(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Daniel Lee writes:

We’ve just launched our new website.

Generable is where precision medicine meets statistical machine learning.

We are building a state-of-the-art platform to make individual, patient-level predictions for safety and efficacy of treatments. We’re able to do this by building Bayesian models with Stan. We currently have pilots with AstraZeneca, Sanofi, and University of Marseille. We’re particularly interested in small clinical trials, like in rare diseases or combination therapies. If anyone is interested, they can reach Daniel at daniel@generable.com

I’ve been collaborating with Daniel for many years and I’m glad to hear that he and his colleagues are doing this work. It’s my impression that in many applied fields, pharmacometrics included, there’s a big need for systems that allow users to construct open-ended models, using prior information and hierarchical models to regularize inferences and thus allow the integration of multiple relevant data sources in making predictions. As Daniel implies in his note above, Bayesian tools are particularly relevant where data are sparse.

The post Generable: They’re building software for pharma, with Stan inside. appeared first on Statistical Modeling, Causal Inference, and Social Science.

**Please comment on the article here:** **Statistical Modeling, Causal Inference, and Social Science**