Professional Summary
Jeremy Taylor is the Pharmacia Research Professor of Biostatistics and a Professor in the Department of Radiation Oncology in the School of Medicine. He is the director of the University of Michigan Cancer Center Biostatistics Unit. He is 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 and INSERM, Bordeaux. 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, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.
Courses Taught
BIOSTAT699: Analysis of Biostatistical Investigations
BIOSTAT803: Biostatistics in Cancer Seminar
Education
Ph.D., Statistics, University of California, Berkeley, 1983
B.A., Honours Mathematics, Cambridge University, 1978
Research Interest & Projects
My interests are the theory and application of statistics to biomedical problems. I believe that data-driven, robust, flexible statistical methods should be used, incorporating scientific knowledge from the substantive area of investigation. A good statistician has to be heavily involved in the underlying science of the data he/she is investigating. My research has focused on the application of statistics to AIDS and cancer, especially to radiation oncology. The specific areas are modelling, survival analysis and longitudinal data. This has led to developing methods involving the use of mixture models, stochastic processes and multiple imputation. My theoretical statistical interests are in Box-Cox power transformations, robust methods, nonparametrics and smoothing techniques. My work in radiation oncology research has focussed on the effect of fraction size, total dose, time and volume on the biological response to radiation. Much of this work has been in collaboration with Rodney Withers. We have studied the response of both tumours and normal tissues to radiation. My work related to cancer has focused on modelling and evaluating biomarkers, particularly PSA in prostate cancer. More recent applied work has been concerned with methods for combining multiple biomarkers and methods for analysing data from gene expression arrays.
Selected Publications
Faucett, C. L., Schenker, N., Taylor, J. M. G. (2002). Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data. Biometrics, 58, 37-47.
Wang, Y., Taylor, J.M.G (2001). Jointly modeling longitudinal and event time data: Application in AIDS studies. Journal of the American Statistical Association, 96, 895-905.
Sy, J.P. and Taylor, J.M.G. (2000). Estimation in a Cox proportional hazards cure model. Biometrics, 56, 227-236.
Solomon, P.J. and Taylor, J.M.G. (1999). Orthogonality and transformations in variance components model. Biometrika, 86, 289-300.
Siqueira, A., Taylor, J.M.G. (1999). Treatment effects in a logistic model involving the Box-Cox transformation. Journal of the American Statistical Association, 94, 240-246.
Taylor, J.M.G., Siquiera, A.L. and Weiss, R.E. (1996). The cost of adding parameters to a model. J. Royal Stat. Soc. Series B, 58, 593-607.
Professional Affiliations
Royal Statistical Society
American Statistical Association
International Biometrics Society
Institute of Mathematical Statistics
International Statistical Institute
Radiation Research Society
Additional Information
Director of the Cancer/Biostatistics training program.
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