radon gas , in contrast , is a weaker cause because it has a causal role in a much smaller ...
... suppose we say that smoking is a strong cause of lung cancer because it plays a causal role in a large proportion of cases . Exposure to ambient radon gas , in contrast , is a weaker cause because it has a causal role in a much smaller ...
Page 12
... Suppose that the differences in the rates reflect causal effects . Among those who are smokers and alcohol drinkers , what proportion of cases of head and neck cancer that occur is attributable to the effect of smoking ? We know that ...
... Suppose that the differences in the rates reflect causal effects . Among those who are smokers and alcohol drinkers , what proportion of cases of head and neck cancer that occur is attributable to the effect of smoking ? We know that ...
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