A multivariate mixed-effects model seems to be the most appropriate for gene expression data collected in a crossover trial. It is, however, difficult to obtain reliable results using standard statistical inference 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 was adopted to deal with this situation. In addition to estimation, MCEM likelihood ratio tests (LRTs) are developed to test fixed effects in crossover models with missing data. Intensive simulation studies were conducted prior to analyzing gene expression data.

翻译：在交叉试验中收集的基因表达数据，最适宜采用多元混合效应模型进行分析。然而，当部分响应数据缺失时，使用标准统计推断很难获得可靠结果。尤其对于交叉研究而言，由于试验所需受试者数量较少，数据缺失问题尤为严峻。本研究采用基于蒙特卡洛期望最大化（MCEM）的方法应对这一情况。除参数估计外，还开发了MCEM似然比检验（LRT）用于检验存在缺失数据的交叉模型中固定效应的显著性。在分析基因表达数据之前，我们开展了大量模拟研究。