Is it appropriate to consider animal ID as random effect group level for mixed model? My gene expression data are set up as 24 animals with independent Animal.ID assigned to 3 "Status" groups (8 animals each) based on herd test results.
Each status group receives Treatment or No Treatment.
After removing extreme outliers, some treatment groups are unbalanced. My thought was to run a linear mixed effects model to account for the unbalanced groups and have "Animal.ID" as a random effect (instead of Status with only 3 levels) since there are some animal characteristics I can't control for. Is this appropriate or should I stick with a linear regression model and Type III ANOVA?
My mixed effects model:
Gene.mix <- lmer(ddCt ~ Status + Treatment + Status*Treatment + (1|Status:Animal), data=Gene1)

My linear regression model:
Gene.lm <- lm(ddCt ~ Status + Treatment + Status*Treatment, data=Gene1)

 A: The first model:
Gene.mix <- lmer(ddCt ~ Status + Treatment + Status*Treatment + (1|Status:Animal), data=Gene1)

will account for repeated measures within each unique combination of Status and Animal. You haven't explained what status is, though I'm guessing that it's the treatment group, but it is included also as a fixed effect and this rarely makes sense. You said there are 3 levels of it, so I would suggest it stays as fixed effect only, since you are clearly interesting in the "effect" of it:
Gene.mix <- lmer(ddCt ~ Status + Treatment + Status*Treatment + (1|Animal), data=Gene1)

You mention that there are unmeasured animal effects that you can't control for - this is part of the reason that there is non-independence (correlations within animals) and which is why we fit random intercepts in the first place.
The second model:
Gene.lm <- lm(ddCt ~ Status + Treatment + Status*Treatment, data=Gene1)

will not account for repeated measures within Animal. If you wanted to use a linear model you would want to fit Animal as fixed effect to handle the repeated measures (not usually a good idea when you have more than a small number of them).
As you've said, mixed models will can handle unabalanced designs.
So I would suggest that you go with the mixed model.
Are you sure the outliers are bad data, and not interesting/extreme data ?
