USING SAS on demand for academics. Analyze NHANES data to data from an Arizona study. 3-to-1 matching of NHANES participants who had PFAS measurements to firefighters can be conducted. Participating f Original Papers

https://www.mediafire.com/folder/9nyf938zaq7xg/Documents

USING SAS on demand for academics. Analyze NHANES data to data from an Arizona study.

3-to-1 matching of NHANES participants who had PFAS measurements to firefighters can be conducted. Participating firefighters will be randomly shuffled. In shuffled order, each firefighter can be matched to the NHANES participant with the same sex, ethnicity, and serum PFAS collection time-period who was closest in age to the target firefighter. After all firefighters had been assigned a single match, they will be randomly shuffled again, and the process will be repeated two more times with the remaining NHANES participants. This process will result in a 3-to-1 matched NHANES data set. To assess whether the results of the PFAS serum levels in the study would be generalizable to Georgia residents, the distribution of participants’ age, sex, and race can be compared to the 2023 U.S. census for Georgia using Chi-squared tests. Test statistically significant differences among continuous variables.. Associations between serum levels and demographic characteristics will be assessed for all participants using generalized linear regression. 

Confounders and effect modification should be assessed between PFAS type (n-PFOS, Sm-PFOS, PFHxS, n-PFOA, PFNA, and PFDA) and gender, age, and race-ethnicity.  The contribution of each covariate to the models [age, gender (reference = male), race-ethnicity (reference = non-Hispanic white), and occupation status can be assessed in nested models by the model fit using the likelihood ratio test, and by observing whether there was a change in the odds ratio of more than 10%. Factors such as age, gender, education level, race/ethnicity, years of firefighting should be compared to NHANES data. 

The R-squared estimate should be used to measure the goodness of the fit of the model to the observed data.  Stepwise regression will be run in SAS using PROC SURVEYREG.