From the course: Descriptive Healthcare Analytics in R

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Designing confounders: Age and smoking

Designing confounders: Age and smoking - R Tutorial

From the course: Descriptive Healthcare Analytics in R

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Designing confounders: Age and smoking

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- [Instructor] Welcome to chapter two section six where we will design the confounding variables we will use for age and smoking in our analysis. So where are we in our journey? We selected one hypothesis per outcome variable to guide our analyses and put our native variables in our data dictionary. In the last section, I showed you my final web of causation, which included the confounders I am choosing to include in the analysis. The next step is I need to go into the code book and find all the native variables that correspond to those variables I selected as potential confounders. I need to make sure I retain these in the analytic data set. Next, as you saw with the exposure and outcome, I need to redesign most if not all of the variables to make it so I can use them in the descriptive analysis. Also, I will need indicator variables for multi-level categorical variables. After making my web of causation, I added age and smoking to my data dictionary. Let's see what I did. So you can…

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