Causal Bayesian version of rbmi that exploits post-ICE data

I've recently started a new 3 year project that will develop Bayesian reference based methods. @wolbersm is a collaborator on the project.

In particular, we want to implement in a package the methods described in this arXiv preprint. Briefly, this involves fitting a modified model in Stan that incorporates any observed post-intercurrent event (ICE) data. The modified model is based on that proposed by White et al (2020). The model includes one or two additional parameters that dictate how mean trajectories post-ICE depend on the on-treatment model parameters (as estimated in the current standard model used by rbmi).

I am interested in exploring the potential to include this additional functionality within rbmi, rather than as a separate stand-alone package, and would be keen to find out if the rbmi maintainers/owners are interested/willing to explore this.

Start by reading the linked arXiv preprint and comparing its modified Stan model with the current standard model used by rbmi. The project needs an agreed design for incorporating post-ICE data and deciding whether the functionality belongs in rbmi or a separate package; no files or tests are identified yet.