In this assignment, you will use the data set, “lc_all”, containing NHANES data similar to the data used by Schooling et al. in their analysis of childhood exposure to infections and adult obesity. This data set is available on Canvas.
1. From the dataset ‘lc_all’, create a temporary dataset named ‘lc’ and make the following changes to the data and variables.
a. Using data for waist circumference (‘waistcirc’) and gender (‘gender’) variables, create a new binary or dichotomous (two level) variable, “ab_obesity”, and code it as follows : 1) for males: <=102 cm as ‘0’ and > 102 cm as ‘1’; for females: <=88 cm as ‘0’ and >88 as ‘1'.
b. What percentage of participants are in the abdominal obesity category in the overall sample? Is the prevalence of abdominal obesity higher or lower in males compared to females? Report the percentage of abdominal obesity for each gender group.
2. Use PROC LOGISTIC to examine the association between age (independent variable) and ab_obesity (outcome). Use a UNIT= statement to obtain estimates for every 10 year increase in age. Be sure to model the probability of being obese (category 1 corresponding to waist >102 in males and >88 in females).
a. Report and interpret the parameter estimate (beta coefficient) for the association between obesity and age.
b. Report and interpret the odds ratio and 95% confidence interval for the association between obesity and age (in 10 year unit).
c. What is the relationship between the odds ratio and the logistic regression parameter estimate? i.e., how can you obtain odds ratio from the parameter estimate generated from logistic regression models?
3. Examine the association between gender (independent variable) and the binary/dichotomous variable for abdominal/central obesity (outcome or dependent variable). Use males as the reference category. Report the OR and 95% confidence intervals and interpret the odds ratio for abdominal obesity comparing females to males.
4. Examine the association between child pathogen burden and binary variable for abdominal obesity as the outcome. Use formats to ensure that the childhood pathogen burden group zero is modeled as the reference group.
a. Report and interpret the odds ratio and 95% confidence interval for the odds of abdominal obesity for a childhood pathogen burden of three compared to a child pathogen burden of zero.
5. Re-run the logistic regression analysis. This time, use childhood pathogen burden three as the reference category. (You do not need to use formats here).
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