Hi Amanda,
I would recommend you to quantify reliability using the same processing pipeline you will use to test for longitudinal effects in your sample. Relevant literature include those listed below. For reliability you would want to evaluate subjects over a time period in which you do not expect the main effects of your study (e.g., disease related or learning related morphometric effects), but rather standard physiological and MRI system variability. Ideally this group also has the same age/gender representation of the group in which you later want to test for longitudinal effects.
Cheers,
Jorge
https://www.ncbi.nlm.nih.gov/pubmed/22430496
https://www.ncbi.nlm.nih.gov/pubmed/23668971
On 20/09/2017 15:00, Worker, Amanda wrote:
Hi all,
I have a longitudinal dataset that I'd like to calculate test-retest reliability for. However, I am not sure whether to calculate this for the cross-sectionally processed data or longitudinal data? It would seem to make sense to use the cross-sectional data, as the time points are independent, but then it means that the test-retest results would not be applicable in a dataset processed longitudinally. On the other hand, calculating reliability metrics for data processed in the exact same way as will be used in further studies seems to make sense also, but would calculating test-retest on the longitudinally processed data bias the results, as the data points are not fully independent?
Does anyone have any idea of the best way forward?
Thanks,
A
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