Jorge,
We are interested in examining the impact of cannabis exposure (time-varying continuous variable) on local gyrification index over 3 time points (baseline, 18 month, 36 month)- so this is a time-varying random effect. I apologize in advance if these are student questions… we are novices here… From what you said previously, we would want to model intercept+time, vs intercept+cannabis use, vs intercept+time+ cannabis use. Vs. intercept+time+ cannabis use.+covariates (alcohol use over time, gender, age, drug use over time). We are trying to figure out how to do this in Freesurfer/Matlab using the Wiki (https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels). There is a great example in there with AD/MCI groups, but none outlining an example focused on time-varying continuous variables. So with OUR question, would we organize the data as follows: Intercept Time Cannabis total (changes over time) Alcohol total (changes over time) Other Drug total (changes over time) Gender Age at baseline So, a few questions: 1) We need help writing the syntax for Matlab for testing the models (assuming linear trend was significant): a. intercept+time, vs b. intercept+cannabis use, vs c. intercept+time+ cannabis use. Vs. d. intercept+time+ cannabis use.+covariates (alcohol use over time, gender, age, drug use over time)… i. For example, this is the linear model for cortical thickness over time for the groups example: Yij = ß1 + ß2*tij + ß3*t²ij + ß4*sMCIi + ß5*sMCIi*tij + ß6*sMCIi*t²ij + ß7*cMCIi + ß8*cMCIi*tij + ß9*cMCIi*t²ij + ß10*ADi + ß11*ADi*tij + ß12*ADi*t²ij + ß13*E4i + ß14*E4i*tij + ß15*Genderi + ß16*BslAgei + ß17*Educationi + b1i + b2i*tij+ eij ii. This is the design matrix: [lhTh0,lhRe] = lme_mass_fit_EMinit(X,[1 2],Y,ni,lhcortex,3); 2) Is there a GUI available for setting up these models? (We are assuming there isn’t and that it is all matlab based.) 3) Once we test these models, is it correct that we open the spheres representing the liklihood ratio test results (corrected for multiple comparisons) and pick the “best” model based on the greatest #/size of significant clusters? Jon Jon Wieser Research Specialist UW-Milwaukee Psychology Department, Pearse Hall Rm 375 2441 East Hartford Ave Milwaukee, WI 53211 Phone: 414-229-7145 Fax: 414-229-5219 ________________________________ From: freesurfer-boun...@nmr.mgh.harvard.edu <freesurfer-boun...@nmr.mgh.harvard.edu> on behalf of jorge luis <jbernal0...@yahoo.es> Sent: Tuesday, December 2, 2014 12:34 PM To: Freesurfer support list Subject: Re: [Freesurfer] longitudinal statistics LGI Hi Jon I guess that when you say “we have continuous data as to the amount of drug usage” you actually mean that the amount-of-drug-usage is a continuous variable that changes over time for each subject. So yes you can keep this variable as a continuous variable. In fact it can even be a random effect in your statistical model. You will need to select the model with the best combination of random effects : intercept+time vs intercept+amount-of-drug-usage vs intercept+time+amount-of-drug-usage. Actually one nice feature of the LME model implemented in freesurfer vs commonly used two-levels random effects models in neuroimaging is that you can include this type of longitudinal continuous variables in the model for the mean without requiring it be included in the model for the covariance (i.e included as a random effect). You just select the best subset of random effects as explained above. -Jorge ________________________________ De: Jon Alan Wieser <wie...@uwm.edu> Para: "freesurfer (freesurfer@nmr.mgh.harvard.edu)" <freesurfer@nmr.mgh.harvard.edu> Enviado: Martes 2 de diciembre de 2014 12:29 Asunto: [Freesurfer] longitudinal statistics LGI HI freesurfer experts I have a question about the statistical analysis of longitudinal data. we have run our data through the longtudinal data processing stream. We are looking at the longitudinal effect on the LGI data We are looking at doing a Mixed effects analysis. our main Model Factor (Independent Variable) of interest is drug usage. we have continuous data as to the amount of drug usage. Can this variable be continous variable, or do we have to break it up into discrete levels of usage ( example, low, middle, high) Thanks Jon _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
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