# Mixed Effect Modeling with Categorical Variables

I am running an experiment where I get repeated measures over time from ~10 subjects. Because the effects of time aren't constant, I am treating timepoints categorically. So my model looks like:

Model<-lmer(data, Response ~ Categorical.Time + (1|Animal.Name))

Now when I do

summary(Model)

I get this (made up values)

            Estimate    Std. Error     df   t value  Pr(>|t|)
(Intercept)    10            2         10      20    1.00E-06
Day1           4             2         10      1     0.3
Day2           6             3         10      2     0.06
Day3           3             2         10     0.18   0.2
Day4           5             2         10      3     0.01*


Now, I figured I would also have to run an anova to get an overall effect of time on my response variable.

anova(Model), then take the p-value from that


A couple questions:

1. assuming the anova of the overall effect of time is NOT significant, should I disregard any individually significant timepoints? My thought is yes, to ignore anything if the overall time effect is ns. On the other hand, if the anova is significant I can report that the overall effect of time is significant, and Day4 specifically has a positive association with my response variable.

1. Second question: is anything else horribly wrong, from what I've written here?