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Applications: Bioinformatics, Climate Research, Epidemiology, Genetics, Genomics, Human Subjects Trials and Intervention Studies, Population Sciences, Precision Health Methodologies: Bayesian Methods, Causal Inference, Classification, Data Mining, Dynamical Models, Heterogeneous Data Integration, High-Dimensional Data Analysis, Longitudinal Data Analysis, Machine Learning, Missing Data and Imputation, Predictive Modeling, Statistical Inference, Statistical Modeling, Statistics Relevant Projects:

NIH R01 in Statistical Methods for Cancer Biomarkers

Jeremy M G Taylor

Professor, Biostatistics


Computational Medicine and Bioinformatics, Radiation Oncology

Jeremy Taylor, PhD, is the Pharmacia Research Professor of Biostatistics in the School of Public Health and Professor in the Department of Radiation Oncology in the School of Medicine at the University of Michigan, Ann Arbor. He is the director of the University of Michigan Cancer Center Biostatistics Unit and director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England; the University of Adelaide; INSERM, Bordeaux and CSIRO, Sydney, Australia. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.

I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.