Estimation of Treatment Effect with Missing Observations for Three Arms and Three Periods Crossover Clinical Trials

Atanu Bhattacharjee ()
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Atanu Bhattacharjee: Centre for Cancer Epidemiology, Tata Memorial Centre

Annals of Data Science, 2020, vol. 7, issue 3, No 6, 447-460

Abstract: Abstract 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. This manuscript is dedicated towards development of missing data handling technique for three arms three periods crossover trial.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. However, we failed to perform it through mixed effect model jointly. We performed separately 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: Missing data; Cross over trial; Multiple imputation; Bayesian; Simulation; OpenBUGS (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s40745-020-00303-y

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