Hi everyone,

in another thread on this forum (https://afni.nimh.nih.gov/afni/community/board/read.php?1,166126,166126#msg-166126), Gang has posted some good suggestions about result reporting (abandon strict dichotomization, report full results, quantify effects & model data hierarchy).
The original paper (https://www.biorxiv.org/content/10.1101/2021.05.09.443246v1) is focussing on bayesian analysis.
However, it sounds like following this advice seems to be good for 3dlmer, too. Is that correct?

In our particular case, our study is rather small, so there's a good chance that some interesting effects are below significancy threshold. So based on Gangs suggestions, we think it would be good to also report trends that are below cluster corrected significany.

Therefore, we are wondering what would be good practice for reporting results and trends of a 3dlmer analysis?
Since 3dlmer does voxelwise analysis, I would argue that unlike in bayesian analysis, with 3dlmer it is not possible to list ALL results, as the whole brain/ mask would be included at some point.
When 3dclustsim suggests f.e. a cluster size of >=50 at p=0.001 is a significant result, there seem to be two ways of searching for trends: increasing p and decreasing cluster size. When aiming to present results in a countinuous manner, it seems that we have to make a cut-off at some point, or it would be too many small and rather insignificant clusters.

What is the best approach to check/report trends from a 3dlmer model? (And therefore more complete results, even though not full results)
1. Adjust p value, 2. decrease cluster size, 3. do both, 4. use 3dclustsim with a different alpha error setting, or 5. something else??

Thanks in advance for your feedback!

--007