In a repeated measures model where assessments are performed over time, should baseline data be excluded if baseline covariate is used? In a model where subjects are evaluated over time and a baseline (time=0) covariate is used (eg, score ~ baseline + group + time + group*time), it seems that baseline data are often suggested to be excluded from the dependent variable (eg, score). But excluding baseline data would seem to decrease the model accuracy. 
The above model is statistically equivalent to a change from baseline analysis (eg, change from baseline ~ baseline + group + time + group*time). If time = 0 is not excluded, then all groups have change from baseline of 0 at time = 0, and the argument is that the 100% homogeneity at time = 0 biases the results.
 A: An additional point not mentioned in the other answers is that when you include the baseline measurement as a covariate into the model you assume a constant correlation of this measurement with all subsequent measurements, which most often is not logical to assume.
A: You have 3 choices:
Model 1: score ~ group + time + group*time) including baseline measurement
Model 2: score ~ baseline + group + time + group*time) excluding baseline measurement
Model 3: (score - baseline)~ group + time + group*time) excluding baseline measurement
Of course, you need to incorporate the correlation of the error terms into the model indirectly by specifying random effect, or directly by specifying covariance matrix of error terms, both. 
A: The advice is to not include lagged dependent variables as fixed effects predictors in a longitudinal mixed model. See here.* Technically, you are not doing that with your formulation, however I would still be careful with this. One workaround might be to switch to a structural equation modeling (SEM) framework, particularly if you have few time points. This would allow you to specify regression paths from your baseline score to each of the subsequent scores. The extent to which your theory is contingent on the baseline effect being present would determine whether the switch to SEM makes sense. 
*It may be worth posting this question to Paul Allison in that blog post. I know from firsthand experience that Paul is very responsive to questions. If you do so, please report back on what he says!
