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.papmeyer@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

 

 



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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