This course guides you through various lessons in epidemiology.

How to use ActivEpi Web (Video)

Introduction to ActivEpi Web

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 lesson, we describe examples of epidemiologic research and introduce several important methodologic issues typically considered in such research.

Epidemiologic Research: An Overview

Epidemiologic Research: An Overview (continued)

Epidemiologic Research: An Overview (continued); Examples of Studies

Homework Exercises: Epi Overview

References: Epi Overview

A key stage of epidemiologic research is the study design, the plan of an empirical investigation to assess a conceptual hypothesis about the relationship between one or more exposures and a health outcome.

Epidemiologic Study Designs

Observational Study Designs; Cohort Studies

Case-Control and Cross-sectional Designs

Hybrid and Incomplete Designs

Homework Exercises: Epi Study Designs

References: Epi Study Designs

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 of measure typically depends on the study design being used and the goal of the study.

Measures of Disease Frequency

Measures of Disease Frequency: Risk

Measures of Disease Frequency: Rate

Prevalence, Mortality, Age-Adjustment

Homework Exercises: Measures of Disease Frequency

References- Measures of Disease Frequency

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.

Measures of Effect

Odds Ratio Calculation; Different Study Designs

Odds Ratio Approximation of the Risk Ratio

The Rate Ratio for Person-Time Studies

Homework Exercises: Measures of Effect

References: Measures of Effect

Appendix (An Asterisk): Estimating the Rate Ratio (i.e., IDR) in a Nested Case-Control Study

In the previous lesson (Lesson 5) on Measures of Effect. we focussed exclusively on ratio measures of effect. In this lesson, we consider difference measures of effect and other related measures that all the investigator to consisider the public health importance and/or potential impact of the results obtained from an epidemiologic study.

Measures of Potential Impact

Potential Impact Concept; Etiologic Fraction

Potential Impact (continued): Prevented Fraction

Homework: Potential Impact

References: Potential Impact

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

Validity

Validity (continued)

Homework: Validity

References: Validity

Selection bias concerns systematic error that may arise from the manner in which subjects are selected into one's study. In this lesson, we describe examples of selection bias, provide a quantitative framework for assessing selection bias, show how selectiion bias can occur in different types of epidemiologic study designs, and discuss how to adjust for or otherwise deal with selection bias

Selection Bias

Selection Bias (continued)

Selection Bias (continued)

Selection Bias (continued)

Homework: Selection Bias

References: Selection Bias

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 in this Lesson (i.e., chapter) is on the consequences of having inaccurate information about exposure and disease; in particular, if there is misclassification of exposure or disease, there may be bias in the resulting measure of effect.

Information Bias

Information Bias (continued)

Information Bias: Assessment and Correction

Information Bias: Correcting for Differential Misclassification; Diagnostic Testing

Homework: Information Bias

References: Information Bias

Confounding is a form of bias that concerns how the value of an estimated measure of effect may change depending on whether variables other than the exposure variable are controlled for in the analysis.

Confounding

Confounding: Adjusted Estimates, A priori Criteria

Confounding: Different Studies, Case-Control Example

Confounding, Interaction, and Effect Modification

Homework: Confounding

References: Confounding

This Lesson 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

Confounding Involving Several Risk Factors

Confounding Involving Several Risk Factors (continued)

Homework: Confounding Involving Several Risk Factors

References: Confounding Involving Several Risk Factors

This lesson 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.

Simple Analyses

Statistical Inferences for Simple Analyses (continued)

Simple Analyses (continued)

Simple Analyses: Cohort Studies Involving Risk Ratios

Cohort Studies Involving Risk Ratios (continued)

Simple Analyses: Case-control Studies

Simple Analyses for Rate Ratios in Cohort Studies

Homework: Simple Analysis

References: Simple Analysis

In previous lessons, we have discussed and illustrated several important concepts concerning the control of additional variables when assessing a relationship between an exposure variable and a health-outcome variable. In this lesson/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.

Options for Control of Extraneous Factors

Options for Control (continued)

Options for Control: Mathematical Modeling

Homework: Options for Control

References: Options for Control

In this lesson, we focus on overall assessment of the exposure-disease relationship in a stratified analysis, which is the most conceptually and mathematically complicated of the four steps involved in stratification. For overall assessment, the point estimate is an adjusted estimate that is typically in the form of a weighted average of stratum-specific 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 for simple analysis.

Stratified Analysis: Overview

Stratified Analysis: Overall Test

Stratified Analysis: Adjusted Estimates

Stratified Analysis: Adjusted OR

Mantel-Haenszel Adjusted Estimate; Interval Estimation

More Than 2 Exposure Categories; Test for Trend

Homework: Stratified Analysis

References: Stratified Analysis

Matching is an option for control that is available at the study design stage. We previously introduced matching on the second lesson page in Lesson 13 on Options for Control. In this lesson, 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.

Matching Overview

Why Use Matching?

Analysis of Matched Data

Matched Analysis (continued)

Analysis of Matched Cohort Data; Logistic Regression for Matched Data

Homework: Matching

References: Matching