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 14
Page 8
... cause of turning on the light , but in reality we can define a more intri- cate causal mechanism , in which the switch is one component of several . The tendency to identify the switch as the unique cause stems from its usual role as ...
... cause of turning on the light , but in reality we can define a more intri- cate causal mechanism , in which the switch is one component of several . The tendency to identify the switch as the unique cause stems from its usual role as ...
Page 9
... component factors , or component causes . Each component cause is an event or condi- tion that plays a necessary role in the occurrence of some cases of a given disease . For ... causes It is a strong assertion that. What Is Causation ? 9.
... component factors , or component causes . Each component cause is an event or condi- tion that plays a necessary role in the occurrence of some cases of a given disease . For ... causes It is a strong assertion that. What Is Causation ? 9.
Page 10
... component causes . What would be the ge- netic component causes of someone who gets drunk and is killed in an automobile after colliding with a tree ? It is easy to conceive of genetic traits that lead to psychiatric problems such as ...
... component causes . What would be the ge- netic component causes of someone who gets drunk and is killed in an automobile after colliding with a tree ? It is easy to conceive of genetic traits that lead to psychiatric problems such as ...
Page 11
An Introduction Kenneth J. Rothman. Strength of Causes It is common to think that some component causes play a more impor- tant role than others in the causation of disease . One way this concept is expressed is by the strength of a ...
An Introduction Kenneth J. Rothman. Strength of Causes It is common to think that some component causes play a more impor- tant role than others in the causation of disease . One way this concept is expressed is by the strength of a ...
Page 12
... factors in the same causal mechanism for disease interact with one another to cause disease . Thus , the head trauma interacted with the weather condi- tions as well as with the other component causes , such as the type of footwear ...
... factors in the same causal mechanism for disease interact with one another to cause disease . Thus , the head trauma interacted with the weather condi- tions as well as with the other component causes , such as the type of footwear ...
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 |
Other editions - View all
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