Kamso Mohammed Mujaab

Comparative effectiveness research is a broad field of research that aims to provide ‘real-world’ estimates of treatment benefits and harms to help inform treatment decisions. Patients’ preferences for these benefits and harms should then be used to guide decision-making. Bayesian methods offer advantages as they facilitate the incorporation of multiple sources of evidence, while readily accounting for the uncertainty in their estimation. In this project, we will have access to large datasets on treatment benefits and harms (including datasets from network meta-analysis and observational cohorts) and data from patient preference studies. We will explore the use of Bayesian methods to synthesize comparative effectiveness research and to inform the design of future clinical trials in view of the existing evidence base and patients’ preferences. We shall explore the dataset using the fixed effects and random effects models in the estimates of the parameters. The research will focus on rheumatoid arthritis treatment, which is of high interest to multiple stakeholders, given the increased availability of highly effective, but expensive treatment options.