Glen Hazlewood, MD, PhDMD Cumming School of Medicine University of Calgary, Canada
FRCPC, Specializations in Internal Medicine and Rheumatology Cumming School of Medicine University of Calgary, Canada
PhD, Clinical Epidemiology Institute of Health, Policy, Management and Evaluation University of Calgary
Areas of Research
I am interested in both qualitative and quantitative methods to understand patient preferences and incorporate these into decision making. Methods include: qualitative research, discrete-choice experiments, decision tools.
I am interested in comparative effectiveness research that seeks to understand treatment benefits and risks. Methods include: advanced longitudinal models, evidence synthesis, meta-analysis, network meta-analysis, Bayesian methods
My clinical area of interest is rheumatoid arthritis, where much of research is focused. However, I am interested in applying my methodological interests across other diseases.
Working with this supervisor
I am interested in motivated students interested in pursuing an MSc or PhD related to any of my interests. In addition I am seeking a PhD for the following project. PhD Position: A major and current concern in biomedical data science is how to tailor treatments for various conditions to individual patients, taking into account not only treatment benefits, but also possible harms and side effects. This is not only a clinical matter, but also relates to patients preferences regarding potential benefits and harms. The Bayesian approach to statistics is a very natural way to deal with such questions, building models from multiple sources of evidence, whilst also accounting for various sources of uncertainty (e.g., quality of model fit, quality of data, uncertainty of experts and/or patients expressing preferences). Dr. Glen Hazlewood (Dept. of Community Health Sciences) and Dr. Rob Deardon (Dept. of Mathematics & Statistics) are requesting applications for a fully funded 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. The ultimate goal is to produce cutting-edge models and software that can provide physicians with patient-tailored treatment recommendations. A generous University of Calgary Eyes High scholarship is available for this position. The successful applicant will have an MSc in Data Science, Statistics, Biostatistics, or equivalent, with a good GPA, and have an interest in applied clinical research methods and computational statistics. Strong communication skills are essential.
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