I am currently working on a hierarchical Bayesian meta-analysis model in Stan, where I need to synthesize effect sizes from a large corpus of existing academic literature.
A major practical hurdle is building a stable pipeline to gather the necessary data assets (sample sizes, standard errors, and citation graphs) across thousands of published papers. Initially, I looked into building web scrapers to gather this from public search engines, but dealing with unstructured HTML text and inconsistent formatting makes extracting precise parameters for prior distributions almost impossible.
To make the data ingestion cleaner, I am looking into switching to a structured data service like ScholarAPI to fetch machine-readable JSON metadata directly. The API outputs clean citation counts and structured text fields, which should make parameter extraction much easier.
I would love to get some advice on structuring this data layer for Stan workflows:
When handling large arrays of academic metadata for meta-analysis, what are the best practices for preprocessing the JSON features into a dense matrix format that Stan can read efficiently?
Has anyone integrated automated data pipelines directly with RStan or CmdStanPy workflows to dynamically update prior hyperparameters as new research data becomes available?
Any thoughts or examples of data structures used for large-scale academic meta-analysis would be greatly appreciated!
Hi, @Stream_On, and welcome to the Stan forums. I’m afraid this may not be answered because it wasn’t entirely clear what the question was.
My first advice is listen to Knuth (1974):
“We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.”
Just how large is large? The answers are going to be very different if (a) it fits/doesn’t fit into memory, and (b) needs to be done once/in an ongoing fashion.
If it fits into memory and is only going to be done once, don’t worry about it.
Do you have a current implementation that’s too slow?
JSON’s not designed for efficiency, so if you really need efficiency, you’ll want a custom binary format.
Not that I know of. Most of the priors that people tend to use in practice with Stan are hierarchical with only weakly informative priors on the hyperparameters because they’re not very sensitive. The one exception I see regularly is pharmacometric models where the PK parameters are very well known from similar drugs and informative priors can be very helpful.
Are you working in a model where the posterior is very sensitive to the prior hyperparameters?