Context: I am working on a longitudinal study that aim to analyse a response variable in Alzheimer's disease population and determine the differences between 4 groups of the AD population
I want to fit a linear mixed model and I was wondering if it's necessary to add the age and sex as covariates in my model when the groups are already age- and sex-matched? :
LMM.fit<-lmer(DV ~ Groups + Age + Sex + (1|Subject),data)If the answer is yes (I need to add age and sex as covariates in my model), can I just skip the age- and sex-matching step in the future (since there is an adjustment for Age and Sex in the model)?
