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help > RE: Excluding Subjects
Mar 15, 2023 10:03 PM | Alfonso Nieto-Castanon - Boston University
RE: Excluding Subjects
Dear Yoshiko,
The latter, you would define a model of the form
y~Disease1 + Disease1*Age+ Disease1*Gender + Disease1*Task (0 0 0 1)
to evaluate the association between Task and connectivity across subjects within the Disease1 group only (and while controlling for age and gender covariates within the same group). The first 'disease1' term in the above model corresponds to the constant term of your regression analyses, i.e. the model is equivalent to:
y ~ Disease1 * (1 + Age+ Gender + Task)
Best
Alfonso
Originally posted by Yoshiko Yabe:
The latter, you would define a model of the form
y~Disease1 + Disease1*Age+ Disease1*Gender + Disease1*Task (0 0 0 1)
to evaluate the association between Task and connectivity across subjects within the Disease1 group only (and while controlling for age and gender covariates within the same group). The first 'disease1' term in the above model corresponds to the constant term of your regression analyses, i.e. the model is equivalent to:
y ~ Disease1 * (1 + Age+ Gender + Task)
Best
Alfonso
Originally posted by Yoshiko Yabe:
Dear Exparts,
Thank you for the informative discussion here.
I have a set of preprocessed data from multiple groups but I am interested in only the correlation between the FCs and the task perormance obtained from the group Disease1. The data from Disease2 is taken for a different study.
HC: [1 1 0 0 0 0]
Disease1: [0 0 1 1 0 0]
Disease2: [0 0 0 0 1 1]
Age: [22 23 24 21 24 25]
Gender: [1 0 1 0 1 1]
Task: [2 1 3 5 2 9]
The covariates of Disease1*Age, Disease1*Gender, and Disease1*Task were created on the SETUP>Covariates (2nd level) tab.
Should I test the between-subjects model 'y~Disease1*Age+ Disease1*Gender + Disease1*Task (0 0 1)'
or another model 'y~Disease1 + Disease1*Age+ Disease1*Gender + Disease1*Task (0 0 1)'?
Thank you very much.
Best wishes,
Yoshiko
Originally posted by Chihhao Lien:
Thanks for your discussion.
For the way 1), I'm wondering whether I understand your discussion correctly.
If I have 6 subjects (including 3 healthy controls and 3 patients) and several covariates (e.g. age and gender), I have the following 2nd-level covariates:
HC: [1 1 1 0 0 0]
Patient: [0 0 0 1 1 1]
Age: [22 23 24 21 24 25] (after orthogonalizing to all subjects, it's [-1.167 -0.167 0.833 -2.167 0.833 1.833])
Gender: [1 0 1 0 1 1] (1 for male and 0 for female)
Then, I want to remove one patient (the last patient) from the analysis, so I add new covariates.
HC_new: [1 1 1 0 0 0]
Patient_new: [0 0 0 1 1 0]
Age_new: [22 23 24 21 24 0] (after orthogonalizing to all remained subjects, it's [-0.8 0.2 1.2 -1.8 1.2 0])
Gender: [1 0 1 0 1 0]
For comparing differences between HC and Patients after controlling for the effect of age and gender, I select these 4 new covariates and input [-1 1 0 0] as the vector for between-subject contrast.
Is the result as the same if I create a new project only including the first 5 subjects?
Thanks.
Best,
Chih-Hao Lien
Thank you for the informative discussion here.
I have a set of preprocessed data from multiple groups but I am interested in only the correlation between the FCs and the task perormance obtained from the group Disease1. The data from Disease2 is taken for a different study.
HC: [1 1 0 0 0 0]
Disease1: [0 0 1 1 0 0]
Disease2: [0 0 0 0 1 1]
Age: [22 23 24 21 24 25]
Gender: [1 0 1 0 1 1]
Task: [2 1 3 5 2 9]
The covariates of Disease1*Age, Disease1*Gender, and Disease1*Task were created on the SETUP>Covariates (2nd level) tab.
Should I test the between-subjects model 'y~Disease1*Age+ Disease1*Gender + Disease1*Task (0 0 1)'
or another model 'y~Disease1 + Disease1*Age+ Disease1*Gender + Disease1*Task (0 0 1)'?
Thank you very much.
Best wishes,
Yoshiko
Originally posted by Chihhao Lien:
Dear experts,
Thanks for your discussion.
For the way 1), I'm wondering whether I understand your discussion correctly.
If I have 6 subjects (including 3 healthy controls and 3 patients) and several covariates (e.g. age and gender), I have the following 2nd-level covariates:
HC: [1 1 1 0 0 0]
Patient: [0 0 0 1 1 1]
Age: [22 23 24 21 24 25] (after orthogonalizing to all subjects, it's [-1.167 -0.167 0.833 -2.167 0.833 1.833])
Gender: [1 0 1 0 1 1] (1 for male and 0 for female)
Then, I want to remove one patient (the last patient) from the analysis, so I add new covariates.
HC_new: [1 1 1 0 0 0]
Patient_new: [0 0 0 1 1 0]
Age_new: [22 23 24 21 24 0] (after orthogonalizing to all remained subjects, it's [-0.8 0.2 1.2 -1.8 1.2 0])
Gender: [1 0 1 0 1 0]
For comparing differences between HC and Patients after controlling for the effect of age and gender, I select these 4 new covariates and input [-1 1 0 0] as the vector for between-subject contrast.
Is the result as the same if I create a new project only including the first 5 subjects?
Thanks.
Best,
Chih-Hao Lien
Threaded View
| Title | Author | Date |
|---|---|---|
| Harriet Johnston | Jun 23, 2014 | |
| Alfonso Nieto-Castanon | Jun 23, 2014 | |
| Chihhao Lien | Sep 20, 2022 | |
| Yoshiko Yabe | Mar 13, 2023 | |
| Alfonso Nieto-Castanon | Mar 15, 2023 | |
| Reza Momenan | Dec 29, 2023 | |
| Reza Momenan | Dec 29, 2023 | |
| Alfonso Nieto-Castanon | Sep 26, 2022 | |
| Chihhao Lien | Sep 27, 2022 | |
| Harriet Johnston | Jun 23, 2014 | |