Prof. Dr. Duolao Wang is a Professor in Biostatistics at the Liverpool School of Tropical Medicine, Liverpool, UK. Professor Wang joined the Liverpool School in March 2014. Previously, he was a lecturer and then a senior lecturer in medical statistics at the Department of Medical Statistics, London School of Hygiene and Tropical Medicine, for 15 years. He is a statistical adviser to the Lancet series and a referee for several medical journals and statistical journals. His methodological interests include statistical issues in design, analysis and reporting of clinical trials (e.g., win ratio method, Bayesian method, quasi-experimental design, QT correction methods, pharmacokinetic/pharmacodynamic modelling, meta-analysis, and trial simulation) and observational studies (e.g., longitudinal data and multilevel modelling, prognostic models, non-parametric regression, varying-coefficient models, the propensity score approach, data mining, machine learning, and big-data analysis). His areas of applied research include infectious disease, cardiovascular disease, respiratory disease, oncology, maternal and child health, mental health, nutrition, and translational research.
Prof. Dr. Duolao Wang is a Professor in Biostatistics at the Liverpool School of Tropical Medicine, Liverpool, UK. Professor Wang joined the Liverpool School in March 2014. Previously, he was a lecturer and then a senior lecturer in medical statistics at the Department of Medical Statistics, London School of Hygiene and Tropical Medicine, for 15 years. He is a statistical adviser to the Lancet series and a referee for several medical journals and statistical journals. His methodological interests include statistical issues in design, analysis and reporting of clinical trials (e.g., win ratio method, Bayesian method, quasi-experimental design, QT correction methods, pharmacokinetic/pharmacodynamic modelling, meta-analysis, and trial simulation) and observational studies (e.g., longitudinal data and multilevel modelling, prognostic models, non-parametric regression, varying-coefficient models, the propensity score approach, data mining, machine learning, and big-data analysis). His areas of applied research include infectious disease, cardiovascular disease, respiratory disease, oncology, maternal and child health, mental health, nutrition, and translational research.