Abstract
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 are developed to test fixed effects in crossover models with missing data. Intensive simulation studies were conducted prior to analyzing gene expression data.
Acknowledgments
The authors thank Dr. Atanu Bhattacharjee from Tata Memorial Center, Mumbai, India, for providing valuable assistance in obtaining the gene data set. Also, the authors would like to thank the anonymous referees, Associate Editor, and Editor for their insightful comments, which have significantly improved the quality of this research article.
Citation
Savita Pareek. Kalyan Das. Siuli Mukhopadhyay. "Likelihood-based missing data analysis in crossover trials." Braz. J. Probab. Stat. 37 (2) 329 - 350, June 2023. https://doi.org/10.1214/23-BJPS570
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