Rate My Professor Sofia Dias

Sofia Dias is Professor in Health Technology Assessment in the Department of Health Sciences at the University of York, where she serves at the Centre for Reviews and Dissemination. She earned her PhD in Predictive Comparisons from the University of Sheffield. As Director of the York Technology Assessment Group, she oversees the delivery of technology assessment reports for the National Institute for Health and Care Excellence through the NIHR-funded Technology Assessment Reports programme. A statistician, her research specializations include Bayesian methods for evidence synthesis applied to decision-making, network meta-analysis encompassing indirect and mixed treatment comparisons, bias-adjustment, synthesis of related outcomes, and population-adjusted treatment comparisons. She has extensive experience developing and applying evidence synthesis methods in NICE clinical guidelines and Cochrane Reviews.

Professor Dias is a member of NICE Technology Appraisals Committee D and has collaborated with the NICE Decision Support Unit to produce technical support documents that guide evidence submission and critique in NICE Technology Appraisals. She is lead author of the Wiley book Network Meta-analysis for Decision Making. Her key publications include 'On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis' (2021, Research Synthesis Methods), 'Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response, Model-Based Network Meta-analysis (MBNMA)' (2021, Medical Decision Making), and 'Exploring Heterogeneity in Histology-Independent Technologies and the Implications for Cost-Effectiveness' (2021, Medical Decision Making). She has delivered keynote lectures such as 'Evidence synthesis for decision-making: multiple treatments, multiple outcomes' (May 2021) and invited talks including 'Network meta-analysis for decision making: making best use of relevant evidence' (January 2024) and 'Evidence synthesis for decision making: making best use of relevant evidence' (December 2024).