I am currently thinking about two possible submissions, happy to get feedback what people find most interesting:
- Using Hidden Markov Models as a complement/alternative to survival models (as discussed at Fitting HMMs with time-varying transition matrices using brms: A prototype)
- Bayesian modelling of organoid growth: In cystic fibrosis research (and presumably elsewhere) a common way to asses organoid growth is to compute area under the growth curve and compre this value between groups. It however turns out that using a simple hierarchical linear model on the logarithm of the size of the organoids adjusting for baseline size fits the data much better and provides more interpretable results.