This paper addresses some aspects of the analysis of cross-over trials with missing or incomplete data. A literature review on the topic reveals that many proposals provide correct results under the missing completely at random assumption while only some consider the more general missing at random situation. It is argued that mixed-effects models have a role in this context to recover some of the missing intra-subject from the inter-subject information, in particular when missingness is ignorable. Eventually, sensitivity analyses to deal with more general missingness mechanisms are presented.