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 57
Page 4
... relation in the crude data of Table 1-1 . In Chapter 8 , we return to these data and show how to calculate the effect of smoking on the risk of death after removing the age confounding . Confounding is a problem that pervades many ...
... relation in the crude data of Table 1-1 . In Chapter 8 , we return to these data and show how to calculate the effect of smoking on the risk of death after removing the age confounding . Confounding is a problem that pervades many ...
Page 8
... relations comes the general concept that some events or conditions can be considered causes of other events or conditions . Thus , our first appreciation of the concept of causation is based on our own observations . These observations ...
... relations comes the general concept that some events or conditions can be considered causes of other events or conditions . Thus , our first appreciation of the concept of causation is based on our own observations . These observations ...
Page 14
... relation between exposure of a female fetus to di- ethylstilbestrol ( DES ) and the subsequent development of adenocarci- noma of the vagina . The cancer is usually diagnosed between the ages of 15 and 30 years . Since the causal ...
... relation between exposure of a female fetus to di- ethylstilbestrol ( DES ) and the subsequent development of adenocarci- noma of the vagina . The cancer is usually diagnosed between the ages of 15 and 30 years . Since the causal ...
Page 15
... relation is causal ? Some scientists refer to checklists for causal inference , and others focus on complicated statistical approaches , but the answer to this question is not to be found either in checklists or in statistical methods ...
... relation is causal ? Some scientists refer to checklists for causal inference , and others focus on complicated statistical approaches , but the answer to this question is not to be found either in checklists or in statistical methods ...
Page 16
... relation . The statement about nature will be either reinforced by further observations or refuted by contradictory observations . For example , suppose an in- vestigator in New York conducts an experiment to observe the boiling point ...
... relation . The statement about nature will be either reinforced by further observations or refuted by contradictory observations . For example , suppose an in- vestigator in New York conducts an experiment to observe the boiling point ...
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