Missing data in 3dLME versus including betas with low trial count

AFNI Message Board
1

Hello AFNI team,

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.

Thank you,
Catherine

2

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.

Gang

3

Hello Gang,

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.

3dLME -prefix PF
-jobs 8
-model 'PF'
-ranEff '~1+PF'
-qVars 'PF'
-mask mask.nii.gz
-dataTable
Subj Cond PF InputFile
sub-001 t2017to2015 0.333333333 3dREML_output+tlrc.BRIK[591]
sub-001 t2014to2012 0.083333333 3dREML_output+tlrc.BRIK[594]
sub-001 t2011to2009 0.047619048 3dREML_output+tlrc.BRIK[597]
sub-001 t2008to2006 0.033333333 3dREML_output+tlrc.BRIK[600]
sub-001 t2005to2003 0.025641026 3dREML_output+tlrc.BRIK[603]
sub-001 t2002to1998 0.020833333 3dREML_output+tlrc.BRIK[606]
sub-001 t1997to1993 0.015873016 3dREML_output+tlrc.BRIK[609]
sub-001 t1992to1988 0.012820513 3dREML_output+tlrc.BRIK[612]
sub-002 t2017to2015 0.333333333 3dREML_output+tlrc.BRIK[591]
sub-002 t2014to2012 0.083333333 3dREML_output+tlrc.BRIK[594]
sub-002 t2011to2009 0.047619048 3dREML_output+tlrc.BRIK[597]
sub-002 t2008to2006 0.033333333 3dREML_output+tlrc.BRIK[600]
sub-002 t2005to2003 0.025641026 3dREML_output+tlrc.BRIK[603]
sub-002 t2002to1998 0.020833333 3dREML_output+tlrc.BRIK[606]
sub-002 t1997to1993 0.015873016 3dREML_output+tlrc.BRIK[609]
sub-002 t1992to1988 0.012820513 3dREML_output+tlrc.BRIK[612]
.....
sub-032 t2017to2015 0.333333333 3dREML_output+tlrc.BRIK[591]
sub-032 t2014to2012 0.083333333 3dREML_output+tlrc.BRIK[594]
sub-032 t2011to2009 0.047619048 3dREML_output+tlrc.BRIK[597]
sub-032 t2008to2006 0.033333333 3dREML_output+tlrc.BRIK[600]
sub-032 t2005to2003 0.025641026 3dREML_output+tlrc.BRIK[603]
sub-032 t2002to1998 0.020833333 3dREML_output+tlrc.BRIK[606]
sub-032 t1997to1993 0.015873016 3dREML_output+tlrc.BRIK[609]
sub-032 t1992to1988 0.012820513 3dREML_output+tlrc.BRIK[612] \

4

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