From the course: Descriptive Healthcare Analytics in R

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Understanding confounders

Understanding confounders - R Tutorial

From the course: Descriptive Healthcare Analytics in R

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Understanding confounders

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- [Instructor] Now we move on to Section Four of Chapter Two, where we get into confounders. This lecture will first explain what confounders are, meaning how they are defined. Next, we will go through why they are important to consider in a BRFSS descriptive analysis. We are eventually doing some bivariate tests, so we have to what confounders we need to think about, including in our analytic data set. Let's start with explaining what a hypothesis is. And then, showing where confounders fit in hypotheses. This is a quick example that will show you how epidemiologists approach making a hypothesis. Hypotheses are not necessary in a descriptive analysis, but they can help guide the analysis, so we are going to use hypotheses to guide our analyses in this course. Briefly, a hypothesis needs a defined subpopulation. I'm giving you the example of Hispanics. Then, it needs a defined exposure or something that you think causes something else, which is the disease or outcome. Here I give the…

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