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 42
Page vii
... Chapter 2. All too often these concepts are skipped over in scientific education . Nevertheless , for epidemiologists they are bedrock concerns that belong in any introduction to the field . Chapter 3 con- tinues with a description of ...
... Chapter 2. All too often these concepts are skipped over in scientific education . Nevertheless , for epidemiologists they are bedrock concerns that belong in any introduction to the field . Chapter 3 con- tinues with a description of ...
Page viii
... Chapter 8 to stratified data . Chapters 9 and 10 address the more advanced topics of interaction and multivariable modeling . These are subjects to be ex- plored in more advanced courses , but their presentation here in elemen- tary ...
... Chapter 8 to stratified data . Chapters 9 and 10 address the more advanced topics of interaction and multivariable modeling . These are subjects to be ex- plored in more advanced courses , but their presentation here in elemen- tary ...
Page 1
... chapter , we glimpse some examples of the epidemiologic concept of confounding as a way to introduce epidemiologic thinking . Common sense tells us that residents of Sweden , where the standard of living is generally high , should have ...
... chapter , we glimpse some examples of the epidemiologic concept of confounding as a way to introduce epidemiologic thinking . Common sense tells us that residents of Sweden , where the standard of living is generally high , should have ...
Page 4
... 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 epidemiologic studies , but it is by no means the ...
... 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 epidemiologic studies , but it is by no means the ...
Page 6
... chapters . The point of these examples is to illustrate that a common - sense approach to a simple problem can be overtly wrong , until we educate our common sense to appreciate better the nature of the problem . Any sensible person can ...
... chapters . The point of these examples is to illustrate that a common - sense approach to a simple problem can be overtly wrong , until we educate our common sense to appreciate better the nature of the problem . Any sensible person can ...
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