Both the frequentist and Bayesian approaches are likelihood-based approaches in this case. And likelihood-based approaches give you valid results under both the missing completely at random and missing at random missing data mechanisms. This is under the proviso that the model is correctly/flexibly specified. This includes also the variance-covariance structure for the repeated measurements. That is, you should carefully postulate a model model the correlations in the repeated measurements adequately.

In both approaches you can/should work with all available data. You should not do a complete cases analysis (i.e., only consider the 15 subjects who provide all measurements) because this will be less efficient and also be valid only under missing completely at random.

Both the frequentist and Bayesian approaches are likelihood-based approaches in this case. And likelihood-based approaches give you valid results under both the missing completely at random and missing at random missing data mechanisms. This is under the proviso that the model is correctly/flexibly specified. This includes also the variance-covariance structure for the repeated measurements. That is, you should model the correlations in the repeated measurements adequately.

In both approaches you can/should work with all available data. You should not do a complete cases analysis (i.e., only consider the 15 subjects who provide all measurements) because this will be less efficient and also be valid only under missing completely at random.