期刊全称 | A Pocket Guide to Epidemiology | 影响因子2023 | David G. Kleinbaum,Kevin M. Sullivan,Nancy D. Bark | 视频video | http://file.papertrans.cn/142/141713/141713.mp4 | 发行地址 | In the nearly three years since the publication of the ActivEpi companion text, the authors received several suggestions to produce an abbreviated version that narrows the discussion to the most "esse | 图书封面 |  | 影响因子 | .A Pocket Guide to Epidemiology. is a stand-alone introductory text on the basic principles and concepts of epidemiology. The primary audience for this text is the public health student or professional, clinician, health journalist, and anyone else at any age or life experience that is interested in learning what epidemiology is all about in a convenient, easy to understand format with timely, real-world health examples. | Pindex | Textbook 2007 |
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Front Matter |
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Abstract
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,A Pocket-Size Introduction, |
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Abstract
Epidemiology is the study of health and illness in human populations. For example, a randomized clinical trial conducted by Epidemiologists at the Harvard School of Public Health showed that taking aspirin reduces heart attack risk by 20 to 30 percent. Public health studies in the 1950’s demonstrated that smoking cigarettes causes lung cancer. Environmental epidemiologists have been evaluating the evidence that living near power lines may have a high risk for childhood leukemia. Cancer researchers wonder why older women are less likely to be screened for breast cancer than younger women. All of these are examples of epidemiologic research, because they all attempt to describe the relationship between a health outcome and one or more explanations or causes of that outcome. All of these examples share several challenges: they must choose an appropriate study design, they must be careful to avoid bias, and they must use appropriate statistical methods to analyze the data. Epidemiology deals with each of these three challenges.
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,The Big Picture - with Examples, |
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Abstract
The field of epidemiology was initially concerned with providing a methodological basis for the study and control of population epidemics. Now, however, epidemiology has a much broader scope, including the study of both acute and chronic diseases, the quality of health care, and mental health problems. As the focus of epidemiologic inquiry has broadened, so has the methodology. In this overview chapter, we describe examples of epidemiologic research and introduce several important methodological issues typically considered in such research.
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,How to Set Things Up? Study Designs, |
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Abstract
A key stage of epidemiologic research is the .. This is defined to be the process of planning an empirical investigation to assess a . about the relationship between one or more . and a .. The purpose of the study design is to transform the conceptual hypothesis into an . that can be empirically tested. Since all study designs are potentially flawed, it is therefore important to understand the specific strengths and limitations of each design. Most serious problems or mistakes at this stage cannot be rectified in subsequent stages of the study.
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,How Often does it Happen? Disease Frequency, |
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Abstract
In epidemiologic studies, we use a . to determine how often the disease or other health outcome of interest occurs in various subgroups of interest. We describe two basic types of measures of disease frequency in this chapter, namely, measures of incidence and measures of prevalence. The choice typically depends on the study design being used and the goal of the study.
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,What’s the Answer? Measures of Effect, |
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Abstract
In epidemiologic studies, we compare disease frequencies of two or more groups using a .. We will describe several types of measures of effect in this chapter. The choice of measure typically depends on the study design being used.
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,What is the Public Health Impact?, |
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Abstract
In the previous chapter on Measures of Effect, we focused exclusively on . In this chapter, we consider . and other related measures that allow the investigator to consider the . public health . of the results obtained from an epidemiologic study.
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,Is There Something Wrong? Validity and Bias, |
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The primary objective of most epidemiologic research is to obtain a . of an . of interest. In this chapter we illustrate three general types of . problems, distinguish validity from ., introduce the term ., and discuss how to adjust for bias.
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,Were Subjects Chosen Badly? Selection Bias, |
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. concerns systematic error that may arise from the manner in which subjects are selected into one’s study. In his chapter we describe examples of selection bias, provide a quantitative framework for assessing selection bias, show how selection bias can occur in different types of epidemiologic study designs, and discuss how to adjust for or otherwise deal with selection bias.
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,Are the Data Correct? Information Bias, |
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Abstract
. is a . in a study that arises because of incorrect information obtained on one or more variables measured in the study. The focus here is on the consequences of having inaccurate information about exposure and disease variables that are dichotomous, that is, when there is . of exposure and disease that leads to a . in the resulting .. We consider exposure and disease variables that are .. More general situations, such as several categories of exposure or disease, continuous exposure or disease, adjusting for covariates, matched data, and mathematical modeling approaches, are beyond the scope of the activities provided below.
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,Other Factors Accounted for? Confounding and Interaction, |
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Abstract
. is a form of bias that concerns how a measure of effect may change in value depending on whether variables other than the exposure variable are controlled in the analysis. ., which is different from confounding, compares estimated effects . other variables are controlled.
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,Confounding can be Confounding - Several Risk Factors, |
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Abstract
This chapter considers how the assessment of . gets somewhat more complicated when controlling for more than one risk factor. In particular, when several . are being controlled, we may find that considering all risk factors simultaneously may not lead to the same conclusion as when considering risk factors separately. We have previously (Chapter 10) argued that the assessment of confounding is not appropriate for variables that are . of the exposure-disease relationship under study. Consequently, throughout this chapter, our discussion of confounding will assume that . of the variables being considered for control are effect modifiers (i.e., there is no interaction between exposure and any variable being controlled).
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,Simple Analyses- 2×2 Tables are not that Simple, |
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Abstract
This chapter discusses methods for carrying out . procedures for epidemiologic data given in a simple two-way table. We call such procedures . because we are restricting the discussion here to dichotomous disease and exposure variables only and we are ignoring the typical analysis situation that considers the control of . variables when studying the effect of an exposure on disease.
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,Control - What It’s all about, |
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Abstract
In previous chapters, we have discussed and illustrated several important concepts concerning the . of additional (.) variables when assessing a relationship between an exposure variable and a health-outcome variable. In this chapter, we briefly review these concepts and then provide an overview of several options for the process of control that are available at both the design and analysis stages of a study.
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,How to Deal with Lots of Tables? Stratified Analysis, |
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Abstract
This is an analysis option for the control of extraneous variables that involves the following steps:.Both stratum-specific analyses and overall assessment require a ., an ., and a .. In this chapter we focus on ., which is the most conceptually and mathematically complicated of the four steps. For overall assessment, the point estimate is an . that is typically in the form of a . of stratumspecific estimates. The . is typically a large-sample interval estimate around the adjusted (weighted) estimate. The test of hypothesis is a generalization of the ..
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,Matching - Seems Easy, But not that Easy, |
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Abstract
Matching is an option for control that is available at the study design stage. We previously introduced matching in Chapter 13. We suggest that you review that chapter before proceeding further with this chapter. The primary goal of matching is to gain . in estimating the measure of effect of interest. There are other advantages to matching as well, and there are disadvantages. In this chapter, we define matching in general terms, describe different types of matching, discuss the issue of whether to match or not match, and describe how to analyze matched data.
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Back Matter |
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Abstract
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