Thanks,
what I mean is in order to quantify a treatment effect for the subjects
that received treatment at T0, you would need another scan before that
(Tminus1). You would need three time points:
group1: baseline, placebo, treatment
group2: baseline, treatment, placebo
you could then test if treatment or placebo differs from baseline.
If you assume that the ordering is not important, you could use a binary
variable (drug / placebo) instead of time in LME and control for the
time delta as a co-variate. This may make sense, as you are not really
interested in change / time unit.
Another model would be to use time as usual in LME and add drug as a
time varying covariate. I am not a statistician, so I don't really know
what would be best in your case.
Cheers, Martin
On 04/21/2016 06:08 PM, Martina Papmeyer wrote:
Hi Martin
Thank you very much for your response! To clarify the design: There
are 44 subjects, all have been scanned twice and thus have repeated
measures of cortical thickness. 22 subjects were first (T0) scanned 2
hours after a placebo treatment. Some days later, the identical
subjects were scanned again (T1) but this time 2 hours after a "real"
treatment. The other 22 subjects were scanned first (T0) after "real"
treatment and then some days later after placebo treatment. The
interval between T0 and T1 varies between subjects which I would like
to take into account into my analyses. The time between receiving
placebo/real treatment and MRI acquisition is identical among all
subjects (2hours) and thus not of concern.
Thank you very much for your help! Best, Martina
Sent from my iPad
On 21 Apr 2016, at 22:09, Martin Reuter <mreu...@nmr.mgh.harvard.edu
<mailto:mreu...@nmr.mgh.harvard.edu>> wrote:
Hi Martina,
so you don't have a baseline (no treatment) measurement? If you have
a treatment at T0, you mean during an interval before T0, right? But
since you did not scan before that treatment, you cannot quantify
that change? The design is not clear to me.
About the random effect (with only two time points and two groups) I
think having the intercept is enough.
Best, Martin
On 04/18/2016 11:51 AM, martina.papme...@puk.unibe.ch wrote:
Dear FreeSurfer experts
I have one question regarding my data analysis and would be
extremely thankful for any advice!
My data-set is as follows: I have repeated measures (time point 0
(T0), time point 1 (T1)) of several subjects. All individuals
underwent an intervention at one of the time points and a placebo
condition at the other time point in a fully randomized fashion.
Thus, half of the subjects received treatment at T0 and half of them
at T1. I am interested in the putative effect of the intervention on
cortical thickness in a ROI. A major challenge is that the time
between T0 and T1 varies between individuals and that I expect the
time to impact on my dependent variable and to likely interact with
the condition (treatment versus placebo).
I thought about conducting a simple repeated-measures ANOVA.
However, as stated, I want to take the time between the two sessions
into account. I also thought about an analysis of rates or percent
changes. However, this approach does not model the correlation among
the repeated measures and is thus associated with a reduction in power.
Accordingly, I am trying to use lme models to analyse my data. Since
I have no between-group variable but a within-subjects design, I am
concerned if my thoughts are correct and would be grateful for feedback.
I ran the longitudinal FS stream and followed the longitudinal lme
model tutorial. I propose the following lme model with one random
factor: thickness = intercept (random factor) + time since baseline
+ ICV + condition (placebo or treatment) + timeXcondition + Age
(does not change across time interval) + gender
The analysis finishes with 0% non-covergence. Can you tell me if my
model is suitable given the fact that it is a within-subjects
design? I also started wondering if it was possible to model time as
a random factor but I think that I read that this is not suitable if
you only have two groups (in my case: conditions).
Thank you very much for help and advice!
All best wishes, Martina
Universitäre Psychiatrische Dienste Bern (UPD)
*Universitätsklinik für Psychiatrie und Psychotherapie*
Systemische Neurowissenschaften der Psychopathologie
Zentrum für Translationale Forschung
Dr. phil. Martina Papmeyer, Wissenschaftliche Mitarbeiterin
Bolligenstrasse 111, CH-3000 Bern 60
Tel: ++41 0(31) 930 9599, Fax: ++41 0(31) 930 9961
Mail: martina.papme...@puk.unibe.ch
www.puk.unibe.ch
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--
Martin Reuter, PhD
Assistant Professor of Radiology, Harvard Medical School
Assistant Professor of Neurology, Harvard Medical School
A.A.Martinos Center for Biomedical Imaging
Massachusetts General Hospital
Research Affiliate, CSAIL, MIT
Phone: +1-617-724-5652
Web :http://reuter.mit.edu
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Martin Reuter, PhD
Assistant Professor of Radiology, Harvard Medical School
Assistant Professor of Neurology, Harvard Medical School
A.A.Martinos Center for Biomedical Imaging
Massachusetts General Hospital
Research Affiliate, CSAIL, MIT
Phone: +1-617-724-5652
Web : http://reuter.mit.edu
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