Likelihood-based Missing Data Analysis in Crossover Trials

TL;DR

This paper introduces a Monte Carlo EM-based method for analyzing gene expression data in crossover trials with missing responses, enabling reliable inference despite small sample sizes.

Contribution

It develops MCEM likelihood ratio tests for fixed effects in crossover models with missing data, addressing a key challenge in such studies.

Findings

MCEM method effectively handles missing data in crossover trials

Likelihood ratio tests provide reliable inference for fixed effects

Simulation studies validate the approach

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.