Likelihood-based missing data analysis in multivariate crossover trials

For gene expression data measured in a crossover trial, a multivariate mixed-effects model seems to be most appropriate. Standard statistical inference fails to provide reliable results when some responses are missing. Particularly for crossover studies, missingness is a serious concern as the trial requires a small number of participants. A Monte Carlo EM (MCEM) based technique has been adopted to deal with this situation. Along with estimation, a MCEM likelihood ratio test (LRTs) is developed for testing the fixed effects in such a multivariate crossover model with missing data. Intensive simulation studies have been carried out prior to the analysis of the gene expression data.