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help > RE: Multiple covariates and scanner-correction
Jan 27, 2017 01:01 AM | Alfonso Nieto-Castanon - Boston University
RE: Multiple covariates and scanner-correction
Dear Joshi,
This is a very good question. If I am understanding correctly all three options will give you exactly the same results, and as such there is no 'better' or 'correct' choice. Strictly speaking, this is only true because you are testing the association with a covariate (IronAmyloid interaction), and that association will be the same irrespective of the level of ScannerType factor chosen (since IronAmyloid by ScannerType interactions have not been entered into your model). If you try to test other contrast in those same models, for example [1 0 0 0 0] in (1), [1 0 0 0 0] in (2), or [1 0 0 0 0 0] in (3), or if you include the ScannerType.*IronAmyloid interactions into your model, then those three options that you mention will result in different statistics/results, and in that case the 'better' choice would be the one where the zero-level of your ScannerType covariate corresponds to the level where you want to evaluate your effect of interest (e.g. IronAmyloid interaction). Often that 'better' choice would be something like:
4. Putting '-1' and '1' for the 8-channel and 32-channel subjects for the variable 'ScannerType' (in this case the zero-level of this covariate represents the average effect across both scanner types)
Hope this helps
Alfonso
Originally posted by Himanshu Joshi:
This is a very good question. If I am understanding correctly all three options will give you exactly the same results, and as such there is no 'better' or 'correct' choice. Strictly speaking, this is only true because you are testing the association with a covariate (IronAmyloid interaction), and that association will be the same irrespective of the level of ScannerType factor chosen (since IronAmyloid by ScannerType interactions have not been entered into your model). If you try to test other contrast in those same models, for example [1 0 0 0 0] in (1), [1 0 0 0 0] in (2), or [1 0 0 0 0 0] in (3), or if you include the ScannerType.*IronAmyloid interactions into your model, then those three options that you mention will result in different statistics/results, and in that case the 'better' choice would be the one where the zero-level of your ScannerType covariate corresponds to the level where you want to evaluate your effect of interest (e.g. IronAmyloid interaction). Often that 'better' choice would be something like:
4. Putting '-1' and '1' for the 8-channel and 32-channel subjects for the variable 'ScannerType' (in this case the zero-level of this covariate represents the average effect across both scanner types)
Hope this helps
Alfonso
Originally posted by Himanshu Joshi:
Dear Conn users,
Thankyou Alfonso et al., for providing such a wonderful software and regularly customising it on the basis of user requirements.
I have come across different opinions regarding putting categorical variable as covariate in the design. What would be everybody's opinion regarding Question 1 in this thread on the below statements
1. Putting '0' and '1' or 8 channel and 32 channel in this case for the variable 'scanner Type'
or
2. Putting '1' and '2' for 8 channel and 32 channel in this case for the variable 'scanner Type'
or
3. Putting '1' for for the subjects scanned with 8 channel and '0' for the subjects scanned with 32 channel for the variable 'Eight channel' and similarly putting '1' for for the subjects scanned with 32 channel and '0' for the subjects scanned with 8 channel for the variable 'Thity-two channel'
and then how would be the correction procedure in second level model for all the these three cases. I feel the contrast to be entered like
for case 1 AllSubjects, Iron, Amyloid, IronAmyloid, and ScannerType as [0 0 0 1 0]
for case 2 AllSubjects, Iron, Amyloid, IronAmyloid, and ScannerType as [0 0 0 1 0]
for case 3 AllSubjects, Iron, Amyloid, IronAmyloid, Eight channel and Thirty-two channel as [0 0 0 1 0 0]
Which of the three option is recommended for analysis. Your suggestions are valuable
Regards
Himanshu Joshi
Thankyou Alfonso et al., for providing such a wonderful software and regularly customising it on the basis of user requirements.
I have come across different opinions regarding putting categorical variable as covariate in the design. What would be everybody's opinion regarding Question 1 in this thread on the below statements
1. Putting '0' and '1' or 8 channel and 32 channel in this case for the variable 'scanner Type'
or
2. Putting '1' and '2' for 8 channel and 32 channel in this case for the variable 'scanner Type'
or
3. Putting '1' for for the subjects scanned with 8 channel and '0' for the subjects scanned with 32 channel for the variable 'Eight channel' and similarly putting '1' for for the subjects scanned with 32 channel and '0' for the subjects scanned with 8 channel for the variable 'Thity-two channel'
and then how would be the correction procedure in second level model for all the these three cases. I feel the contrast to be entered like
for case 1 AllSubjects, Iron, Amyloid, IronAmyloid, and ScannerType as [0 0 0 1 0]
for case 2 AllSubjects, Iron, Amyloid, IronAmyloid, and ScannerType as [0 0 0 1 0]
for case 3 AllSubjects, Iron, Amyloid, IronAmyloid, Eight channel and Thirty-two channel as [0 0 0 1 0 0]
Which of the three option is recommended for analysis. Your suggestions are valuable
Regards
Himanshu Joshi
Threaded View
| Title | Author | Date |
|---|---|---|
| Jiri van Bergen | Nov 3, 2016 | |
| Alfonso Nieto-Castanon | Nov 4, 2016 | |
| Dilip Kumar | Jun 25, 2021 | |
| Himanshu Joshi | Jan 12, 2017 | |
| Alfonso Nieto-Castanon | Jan 27, 2017 | |
| Himanshu Joshi | Jan 25, 2017 | |
| Jiri van Bergen | Nov 7, 2016 | |