Part IIIYou 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, manysubjects 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. 3
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? 4
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- Spring '14