I guess I was unclear in my ask. I conducted health surveys of our health plan members in 2011, 2014, and 2017. I pooled the data from these 3 survey cycles in order to have enough Blacks, Latinos, Filipinos, and Chinese who were aged 35-64 at the time of the survey. I created a post-stratification weighting factor for the pooled survey data that weights each racial group to the age distribution for that race group in the health plan in 2016. In order to be able to compare Whites, Blacks, Latinxs, Filipinxs and Chinese, I used Proc Surveyreg (following the NHANES tutorial) to age-standardize the estimated prevalence and to test for differences between Whites and the other race/ethnic groups. I used the SAS code below to age-standardize. The decimal numbers after age3564gp correspond to the distribution of ages 35-64 (broken into 35-44, 45-54, 55-64 age groups) in the 2016 American Community Survey. I used survyr as my stratum factor. Survwt is my survey weighting factor. I think I'm probably OK just pooling the data and not controlling for survey year because for most of my health behavior variables, there doesn't seem to be much variation across the survey years. However, for some outcomes, there may be enough of a difference across the 3 survey cycles that it would be good to be able to say I controlled for survey year in the analysis. That's what I'm not sure how to do. proc surveyreg ; domain sex; strata survyr; class race age3564gp; weight survwt; format sex sex.; model fvegge3dy= race age3564gp race*age3564gp/noint solution clparm; estimate "WhiteNH 35-64" race 1 0 0 0 0 0 age3564gp .3248 .3430 .3322 race*age3564gp .3248 .3430 .3322 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 /cl; estimate "Black 35-64" race 0 1 0 0 0 0 age3564gp .3248 .3430 .3322 race*age3564gp 0 0 0 .3248 .3430 .3322 0 0 0 0 0 0 0 0 0 0 0 0 /cl; estimate "Latino 35-64" race 0 0 1 0 0 0 age3564gp .3248 .3430 .3322 race*age3564gp 0 0 0 0 0 0 .3248 .3430 .3322 0 0 0 0 0 0 0 0 0 /cl; estimate "Filipino 35-64" race 0 0 0 1 0 0 age3564gp .3248 .3430 .3322 race*age3564gp 0 0 0 0 0 0 0 0 0 .3248 .3430 .3322 0 0 0 0 0 0 /cl; estimate "Chinese 35-64" race 0 0 0 0 1 0 age3564gp .3248 .3430 .3322 race*age3564gp 0 0 0 0 0 0 0 0 0 0 0 0 .3248 .3430 .3322 0 0 0 /cl; estimate "SoAsn 35-64" race 0 0 0 0 0 1 age3564gp .3248 .3430 .3322 race*age3564gp 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .3248 .3430 .3322 /cl; estimate "Wh v Black 35-64" race 1 -1 0 0 0 0 race*age3564gp .3248 .3430 .3322 -.3248 -.3430 -.3322 0 0 0 0 0 0 0 0 0 0 0 0/cl; estimate "Wh v Latino 35-64" race 1 0 -1 0 0 0 race*age3564gp .3248 .3430 .3322 0 0 0 -.3248 -.3430 -.3322 0 0 0 0 0 0 0 0 0/cl; estimate "Wh v Filipino 35-64" race 1 0 0 -1 0 0 race*age3564gp .3248 .3430 .3322 0 0 0 0 0 0 -.3248 -.3430 -.3322 0 0 0 0 0 0/cl; estimate "Wh v Chinese 35-64" race 1 0 0 0 -1 0 race*age3564gp .3248 .3430 .3322 0 0 0 0 0 0 0 0 0 -.3248 -.3430 -.3322 0 0 0/cl; estimate "Wh v SoAsn 35-64" race 1 0 0 0 0 -1 race*age3564gp .3248 .3430 .3322 0 0 0 0 0 0 0 0 0 0 0 0 -.3248 -.3430 -.3322/cl; run;
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