Introduction to Epidemiologic Methods and Quantitative Research
Understanding how and why diseases are distributed in populations and what factors are associated with disease relies on a set of epidemiologic methods that can be applied flexibly to many different settings. This unit introduces the main concepts in epidemiology and reviews the methodological approaches to measuring diseases in populations and assessing relationships between exposures and diseases.
Introduction to Statistical Decision Making
This unit offers an introduction to core topics in statistics for the analysis of health-related data with emphasis on analyzing the most common epidemiologic study designs.
Epidemiologic Study Designs
Epidemiologists employ a variety of observational and experimental study designs to investigate the distribution and causation of diseases. The primary types of epidemiologic studies will be introduced with examples to illustrate how investigators determine which study designs are best suited to addressing a set of scientific aims. The strengths and limitations of each study design are outlined for use in planning studies and interpreting and evaluating findings from published results.
Causation, Bias, and Confounding
To identify causal relationships between exposure and disease it is sometimes necessary to rely on observational data to assess causation, which introduces important issues of bias inherent in observational studies. Interpretation of epidemiologic data is often complicated by the fact that other factors may distort the relationship between an exposure of interest and a given disease. Factors that are associated with both exposure and disease can induce what is called confounding, and if not appropriately compensated for, can obscure the true exposure-disease relationship.
Measurement, Classification, and Misclassification
To understand the distribution of disease in populations and identify causal relationships between exposures and disease outcomes, epidemiologist must measure both exposure and disease in the population under study. Researchers must understand how to measure exposure and disease, and how the research question dictates the approach to classifying subjects as exposed or unexposed, diseased or non-diseased. What are the implications of misclassifications? How can sensitivity, specificity, positive predictive value, and negative predictive value be used to assess measurement instruments?
Data Management Practices in Health Research
Epidemiologic studies produce data in a wide range of formats and structures. To make full use of the information gained in these studies, researchers should consider how study data will be stored and managed. Decisions about data management will depend on how the data are collected, who will be accessing the data, and what types of analyses will be performed. What factors effect data management strategies and outline techniques for effective data management at the time of collection, storage, processing, and analysis?
Interpretation of Epidemiologic Studies and Decision Making
Findings from epidemiologic studies guide clinical and public health policy decisions, but sound decisions depend on a thorough understanding of relative strengths and weaknesses of different sources evidence. Study design, selection of study subjects, and differences between populations can greatly influence how research results are interpreted in the context of applied settings. Real-world examples demonstrate how epidemiologic principles can be used to synthesize evidence from different studies to evaluate the strength of evidence linking exposure and disease and to inform decisions about how to implement this knowledge into public health practice.
Multiple variable regression models in epidemiology
Multivariate regression is a common approach used to analyze epidemiologic data that allows the investigator to simultaneously adjust for multiple confounders. This unit provides an introduction to multivariate regression and an overview of logistic and Cox regression methods. Particular emphasis is given to how odds ratios and hazard ratios from regression models should be reported and interpreted in the scientific literature.
Qualitative Research Methods
Qualitative research methods are an important complement to the quantitative methods used by epidemiologists. Qualitative approaches are used to develop strategies to implement public health interventions, understand health decision making, and to follow-up on findings from quantitative studies. An introduction to qualitative research methods will provide a background for implementation of qualitative methods into epidemiologic research and public health practice. Practical examples will be used to illustrate the use of phenomenology and grounded theory methods, with a discussion of sampling and data collection.
Analyzing Qualitative Data and Public Health Applications
Qualitative research employs data collection strategies that differ in important ways from quantitative research. The analysis of qualitative data involves analysis approaches tailored to the unique data structure of qualitative research and allows researchers to interpret and apply the results for these studies. Practical examples will be used to illustrate key topics including data management, coding, data analysis, and writing.
* NOTE: course content is subject to change