## 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 vii

The first

sense," unless one has uncommonly good common sense. Although it is unusual

to begin an epidemiology text with a discussion of confounding, I believe that ...

The first

**chapter**illustrates that epidemiology is more than just applying “commonsense," unless one has uncommonly good common sense. Although it is unusual

to begin an epidemiology text with a discussion of confounding, I believe that ...

Page viii

ing epidemiologic effects; these methods are extended in

data.

multivariable modeling. These are subjects to be explored in more advanced ...

ing epidemiologic effects; these methods are extended in

**Chapter**8 to stratifieddata.

**Chapters**9 and 10 address the more advanced topics of interaction andmultivariable modeling. These are subjects to be explored in more advanced ...

Page 1

In this

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 ...

In this

**chapter**, we glimpse some examples of the epidemiologic concept ofconfounding as a way to introduce epidemiologic thinking. Common sense tells

us that residents of Sweden, where the standard of living is generally high,

should ...

Page 4

In

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

only ...

In

**Chapter**8, we return to these data and show how to calculate the effect ofsmoking 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

only ...

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We will explore the proper way to make epidemiologic comparisons in later

approach to a simple problem can be overtly wrong, until we educate our

common sense to ...

We will explore the proper way to make epidemiologic comparisons in later

**chapters**. The point of these examples is to illustrate that a common-senseapproach to a simple problem can be overtly wrong, until we educate our

common sense to ...

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### 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 clinical 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 epidemic epidemiologic epidemiologic study equation 3–1 evaluation example experiment exposed and unexposed exposed group Figure flutamide follow-up formula imbalance 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 tamoxifen tion tolbutamide treatment value function variable women