Hi, @stemangiola! You donâ€™t need a prior with covariance. For example, Andrew Gelman and I didnâ€™t use multivariate priors in our Covid sensitivity and specificity paper because we didnâ€™t have the kind of data required to fit it. But Iâ€™d recommend doing it if your data supports it, especially if you donâ€™t have much data and there are strong correlations.

We explain how to code the multivariate priors efficiently following Gelman and Hillâ€™s Red-State/Blue-State example in their original regression book.

Luckily, it doesnâ€™t change the stratification logic at allâ€”MRP still works exactly the same way.

So far for discrete covariates I allow multilevel model without any multivariate prior. Now, I have a design matrix with these columns

tissue_heart (binary) | tissue_blood (binary) | age (continuous)

Does the statement

is valid even if I have now also a continuous covariate? for a model like

~ 0 + tissue + age + (tissue + age | GROUPING)

If I get your point is that I might need prior with covariance only if tissue and age are very correlated (e.g. a tissue have mostly young and another tissue have mostly old).