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
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Page 2
... Data Base . a given year , despite the lower death rates within age ... data , summarized from a study that looked at smoking habits of residents of Whickham , England , in the ... Risk of death in a 20 -. 2 Epidemiology : An Introduction.
... Data Base . a given year , despite the lower death rates within age ... data , summarized from a study that looked at smoking habits of residents of Whickham , England , in the ... Risk of death in a 20 -. 2 Epidemiology : An Introduction.
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... Risk ( dead / total ) 0.24 0.31 0.28 * Data from Vanderpump et al . " this difference indicate that women who were smokers fared better than women who were not smokers ? Not necessarily . One difficulty that many readers quickly spot is ...
... Risk ( dead / total ) 0.24 0.31 0.28 * Data from Vanderpump et al . " this difference indicate that women who were smokers fared better than women who were not smokers ? Not necessarily . One difficulty that many readers quickly spot is ...
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... Risk 0.81 0.78 0.79 Dead 13 64 77 75+ Alive 0 0 0 Risk 1.00 1.00 1.00 * Data from Vanderpump et al . ing the study in Whickham , there was a trend for increasing proportions of young women to become smokers . The oldest women in the ...
... Risk 0.81 0.78 0.79 Dead 13 64 77 75+ Alive 0 0 0 Risk 1.00 1.00 1.00 * Data from Vanderpump et al . ing the study in Whickham , there was a trend for increasing proportions of young women to become smokers . The oldest women in the ...
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... data at all . Furthermore , the reporter's observation has problems that go beyond the reliance on anecdotes instead ... risk of death among orchestra conductors with the risk of death among other people who have attained the same age as ...
... data at all . Furthermore , the reporter's observation has problems that go beyond the reliance on anecdotes instead ... risk of death among orchestra conductors with the risk of death among other people who have attained the same age as ...
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... risk of death . When one looks at the average age at death , one looks only at those who actually die and ignores ... data in Table 1-2 to plot the 20 - year risk of death against age . Put age on the horizontal axis and the 20- year ...
... risk of death . When one looks at the average age at death , one looks only at those who actually die and ignores ... data in Table 1-2 to plot the 20 - year risk of death against age . Put age on the horizontal axis and the 20- year ...
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