Likelihood-based Missing Data Analysis in Crossover Trials
Methodology
2023-09-12 v2 Applications
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 (LRTs) are developed to test fixed effects in crossover models with missing data. Intensive simulation studies were conducted prior to analyzing gene expression data.
Cite
@article{arxiv.2103.06567,
title = {Likelihood-based Missing Data Analysis in Crossover Trials},
author = {Savita Pareek and Kalyan Das and Siuli Mukhopadhyay},
journal= {arXiv preprint arXiv:2103.06567},
year = {2023}
}