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help > RE: calculating cohen's d from rZ values
Jan 15, 2015 02:01 AM | Alfonso Nieto-Castanon - Boston University
RE: calculating cohen's d from rZ values
Hi Patrick,
Some thoughts on your questions below
Best
Alfonso
Originally posted by Patrick McConnell:
I am assuming here that roi1a and roi1b represent your a priori seeds and roi2a and roi2b represent the clusters that show significant between-group differences in connectivity with each of these seeds respectively (step 1). If this is correct then, yes, unfortunately the average connectivity between roi1a and roi2a will be biased towards showing higher between-group connectivity differences and higher Cohen's D values for this particular sample of subjects than what you would expect to obtain using the same masks in the population (because the roi2a mask has been defined from this same group of subjects in a non-orthogonal way).
We are basically seeing two separate paths, one of which seems to be strengthened in Tx group (roi1a path), and another that seems to be weakened in Tx group (roi1b path). Would it be valid to determine power/sample size for future studies based on these Cohen's D values?
For the reasons above those sample size estimates are likely going to be conservative (too few subjects). For these cases I typically use leave-one-out cross-validation to obtain unbiased measures of your between-group differences (basically you estimate roi2a using the same between-group test but applied only to N-1 subjects, and then extract the average connectivity from the resulting roi2a mask from the left-out subject alone, and repeat this process for each subject). If you want to give it a try the attached function (spm_crossvalidation) will take the current second-level analysis being displayed in SPM and apply this cross-validation procedure to obtain unbiased measures of your effect sizes.
Hope this helps
Alfonso
Some thoughts on your questions below
Best
Alfonso
Originally posted by Patrick McConnell:
Alfonso,
Thanks again for the thoughtful reply.
For the initial seed-voxel bivariate correlations (step 1) and follow-up seed-voxel bivariate correlations (step 3/4), I generated the SPM.mat file using an F-test [1 0; 0 1] but then used t-contrasts to find the significant correlations (1, -1 and -1,1).
In step 6, results (i.e., single subject regression coefficients) were used only to perform a within-group dependent samples t-test to determine whether there were statistically significant differences in magnitude of regression coefficient for each path (e.g., roi1 --> roi2 vs. roi2 --> roi1). Denoised time-series were only used for visualization. I understand why the Cohen's D will be biased for the follow-up seed-voxel bivariate correlations and the ROI-ROI bivariate regressions, but is there any issue of bias with the Cohen's D values calculated from the initial seed-voxel analyses (e.g., roi1a --> roi2a and roi1b --> roi2b)?
Thanks again for the thoughtful reply.
For the initial seed-voxel bivariate correlations (step 1) and follow-up seed-voxel bivariate correlations (step 3/4), I generated the SPM.mat file using an F-test [1 0; 0 1] but then used t-contrasts to find the significant correlations (1, -1 and -1,1).
In step 6, results (i.e., single subject regression coefficients) were used only to perform a within-group dependent samples t-test to determine whether there were statistically significant differences in magnitude of regression coefficient for each path (e.g., roi1 --> roi2 vs. roi2 --> roi1). Denoised time-series were only used for visualization. I understand why the Cohen's D will be biased for the follow-up seed-voxel bivariate correlations and the ROI-ROI bivariate regressions, but is there any issue of bias with the Cohen's D values calculated from the initial seed-voxel analyses (e.g., roi1a --> roi2a and roi1b --> roi2b)?
I am assuming here that roi1a and roi1b represent your a priori seeds and roi2a and roi2b represent the clusters that show significant between-group differences in connectivity with each of these seeds respectively (step 1). If this is correct then, yes, unfortunately the average connectivity between roi1a and roi2a will be biased towards showing higher between-group connectivity differences and higher Cohen's D values for this particular sample of subjects than what you would expect to obtain using the same masks in the population (because the roi2a mask has been defined from this same group of subjects in a non-orthogonal way).
We are basically seeing two separate paths, one of which seems to be strengthened in Tx group (roi1a path), and another that seems to be weakened in Tx group (roi1b path). Would it be valid to determine power/sample size for future studies based on these Cohen's D values?
For the reasons above those sample size estimates are likely going to be conservative (too few subjects). For these cases I typically use leave-one-out cross-validation to obtain unbiased measures of your between-group differences (basically you estimate roi2a using the same between-group test but applied only to N-1 subjects, and then extract the average connectivity from the resulting roi2a mask from the left-out subject alone, and repeat this process for each subject). If you want to give it a try the attached function (spm_crossvalidation) will take the current second-level analysis being displayed in SPM and apply this cross-validation procedure to obtain unbiased measures of your effect sizes.
Hope this helps
Alfonso
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| Title | Author | Date |
|---|---|---|
| Crystal Goh | Jun 2, 2012 | |
| Alfonso Nieto-Castanon | Jul 8, 2012 | |
| Patrick McConnell | Dec 20, 2014 | |
| Alfonso Nieto-Castanon | Dec 21, 2014 | |
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