Hello,

I am new to bayesian methods and currently running the following logistic model on 10 imputed datasets.

All explanatory variables are factor variables. `region`

has 8 levels and `period`

has 3 levels. After running the model, I am interesting in checking if the response for the second and third period is different compared to the reference first period for each region.

I have run several `hypothesis`

tests (similar to one shown below). Exponentiating the estimate and CI intervals from the hypothesis test results provides odds ratios consistent with those from frequentist analysis.

```
mod <- brm_multiple(
case ~ region * period + imd + age_group * vaccinated + lab_result,
data = data,
prior = set_prior("normal(0, 1)"),
sample_prior = TRUE,
family = bernoulli()
)
hypothesis(mod, c(region_2 = "period3 + region2:period3 = 0"))
Hypothesis Tests for class b:
Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
1 region_2 0.26 0.13 0.01 0.53 1.51 0.6 *
---
```

My question is 1) whether this approach is appropriate and 2) are there any other approaches recommended instead of the `hypothesis`

function? I have read somewhere the posterior draws can be used for such analysis but I am unclear how I would be able to apply the contrasts correctly on posterior samples and obtain odds ratios?

Thanks in advance for any advice.