معرفی
Dr. David McAllister is a Wellcome Trust Intermediate Clinical Fellow at the University of Glasgow's Institute of Health and Wellbeing, where he has been employed since December 2016. His research bridges clinical medicine and advanced epidemiological methods, focusing on generating evidence that directly informs real-world healthcare practice.
Dr. McAllister's research primarily centers on diabetes (both Type 1 and Type 2), multimorbidity, and cardiovascular outcomes. His work examines how treatments perform in diverse patient populations beyond the controlled settings of clinical trials, with particular attention to how factors like age, sex, and multiple chronic conditions affect treatment effectiveness. He has developed expertise in analyzing routinely collected health data from sources like UK Biobank and national healthcare databases to address methodological challenges in comparative effectiveness research.
His recent publications (2023-2025) demonstrate a strong focus on precision medicine approaches, examining sex and age differences in treatment responses, developing methods for better clinical guideline development, and investigating how social factors interact with clinical conditions. His work frequently appears in high-impact journals including JAMA, Diabetes Care, and Nature Communications.
Dr. McAllister has secured substantial research funding, including multiple Wellcome Trust awards, Medical Research Council grants, and Diabetes UK funding. His current projects (running through 2029) focus on standardized protocols for research using routinely collected health data, understanding problematic polypharmacy in diabetes, and predictors of early trial termination.
- Wellcome Trust Intermediate Clinical Fellowship
- Canadian Institutes of Health Research grant (2024-2029)
- Diabetes UK grant (2024-2029)
- Medical Research Council grants
- UK Research and Innovation funding
His methodological expertise includes individual participant data meta-analysis, network meta-analysis, instrumental variable methods, and latent class analysis for multimorbidity clustering. These approaches allow him to address critical questions about how treatments work for specific patient subgroups in real-world settings, helping bridge the gap between clinical trial evidence and everyday medical practice.
