One of the key steps in establishing the determinants of health and disease is to accurately estimate the degree to which specific exposures are associated with specific health outcomes.
Previous modules have provided concepts and tools for comparing groups with respect to continuously distributed variables (measurements). This module will similarly provide concepts and tools for comparing groups with respect to dichotomous, categorical, and ordinal outcomes for which we are comparing frequencies rather than mean values of a measurement, and we will focus on evaluating sampling error when making these types of comparisons.
We will first explore the utility of two very versatile and commonly used methods:
- The chi-square goodness of fit test
- The chi-square test of independence
We will then turn our attention to computing and interpreting confidence intervals for commonly used measures of association.
How do we measure the association between categorical outcomes and exposures?
How do we determine whether an exposure increases the risk of a particular health outcome?
What information can we assess from categorical data? What cannot be assessed?
When is it most appropriate to assess data categorically?
After successfully completing this module, the student will be able to:
- Identify situations in which it is appropriate to use the chi-square goodness of fit test, the chi-square test of independence, or Fisher's Exact Test to compare frequencies
- Compute (using R) and interpret confidence intervals for risk ratios, rate ratios, odds ratios, and risk differences