Thomas Alexander Gerds

Thomas Alexander Gerds is a Professor in the Department of Public Health, Section of Biostatistics, at the University of Copenhagen's Faculty of Health and Medical Sciences. His research focuses on the development and application of statistical methods for analyzing binary, longitudinal, and time-to-event data, with a particular emphasis on causal inference and machine learning in the context of medical and public health studies.

Gerds' primary research areas include causal inference, machine learning for pharmacoepidemiology, and statistical methodology for survival analysis. He collaborates with the Danish Heart Foundation on registry data studies, aiming to improve study design and causal conclusions in observational research. His work spans prediction modelling strategies, competing risk models, and the development of statistical software for applications in cardiology and public health.

His recent publications (2024-2025) demonstrate a strong focus on applying advanced biostatistical methods to diverse medical fields. Key trends include the development of risk prediction models for conditions like kidney failure and cardiovascular disease, causal inference in pharmacoepidemiology (e.g., studying cholesterol-lowering drugs), and longitudinal mediation analysis. His work often involves registry-based cohort studies and systematic reviews, with applications in dentistry, neurology (cerebral palsy), diabetes, and emergency medicine.