treating measurement occasions as a numerical or as a factor predictor

Hi all,

I am (almost) sure this question has been asked before but I am new
here and I have not found the best answer yet after some googling.

I would like to ask a question about treating measurement occasions in
a longitudinal analysis specifically when using linear mixed model. In
my study, I have taken data on 3 separate occasions (at baseline, at 1
month and at 3 months post baseline). I am not sure what is best
approach treat these measurement occasions in my analysis using lmer
or lme functions. Should I treat them as a numeric or as a factor
variable. My feeling says that I should treat such measurement
occasions as a factor but I do not have strong theoretical reasons for
that.

Treating them as a factor predictor will make my nlme::lme codes like these;

for random intercept:
lme(y~1+ covariateA+factor(occasion), random=~1|subject, data=data)
and for random slope:
lme(y~1+covariteA+factor(occasion),random=~1+factor(occasion)|subject,
data=data)

I appreciate someone can enlighten me on this issue or point to any
useful literature.

Thanks very much.

Best wishes,

Kamarul

Dr. Kamarul Imran Musa (MD MCommunityMed)
Dept of Community Medicine,
School of Medical Sciences,
Universiti Sains Malaysia,
16150 Kbg Kerian Kelantan
MALAYSIA

ResearcherID: http://www.researcherid.com/rid/N-3198-2015
Personal blog: http://designdataanalysis.wordpress.com
Email : drki.musa at gmail.com , k.musa at lancaster.ac.uk