Abstract

Background: The statistical analysis in presence of missing data in any study is challenging. It gets more attention since last few years for clinical trials. There are several reasons for the occurrence of missing data in the crossover trial. However, attempts toward crossover trial data are negligible.

Objective: Development of missing data handling technique to handle crossover trial data where missing data occur during the follow-up measurements.

Methodology: Data obtained from a crossover trial having microarray gene expression values are considered. The gene expression values are considered as outcomes with therapeutic effects. The statistical methodology are explained through Multiple Imputation and Bayesian approach separately. Further, their performance with same data is documented. In Bayesian context, it becomes feasible to perform the causal effect relation jointly with imputation.

Results: Multiple Imputation procedures to overcome the missing values in the dataset and thereafter performed with the mixed effect model to explore the causal effect relation between therapeutic arm on gene expression values.

Keywords: Bayesian, Crossover Design, Gene expression, Imputation.