A Bayesian analysis of the incomplete block crossover design

Abstract

In clinical trials, crossover design is widely used to assess treatment effects of drugs. Due to many practical issues, each patient in the study may receive only a subset of treatments under comparison, which is called an incomplete block crossover design. Correspondingly, the associated challenges are limited information and small sample size. In this article, we propose a Bayesian approach to analyze the incomplete block crossover design. Markov chain sampling method is used to analyze the model. We use several approaches such as data augmentation, scaled mixture of normals representation, parameter expansion to improve efficiency. The approach is illustrated using a simulation study and a real data example.

Keywords:

• Crossover trial
• Incomplete block
• Binary data
• Bayesian analysis
• Markov chain

Acknowledgement

The author thanks my colleagues for their comments and critical readings of the manuscript.

Additional information

Funding

The work is supported in part by the University Grant Program of San Diego State University (UGP 242546).