معرفی
Tron Anders Moger is a Professor at the Department of Health Management and Health Economics, Faculty of Medicine, University of Oslo. His academic career spans over two decades with a focus on biostatistical methods applied to health services research. Moger maintains active collaborations with the Department of Biostatistics at UiO and has held visiting positions at the University of Washington.
Dr. Moger's research interests center on advanced statistical methodologies for health data analysis, particularly survival analysis, frailty models, and analysis of registry data with emphasis on family data structures. His methodological expertise is applied to diverse health topics including cost-effectiveness analysis, chronic disease management, and healthcare utilization patterns. His work bridges theoretical statistics with practical healthcare applications, making significant contributions to evidence-based health policy.
Analysis of Moger's recent publications reveals a strong focus on registry-based studies examining healthcare utilization patterns across various conditions. His research demonstrates expertise in analyzing large-scale Norwegian health registries to investigate questions related to COPD management, musculoskeletal disorders, mental health services, and cardiovascular disease. A consistent theme throughout his work is the application of sophisticated statistical methods to answer pressing health services research questions with policy implications.
- INOREG (INnovations in use Of REGister data) - registry-based study analyzing care pathways
- NORCHER (Norwegian Centre for Health Services Research)
- Health Economics and Policy Group
- Economic evaluation of health technologies
Moger has made substantial contributions to teaching through courses including Research Methods and Statistics, Logistic Regression, Survival Analysis and Cox-Regression. His methodological expertise supports both clinical researchers and health economists in designing and analyzing complex health studies, particularly those utilizing registry data with familial structures.
