From the course: Descriptive Healthcare Analytics in R
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Designing confounders: Age and smoking - R Tutorial
From the course: Descriptive Healthcare Analytics in R
Designing confounders: Age and smoking
- [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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Contents
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Uses of a data dictionary4m 35s
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(Locked)
How to set up a data dictionary3m 48s
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(Locked)
Adding to the data dictionary6m 13s
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(Locked)
Understanding confounders4m 24s
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(Locked)
Making a web of causation6m 28s
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(Locked)
Designing confounders: Age and smoking4m 42s
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(Locked)
Designing confounders: Other demographics4m 19s
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(Locked)
Designing confounders: Other variables used in analysis4m 39s
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