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.
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 (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 (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: Adjusted Estimates, A priori Criteria
Confounding: Different Studies, Case-Control Example
Confounding, Interaction, and Effect Modification
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.
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.
Why Use Matching?
Analysis of Matched Data
Matched Analysis (continued)
Analysis of Matched Cohort Data; Logistic Regression for Matched Data