Print

Print

Hello Anderson,

 

Yes, by order I meant the order in which the 3 interventions were applied.

 

In the example, the third spreadsheet shows EV12 and EV13 which are an example of the FD of two conditions with the reversed sign (so the average of FD is the nuisance). Is it correct?

Thanks a lot

Yacila

 

Von: FSL - FMRIB's Software Library [mailto:[log in to unmask]] Im Auftrag von Anderson M. Winkler
Gesendet: Freitag, 3. Februar 2017 08:16
An: [log in to unmask]
Betreff: Re: [FSL] AW: [FSL] Linear or quadratic effects with permutations testing?

 

Hi Yacila,

 

Please see below:

 

 

On 2 February 2017 at 12:27, Yacila Isabela Deza Araujo <[log in to unmask]> wrote:

Thanks a lot for your reply Anderson.

I have another question regarding the use of covariates in within and between subjects designs in PALM:

As far as I understood, age and gender  do not need to be considered as covariates because they do not change between the measures, but what about intervention order?

Can I create a variable with intervention order to see whether it has effects in my designs, even when they are not significant between groups?

 

Can you give details? By order do you mean the order in which placebo, agonist and antagonist were applied? Or is it something related to the groups?

 

And motion measures such as FD? I am working with resting state data and I cleanes a lot before. There are not differences in FD between groups. Should I still include the mean FD per subject and intervention?

 

Yes, please see here, the 3rd spreadsheet: https://dl.dropboxusercontent.com/u/2785709/outbox/mailinglist/design_yacila.ods

 

You need to decide whether when looking into differences between conditions, it's the difference between FD that is a nuisance, or the average between FD that is the nuisance. In the former case, enter the FD values "as is". In the latter, enter with the first value with the sign reversed for each subject (as in the example file above).

 

For the between-subject effects, it goes same way, except that the logic reverses: reverse the sign for the difference, or keep as is for the average.

 

My last question is about the place where these nuisance variables should be: Should I include them in the within or between subjects design? If I include them in both, my design will be rank deficient, right?

 

These designs operate separately, and FD can go in both.

 

All the best,

 

Anderson

 

 

I am looking forward to your useful comments

 

Thanks a LOT!

Yacila

 

Von: FSL - FMRIB's Software Library [mailto:[log in to unmask]] Im Auftrag von Anderson M. Winkler
Gesendet: Samstag, 21. Januar 2017 14:54
An: [log in to unmask]
Betreff: Re: [FSL] Linear or quadratic effects with permutations testing?

 

Hi Yacila,

 

Please, see below:

 

 

On 20 January 2017 at 10:06, Yacila Isabela Deza Araujo <[log in to unmask]> wrote:

Dear FSL experts,

(especially Anderson),

 

I have two groups and three conditions. Anderson already provided me with a design and contrast template that can be used in Palm.

My main question is: Is it possible to have linear contrast (or quadratic) with permutation testing such as randomise or PALM? My conditions are antagonist, placebo and agonist, so I expected some linear effect more than a comparison between conditions.

 

Do you mean a quadratic contrast with these 3 conditions only? With only 3 values (even if many subjects), this will be prone to overfitting. Testing the pairwise differences between the 3 conditions, and these between the groups (as in the design you have) is probably more informative and less error prone.

 

If you want something linear as in the order A>B>C, then testing A>C leads to the same result (and you have this contrast already, so nothing to change).

 

 

My second question is whether is valid to investigate main intervention effects in the total sample, even when I know I have two groups . (It is a genetic marker, so I also expect differences)

 

Yes, it's ok, provided there are no group differences. Otherwise the test is still valid (i.e., "statistically valid") although it may not be informative.

 

All the best,

 

Anderson

 

 

 

Thanks a lot for the attention!

 

Yacila