Any help would be appreciated, as I'm not sure how to proceed. I have a dataset with binary data and have been able to fit a logistic regression model to it (presence of disease, for example, as a function of age). The data coverage is large, spanning ages 0-90, and I believe I can then use the output betas to predict the probability of disease to other people in the population. I've been considering these predicted probabilities as "priors". Suppose there is then a different study on the same topic, but samples only include children. This study uses a uses a totally different methodology, but the methodology provides a probability of disease as well. What is the proper way to update the priors for children with this new dataset output? I was thinking that I could just use Bayes' theorem, but the second dataset is not in any way related to the first (so that I cannot update the model parameters) and I only have the output predicted probabilities. Thank you in advance.