# Prediction interval in meta-analysis with brms

Hi! I’m conducting a meta-analysis with brms and I want to compute the prediction interval for my average effect size.
This is my model:

mod0_prior <- c(
prior(normal(0, 2), class = "b", coef = "Intercept"),
prior(cauchy(0, 1), class = "sd")
)

fit <- brm(eff_size_g|se(eff_size_se_g) ~ 0 + Intercept + (1|study_meta/substudy),
data = meta_clean,
prior = mod0_prior)


Now I know that prediction interval in meta-analytic model are computed as: \hat{\mu} \pm 1.96\sqrt{\hat{\tau^2}+Var[\hat{\mu}]}. Reading online, with brms and my nested structure, the simple predict() approach should be fine, taking into account all variability sources.
Now, I’m struggling with these approaches:
1.

# Using Tidybayes

pred <- add_predicted_draws(meta_clean %>% select(substudy, eff_size_g, eff_size_se_g),
fit, re_formula = NA)

# Then summarising across all study:substudy using mean or median and 95% interval

# predicting a new study using an average study-level standard error
new_data_un <- tibble(study_meta = "newstudy",  eff_size_se_g = mean(meta_clean\$eff_size_se_g))


Actual distributions are a little bit different, but the first approach returns me a strange bimodal posterior distribution. At the same time, the first approach sounds like the most reasonable for me.
What do you think?

Hey there! I’m sorry your question fell through… Unfortunately I don’t know much about neither meta-analysis nor brms. I saw @ jsocolar come up with good thoughts the other day in this thread. Maybe he knows waht’s going on?

Cheers,
Max

nd = data.frame(study = "new", sei = 0)