I'm using 3dLME for my group analysis (n=32) and each subject has 8 conditions. Within each condition there is a maximum of 4 trials. For some of the subjects, there are only 1-2 trials that would create the beta coefficient used in the model. In that case, 1) would the data reliable enough to include in the model with 1-2 trials? Or would it be preferable to exclude those conditions? Approximately 23 out of the 736 conditions would be excluded if that's the way to go.
Is this an event-related or a block-designed study? Given the potential for varying uncertainty across conditions, I recommend considering the use of 3dMEMA for each contrast.
Thank you for the response. It's a within-subjects design event-related study. I'm interested in testing for brain regions changing according to a specific function (e.g., power law) across my task conditions. I chose 3dLME for that reason - is 3dMEMA capable of doing the same? I've included an example of my 3dLME model. Currently I don't run any specific GLTs at the group level - I cluster threshold the F map.
In that case, it might be fine to include all the data, regardless of their sample sizes. The alternative of removing some data can introduce biases. If this is a genuine concern, you could try both approaches and compare the differences.
Is the PF column generated based on a customized function? An alternative could be to consider an adaptive approach, as discussed in this blog post.
Gang
The
National Institute of Mental Health (NIMH) is part of the National Institutes of
Health (NIH), a component of the U.S. Department of Health and Human
Services.