Causal Bayesian version of rbmi that exploits post-ICE data
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Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
Research direction
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.
Written by the indexing model from the issue text.
Description
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.
- Dominant language
- R
- Stars
- 21
- Forks
- 13
- Avg merge
- 13h 22m
- Merged PRs (30d)
- 7
Getting set up
- No Dockerfile or Docker Compose file
- No pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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