Most scientists can think of the mixed model in Prism as repeated measures ANOVA that allows missing values.
Here is some suggested wording for explaining how repeated measures ANOVA with Prism are done (using the Mixed-model approach):
Repeated measures ANOVA cannot handle missing values. We analyzed the data instead by fitting a mixed model as implemented in GraphPad Prism 8.0. This mixed model uses a compound symmetry covariance matrix, and is fit using Restricted Maximum Likelihood (REML). In the absence of missing values, this method gives the same P values and multiple comparisons tests as repeated measures ANOVA. In the presence of missing values (missing completely at random), the results can be interpreted like repeated measures ANOVA.
Also state whether or not you asked Prism to use the Geisser-Greenhouse correction.
We provide details to statistical consultants in two ways.
We have written FAQs that contain a Prism file along with corresponding R and SAS code for one-way repeated measures anova, and for two-way repeated measures ANOVA with repeated measures in both factors.
The fitting of the mixed model is the same whether or not you selected the Geisser-Greenhouse correction. The Geisser-Greenhouse correction only changes the degrees of freedom used when calculating P values from F ratios. So all results except the P value will be the same with and without the correction.