معرفی
Mark Lunt is a Reader in Medical Statistics at the University of Manchester's Arthritis Research UK Epidemiology Unit within the Faculty of Biology, Medicine and Health. With the Unit since 1999, he specializes in advanced statistical methods for observational studies and causal inference in rheumatology research. His expertise spans propensity score methods, statistical modeling, and handling missing data in complex epidemiological studies.
Educational Background:
- B.Sc. in Mathematics from Warwick University (1985)
- PGCE (1987)
- M.Sc. in Medical Statistics from London School of Hygiene and Tropical Medicine
- Ph.D. in statistical methods for identifying vertebral fractures from Open University (completed 2003)
Lunt's primary research focuses on estimating treatment effects from observational studies, particularly addressing confounding by indication. His work develops and applies methods to compare treated and untreated subjects with similar propensity scores to obtain unbiased treatment effect estimates. He has extensive experience with generalized linear models, propensity methods, and instrumental variables approaches.
His recent publication trends show strong focus on psoriasis treatment effectiveness, opioid utilization in rheumatic diseases, and work productivity in inflammatory arthritis. These studies leverage large patient registries like BADBIR and employ sophisticated statistical techniques to address real-world clinical questions.
Lunt teaches the 'Statistical Modelling in Stata' course annually and contributes to M.Sc. Rheumatology and Translational Medicine programs. He has been involved in multiple major research projects including the Centre for Epidemiology Versus Arthritis and the British Society for Rheumatology Rheumatoid Arthritis Register.
His research team operates within the Arthritis Research UK Epidemiology Unit, collaborating extensively with international partners including Harvard University where he spent a sabbatical year (2006-2007) studying causal inference in the Pharmacoepidemiology Group.
