Hi Sarah
A quadraticpolynomial can have both significant linear and quadratic terms.
Ifthere are regions of the brain exhibiting significant quadratic timeeffects
after correction for multiple comparisons then you shouldkeep the quadratic
term in your model for the mean (not necessary inthe model for the covariance).
Your question is about the generalstatistical hypothesis testing and contrasts
matter rather thanLME-specific. Any local statistician should be able to help
withthat.
Best-Jorge
De: Sarah Whittle <swhit...@unimelb.edu.au>
Para: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>; jorge luis
<jbernal0...@yahoo.es>
Enviado: Viernes 24 de abril de 2015 20:33
Asunto: Re: [Freesurfer] advice/question regarding your LME toolbox for
freesurfer
#yiv8902951642 P {margin-top:0;margin-bottom:0;}Dear Jorge,
Just wondering if you had any thoughts on the below? Particularly point 1 -
that is, how to assess whether linear or quadratic time effects fit the data
better?
Thanks,
Sarah
From: freesurfer-boun...@nmr.mgh.harvard.edu
[freesurfer-boun...@nmr.mgh.harvard.edu] on behalf of Sarah Whittle
[swhit...@unimelb.edu.au]
Sent: Tuesday, 21 April 2015 7:15 AM
To: jorge luis
Cc: freesurfer@nmr.mgh.harvard.edu
Subject: Re: [Freesurfer] advice/question regarding your LME toolbox for
freesurfer
Thanks Jorge!
In our case, at step 4, an F-test does show significant evidence for a
quadratic term, but not across the whole brain. We have a strong hypothesis
that there should be a time effect (linear or quadratic) across most of the
brain, and it makes sense that some regions will show linear and others will
show quadratic effects.
We thought that a top-down approach makes sense, whereby we drop the quadratic
term from the model to investigate significant linear time effects. This
results in overlap in regions showing a significant quadratic effect (first
model) and a significant linear effect (second model).
1. Do you have any thoughts on this approach, and on how to decide whether
there are linear or quadratic effects across different regions of the brain?
2. We also want to look at group differences. We find no significant group x
quadratic time effects, but we do find significant group x linear time effects
(in our second model). The significant regions overlap with those regions where
we found significant quadratic effects for the whole group (first model). While
this scenario makes sense to me statistically, we have had some researchers
tell us that this scenario is not possible/doesn't make sense. Do you have any
advice for looking at group differences in our situation?
Thank you for your time/help.
Sarah
From: jorge luis [jbernal0...@yahoo.es]
Sent: Tuesday, 21 April 2015 2:37 AM
To: Sarah Whittle
Cc: freesurfer@nmr.mgh.harvard.edu
Subject: Re: advice/question regarding your LME toolbox for freesurfer
Hi Sara
For those analysis I went through the following steps:
First considered a full model for both the mean and the covariance and selected
the best model for the covariance:
1- Fitted a full model (model1) including intercept, time and time squared as
both fixed effects and random effects.2- Separately fitted a model (model2)
including intercept, time and time squared as fixed effects but only including
intercept and time as random effects.3- In order to compare the previous models
I then applied the likelihood ratio test vertex-wise and corrected for multiple
comparisons using FDR. The result was that over 80% of the vertices showed no
significant results for the previous test after correcting for multiple
comparisons. So the model with three random effects wasn't significantly better
than the model with two random effects and thus I considered model2 a better
fit for the data.
Once the best model for the covariance was selected I proceeded to select the
best model for the mean:4- I tested the null hypothesis of no quadratic term in
the model for the mean using an F-test on model2. After correction for multiple
comparisons there was no significant evidence for a quadratic term so I dropped
it from the model for the mean. So the final model was one including intercept
and time as both fixed and random effects. No quadratic term included either in
the model for the mean or the model for the covariance.
Hope that helps-Jorge
De: Sarah Whittle <swhit...@unimelb.edu.au>
Para: "jber...@nmr.mgh.harvard.edu" <jber...@nmr.mgh.harvard.edu>;
"jbernal0...@yahoo.es" <jbernal0...@yahoo.es>
Enviado: Lunes 20 de abril de 2015 2:15
Asunto: advice/question regarding your LME toolbox for freesurfer
#yiv8902951642 #yiv8902951642 -- p
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BODY {direction:ltr;font-family:Tahoma;color:#000000;font-size:10pt;}Dear Dr.
Bernal-Rusiel,
I was hoping that you could please give me some advice about model selection
using the LME tools that you have developed for freesurfer. My colleague has
posted to the mailing list about this but hasn't got a response.
I am wondering specifically about the best approach for identifying quadratic
versus linear age effects for mass-univariate analysis of longitudinal data, in
addition to group differences in linear versus quadratic age effects.
I have read the following in your 2013 paper:
"After correcting for multiple comparisons, over 80% of the cortex vertices
included both the intercept and time, andnot time squared, as the optimal set
of random effects. Hence, these two random effects were included in the final
model for all remaining analyses and time squared (the quadratic term) was not
included as a random effect. We then tested the null hypothesis of no group
differences in the quadratic term (i.e., the coefficient of the “time squared”
fixed effect) and no vertex exhibited a statistically significant association
after multiple comparisons correction. Therefore, we dropped the quadratic term
from the model."
I was wondering how you did this more specifically? In the model where you
found >80% vertices included time but not time squared, was this using using
two contrasts from the same 'full' model? Or did you run one model with only
linear age effects, and then a second with linear + quadratic effects?
I have a situation where I have both significant linear and quadratic effects
(after multiple comparison correction), but neither fit >80% vertices. Some
regions have both linear and quadratic effects.
Do you have any advice/thoughts on representing these results?
Thank you for your time and I look forward to hearing your thoughts.
Sincerely,
Sarah Whittle, PhD
Senior Research Fellow
Melbourne Neuropsychiatry Centre
The University of Melboourne
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