Bayesian methods for studying patient-centered comparative effectiveness
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. Dr. Rob Deardon (Dept. of Mathematics & Statistics and Faculty of Veterinary Medicine) and Dr. Glen Hazlewood (Cumming School of Medicine) are requesting applications for a doctoral student position to explore the use of applied Bayesian methods for patient-centered comparative effectiveness research.
The student will have access to large data sets on treatment benefits and harms (including datasets from network meta-analysis and observational cohorts) and data from patient preference studies. The student 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. 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.
Competitive funding for this position is available, but it is expected that the student will also apply for external salary support. The successful applicant will have an MSc in Statistics, Biostatistics, or equivalent, and have an interest in applied clinical research methods and computational statistics. Strong communication skills are essential.
Please send a CV/resume to Dr Rob Deardon (email@example.com) by January 31, 2017 for consideration for the position.
For further details about studying at the University of Calgary please see http://www.ucalgary.ca/about/