Hi Stan developers and users,

I have some general questions about which weighting method is appropriate to use for our data and research question. We are interested in the association between disease interventions and disease rate in the population. We have the county-level data. In each county, there are 7 age groups and quarterly disease rates. Therefore, the total number of rows was ~3130 counties x 7 age groups x 4 quarterly rates. We also have the ACS population estimates for each age group in every county.

If we were using Frequentist method, I think we would use the weighted least square regression using the population size as the weights in the regression model. Then the coefficients of the intervention variables would be the association that we want to estimate in the population.

I tried specifying the `weights()`

using population size by age group and county in `brm`

function. However, the model failed to converge. I guess it was because there were a lot of variation in population size by age group and county. I also read several posts that compared weighted regression and MrP. But, I am not sure whether MrP is applicable in our case since our data are not survey data with individual respondents. I was wondering whether you could give us suggestion about which method (or a different method) is appropriate in Bayesian framework.