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In previous chapters, we have discussed and illustrated several important concepts concerning the control of additional (extraneous) 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. [ABSTRACT FROM AUTHOR]

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 precision 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. [ABSTRACT FROM AUTHOR]

This is an analysis option for the control of extraneous variables that involves the following steps: 1.Categorize all variables.2.Form combinations of categories (i.e., strata).3.Perform stratum-specific analyses.4.Perform overall E-D assessment if appropriate. Both stratum-specific analyses and overall assessment require a point estimate, an interval estimate, and a test of hypothesis. In this chapter we focus on overall assessment, which is the most conceptually and mathematically complicated of the four steps. For overall assessment, the point estimate is an adjusted estimate that is typically in the form of a weighted average of stratumspecific estimates. The confidence interval is typically a large-sample interval estimate around the adjusted (weighted) estimate. The test of hypothesis is a generalization of the Mantel-Haenszel chi square test. [ABSTRACT FROM AUTHOR]

This chapter discusses methods for carrying out statistical inference procedures for epidemiologic data given in a simple two-way table. We call such procedures simple analyses 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 other variables when studying the effect of an exposure on disease. [ABSTRACT FROM AUTHOR]

This chapter considers how the assessment of confounding gets somewhat more complicated when controlling for more than one risk factor. In particular, when several risk factors 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 effect modifiers of the exposure-disease relationship under study. Consequently, throughout this chapter, our discussion of confounding will assume that none of the variables being considered for control are effect modifiers (i.e., there is no interaction between exposure and any variable being controlled). [ABSTRACT FROM AUTHOR]

Confounding 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. Interaction/effect modification, which is different from confounding, compares estimated effects after other variables are controlled. [ABSTRACT FROM AUTHOR]

Information bias is a systematic error 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 misclassification of exposure and disease that leads to a bias in the resulting measure of effect. We consider exposure and disease variables that are dichotomous. 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. [ABSTRACT FROM AUTHOR]

Selection bias 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. [ABSTRACT FROM AUTHOR]

The primary objective of most epidemiologic research is to obtain a valid estimate of an effect measure of interest. In this chapter we illustrate three general types of validity problems, distinguish validity from precision, introduce the term bias, and discuss how to adjust for bias. [ABSTRACT FROM AUTHOR]

In the previous chapter on Measures of Effect, we focused exclusively on ratio measures of effect In this chapter, we consider difference measures of effect and other related measures that allow the investigator to consider the potential public health impact of the results obtained from an epidemiologic study. [ABSTRACT FROM AUTHOR]

In epidemiologic studies, we compare disease frequencies of two or more groups using a measure of effect. We will describe several types of measures of effect in this chapter. The choice of measure typically depends on the study design being used. [ABSTRACT FROM AUTHOR]

In epidemiologic studies, we use a measure of disease frequency 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. [ABSTRACT FROM AUTHOR]

A key stage of epidemiologic research is the study design. This is defined to be the process of planning an empirical investigation to assess a conceptual hypothesis about the relationship between one or more exposures and a health outcome. The purpose of the study design is to transform the conceptual hypothesis into an operational hypothesis 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]