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help > RE: REX Results GUI: Meaning of Effect Sizes
Nov 23, 2016 04:11 PM | Alfonso Nieto-Castanon - Boston University
RE: REX Results GUI: Meaning of Effect Sizes
Dear Julian,
The positive effect sizes in the original "treatment/placebo/score_treatment/score_placebo [1 -1 0 0]" analysis indicate that, at the same level of the score covariate in the two groups, connectivity is higher (either stronger positive connectivity or weaker anticorrelations) in treatment patients compared to controls.
The positive effect sizes in the (A) analyses (treatment/placebo/score1_treatment/score1_placebo [0 0 1 -1]) indicate that the association between score1 and connectivity values is higher (more positive association or less negative association) in treatment patients compared to controls, while the negative effect sizes in the (B) analyses (treatment/placebo/score2_treatment/score2_placebo [0 0 1 -1]) indicate that the association between score2 and connectivity values is higher (more positive association or less negative association) in controls compared to treatment patients.
There is some information missing to be able to fully interpret these results (e.g. you want to know whether you are talking about positive connectivity or anti-correlations to begin with, and also whether the associations between score and connectivity values are positive or negative). In order to get all of that info I would suggest:
1) in the original seed-to-voxel analysis results explorer window select "import values" to import the actual connectivity values for each subject within your suprathreshold cluster. This will be stored as a new second-level covariate (e.g. named "ConnectivityValues")
2) go to Tools.Calculator to explore these values and their associations with the score covariates within each group. For example, selecting in the "predictor variables" list the 'treatment' and 'score1_treatment' covariates (and entering a [0 1] contrast), while selecting in the "outcome variables" list the new 'ConnectivityValues' covariate, will create a scatter plot showing the association between the score1_treatment values and the connectivity values within treatment patients only (so you can infer from this plot whether the connectivity values are actually positive or negative, and whether the association between connectivity and score1 values is positive or negative, which will help you better interpret the differences between groups that you observed)
Hope this helps
Alfonso
Originally posted by Julian Roessler:
The positive effect sizes in the original "treatment/placebo/score_treatment/score_placebo [1 -1 0 0]" analysis indicate that, at the same level of the score covariate in the two groups, connectivity is higher (either stronger positive connectivity or weaker anticorrelations) in treatment patients compared to controls.
The positive effect sizes in the (A) analyses (treatment/placebo/score1_treatment/score1_placebo [0 0 1 -1]) indicate that the association between score1 and connectivity values is higher (more positive association or less negative association) in treatment patients compared to controls, while the negative effect sizes in the (B) analyses (treatment/placebo/score2_treatment/score2_placebo [0 0 1 -1]) indicate that the association between score2 and connectivity values is higher (more positive association or less negative association) in controls compared to treatment patients.
There is some information missing to be able to fully interpret these results (e.g. you want to know whether you are talking about positive connectivity or anti-correlations to begin with, and also whether the associations between score and connectivity values are positive or negative). In order to get all of that info I would suggest:
1) in the original seed-to-voxel analysis results explorer window select "import values" to import the actual connectivity values for each subject within your suprathreshold cluster. This will be stored as a new second-level covariate (e.g. named "ConnectivityValues")
2) go to Tools.Calculator to explore these values and their associations with the score covariates within each group. For example, selecting in the "predictor variables" list the 'treatment' and 'score1_treatment' covariates (and entering a [0 1] contrast), while selecting in the "outcome variables" list the new 'ConnectivityValues' covariate, will create a scatter plot showing the association between the score1_treatment values and the connectivity values within treatment patients only (so you can infer from this plot whether the connectivity values are actually positive or negative, and whether the association between connectivity and score1 values is positive or negative, which will help you better interpret the differences between groups that you observed)
Hope this helps
Alfonso
Originally posted by Julian Roessler:
Dear Alfonso
I have another question about the meaning of the sign of the effect size in the REX displays:
In the seed to voxel analysis "Treatment : Placebo : Score_Total_Treatment : Score_Total_Placebo [1 -1 0 0]" we found significant positive connectivity to a particular ROI.
Then, our symptomscores can be divided into two subscores. So using the REXtoolbox we did the analysis for each subscore:
(A) Starting with the ROI from our seed2voxel analyis (see above) we opened the REX GUI for this particular ROI and loaded the SPM file of our 2nd level analysis for "Treatment : Placebo : subscore1_Treatment : subscore1_Placebo [0 0 1 -1]", here we got a significant positive effect.
(B) Same as above for: "Treatment : Placebo : subscore2_Treatment : subscore2_Placebo [0 0 1 -1]" here we got a significant negative effect.
How can we interpret the results?
Does the positive effect in (A) mean that the connectivity is even more positive for the subscore1 in interaction with the treatment, and the negative value in (B) that this effect is a less positive in interaction with subscore2,
OR that the connectivity is now negative in interaction with subscore 2 (and thus reflects an anticorrelation for those who score high on subscore2 under the influence of the treatment)?
Kind regards
Julian
I have another question about the meaning of the sign of the effect size in the REX displays:
In the seed to voxel analysis "Treatment : Placebo : Score_Total_Treatment : Score_Total_Placebo [1 -1 0 0]" we found significant positive connectivity to a particular ROI.
Then, our symptomscores can be divided into two subscores. So using the REXtoolbox we did the analysis for each subscore:
(A) Starting with the ROI from our seed2voxel analyis (see above) we opened the REX GUI for this particular ROI and loaded the SPM file of our 2nd level analysis for "Treatment : Placebo : subscore1_Treatment : subscore1_Placebo [0 0 1 -1]", here we got a significant positive effect.
(B) Same as above for: "Treatment : Placebo : subscore2_Treatment : subscore2_Placebo [0 0 1 -1]" here we got a significant negative effect.
How can we interpret the results?
Does the positive effect in (A) mean that the connectivity is even more positive for the subscore1 in interaction with the treatment, and the negative value in (B) that this effect is a less positive in interaction with subscore2,
OR that the connectivity is now negative in interaction with subscore 2 (and thus reflects an anticorrelation for those who score high on subscore2 under the influence of the treatment)?
Kind regards
Julian
Threaded View
| Title | Author | Date |
|---|---|---|
| Julian Roessler | Oct 19, 2016 | |
| Alfonso Nieto-Castanon | Oct 19, 2016 | |
| Yana Panikratova | Oct 1, 2019 | |
| Alfonso Nieto-Castanon | Nov 10, 2022 | |
| Yana Panikratova | Oct 21, 2023 | |
| Julian Roessler | Dec 18, 2017 | |
| Athena Demertzi | Nov 25, 2016 | |
| Julian Roessler | Nov 23, 2016 | |
| Alfonso Nieto-Castanon | Nov 23, 2016 | |
| Julian Roessler | Nov 25, 2016 | |