First-time poster here. I would like to hear your input on how to analyse the data from the following study.

I plan to do an **exploratory analysis** of the changes in the immune cell frequencies (flow cytometry data) over time upon infection. **Four volunteers** were enrolled in a controlled human infection trial and then followed up at **six time points**. The follow-up time was long enough until the volunteers can be considered to have a chronic infection before the infections were abrogated.

I am primarily interested to see which immune cell subsets that changed over the period of infection (i.e. the effect of time with cell frequencies as the outcome). Due to the natural history of infection (acute & chronic), I expect **nonlinear** changes of the cell frequencies over time (such as a rapid increase of the cell frequencies during the acute phase and then decrease, and so on). Multiplicity is a concern as I am looking at many immune cell subsets. Thanks to the recent *BBR Course Video Series*, I am considering to do Bayesian analysis of such data.

Unfortunately, I have no practical experience in analysing nonlinear repeated measures data and also Bayesian data analysis. At this point, I could think of doing a model comparison of the full model *(cell frequencies ~ time)* against the null *(cell frequencies ~ 1)* to get the answer to which cell subsets change throughout infection. I have also heard about GAM, but my knowledge is merely scratching the surface.

This is, I repeat, an *exploratory study*. However, I would like to get the most out of it (without P-hacking) so that I can do a future study with more individuals as well as a smaller set of immune cells to be investigated.

I look forward to your input.