What happens if a value is missing? With repeated measures ANOVA, you can't use any of the data for that subject. To do repeated measures ANOVA, you'd need to remove the data for that participant/animal/whatever entirely from the data table before running the ANOVA.

Beginning with Prism 8, Prism offers an alternative method to analyze repeated measures data: fitting a mixed effects model. This analysis works fine even when there are some missing values. This mixed model choice is offered in the ANOVA parameters dialogs.

Missing values are also the situation in which the choice of denominator degrees of freedom method matters most. The Legacy method computes degrees of freedom from the design rather than from the data, so it keeps crediting the analysis with information the missing values took away, and its P values come out too small. Use Kenward-Roger, or Satterthwaite, whenever your repeated measures data have missing values. See denominator degrees of freedom in the mixed model.

The results of fitting a mixed model with missing values will be meaningful, of course, only if the values are missing for random reasons. The results will probably be misleading if the values are  missing because those participants were very sick, or those values were too high to measure (or too low to measure). If the data truly are repeated measures over time and all missing values are at the last time point or last few times points, they are unlikely to be missing at random, but rather missing because something happened to those participants (or animals...) over the course of the study.