Latent Growth Curve vs. Repeated Measures ANOVA

I read this article last night that I think did an excellent job of explaining the advantages of latent growth curve (LGC) modeling over using repeated measures analysis of variance (RM-ANOVA). Below is my summary. My thoughts are consistent with theirs with the exception of Similarity 2 below (which they claim is peculiar to LGC).

SIMILARITY 1: Both result in the same parameter estimates (IFF the stricter RM-ANOVA assumptions are met).

SIMILARITY 2: Both permit a wide variety of alternative specifications such as modeling nonlinearity (ALTHOUGH this is much more straightforward in the LGCs).

DIFFERENCE 1: LGC estimates are not attenuated since the measurement error has been teased out. This is actually the primary advantage of all structural models for latent variables.

DIFFERENCE 2: LGC is not restricted to RM-ANOVAs compound symmetry or sphericity assumptions...any variance/covariance structured can be incorporated and tested.

DIFFERENCE 3: LGC won't listwise delete your missing data. It uses Full Information Maximum Likelihood (FIML) to incorporate all existing data.

DIFFERENCE 4: LGC isn't restricted to equally-spaced time intervals.

Article: Llabre, M. M., Spitzer, S., Siegel, S., Saab, P. G., & Schneiderman, N. (2004). Applying latent growth curve modeling to the investigation of individual differences in cardiovascular recovery from stress, Psychosomatic Medication, 66, 29-41.