Part iii you are interested in modeling the effect of

Part III
You are interested in modeling the effect of smoking (binary, 1 if smoker, 0 if non-smoker) on
subsequent overweight status (binary, 1 if BMI>25, 0 if BMI<=25). In your study population, many
subjects are overweight at follow-up (>30%). You believe age (in years, continuous), sex (binary, 1
if male, 0 if female), race (categorical, 1 if white, 2 if black, 3 if other), and baseline BMI
(continuous) are confounders. You decide to fit a GLM.
1. Is odds ratio the best measure of the association between smoking and subsequent overweight
status? Why or why not?
2. Given your answer in the above question, what is the link function you plan to use for this
model? Explain why you chose this link function.
3. Write out the model you plan to fit, using mathematical notation.
4. Interpret the beta coefficient for sex.
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5. Here is some partial SAS code you plan to use to execute this approach. How would you
appropriately fill in the question marks? (NOTE, not all question marks necessarily need to be
filled)
6. Given the SAS code you describe in Question#5, how are you modeling the variance of the
overweight outcome?
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