I have a dataset including for each subject :
- 22 DV (linears, range [0-1]. These DV are intercorrelated). These measures are volume of different brain regions.
- 1 main effect to test (disease status; 0,1 or 2)
- 2 confounding IV (Age of the subject (linear), sex(binary 1 or 2) ; which have a known impact on my measures)
- 1 "site effect", as both subjects and measures are from different sites (1, 2 or 3)
My aim is to evaluate the effect of disease status on each of my 22 measures, after controlling for age, sex and site effects.
I have planned to use MANCOVA but I have to deal with non homogenous regression slopes. To overcome this problem, I have been told that I should use a linear mixed effect model, 1 model for each DV :
Measure1 ~ AGE:fixed + SEX:fixed + DISEASE_STATUS:fixed + (SITE:random)
The problem is that I would like to test whether there is a global main effect of the disease on my 22 measures, and in a second part of the analysis check which measure is significantly affected by the disease status.
Do you think that I can run my analysis this way : first apply a model for each measure
measure ~ AGE:fixed + SEX:fixed + (SITE:random)and then perform my analysis on the residuals of this first model (ie, a multivariate ANOVA) to check if there is a disease effect on residuals, and thus on DVs ?
If analysis of residuals are not appropriate, do you know if there a way to use some kind of "multivariate linear mixed effect model" ?
The reason why I do not want to apply one model to explain each DV is the multiple comparison issue, as I would dramatically inflate my alpha level.
Thank you in advance for your advices, and thank you for all the previously published answers that are very useful to non-statisticians researchers !
Thank you for your reply. I think I'll follow your advice and I'll use an FDR correction. My first question has been focused on a more general problem concerning the use of residuals as DV. Does the community have an opinion about this strategy?