From the course: Descriptive Healthcare Analytics in R

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Making a web of causation

Making a web of causation - R Tutorial

From the course: Descriptive Healthcare Analytics in R

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Making a web of causation

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- [Instructor] Welcome to Chapter Two, Section Five. Where we work some more with confounders. In this section, we will identify confounders by looking in the literature. And by making a web of causation. I'll give brief examples of both. To identify confounders, we need some practical hypotheses. We already have selected a sub-population, veterans, and an exposure, alcohol drinking. Let's choose two different outcomes so I can demonstrate two different types of descriptive analyses. Sleep duration, which is continuous variable, and asthma status, which is a binary variable. So our goal is to identify confounding variables between our exposure, alcohol drinking, and our outcomes, which are sleep duration and asthma, which are not on the causal pathway between the exposure and the outcome. And a good way to do that is to go shop in the scientific literature. Here is an article from BRFSS about insufficient rest and sleep among veterans. Let's go to Table 1. Here we are. You will see…

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