Application of Bayesian hierarchical models for the analysis of complex clinical trials : an analysis strategy based on two case studies in dental research

• Main Author: Gonzalez-Maffe, Juan Guillermo
• Other Authors: Richardson, Sylvia ; Ashby, Deborah
• Published: Imperial College London 2012
• Subjects: 610.72
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.693882

Aim: To develop a strategy for the analysis of complex experiments using Bayesian hierarchical models, and demonstrate the advantage of the Bayesian formulation when analysing complex experiments. Methods: There is increased popularity in designing complex experiments; such experiments help amplify the efficiency of clinical research. The Bayesian approach is a natural candidate to tackle complex problems in a straightforward manner as it handles efficiently large amounts of missing data and multivariate responses data. Joint models are formulated in order to deal with missing data and multivariate data. A strategy is developed for the analysis of complex experiments based on two clinical experiments in dentistry. Data: Two clinical experiments in dental research are selected for analysis. In dentistry, we encounter complex experiments as the individual units are the teeth which are clustered within subjects. Results and Conclusion: The results indicate that using Bayesian joint models improve the parameters estimation while taking into account the entire complexity of the study design. The Bayesian formulation gives us the advantages to estimate complex joint models in a straightforward manner. Bayesian joint models can deal with missing data and multivariate data efficiently given the exibility by the MCMC analysis. The joint model propagates the entire uncertainty in the model into the posterior distribution. We can easily extend the model to account for different types of missing data, and/or account for different correlation structures when dealing with multivariate data.