Jeremy M G Taylor, Ph.D.

Jeremy Taylor

Professor, Department of Biostatistics

Pharmacia Research Professor, Department of Biostatistics

Professor, Radiation Oncology

Associate Director for Biostatistics, Comprehensive Cancer Center

M4509 SPH II      Vcard icon
1415 Washington Heights
Ann Arbor, Michigan 48109-2029

Biostatistics Office: (734) 936-3287; Fax: (734) 763-2215; Cancer Center: (734) 936-9580


Website(s): Personal Website

Curriculum Vitae (PDF)

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    Syllabus (PDF)
BIOSTAT803: Biostatistics in Cancer Seminar    Syllabus (PDF)


B.A., Honours Mathematics, Cambridge University, 1978
Dip. Stat., Statistics, Cambridge University, 1979
Ph.D., Statistics, University of California, Berkeley, 1983

Research Interests & 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 cancer and  AIDS, 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 previous research in radiation oncology  has focussed on the effect of fraction size, total dose, time and volume on the biological response to radiation.  I 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, methods for analysing data from gene expression arrays, evaluation of surrogate and auxiliary variables and design of phase I trials in oncology.

Selected Publications

Search PubMed for publications by Jeremy Taylor >>

Hu M, Yu J, Taylor JMG, Chinnaiyan A, Qin Z (2010). On the Detection and Refinement of Transcription Factor Binding Sites Using ChIP-Seq Data Nucleic Acids Research, 1-14.

Li Y, Taylor JMG (2010). Predicting Treatment Effects Using Surrogate Markers in Clinical Trials Statistics in Medicine

Hsu C, Taylor JMG (2010). A Robust Weighted Kaplan-Meier Approach with Dependent Censoring Using Linear Combinations of Prognostic Covariates Statistics in Medicine

Shuman AG, Yang Y, Taylor JMG, Prince ME (2010). End of Life Care Among Head and Neck Cancer Patients Otolaryngology-Head and Neck Surgery

Taylor JMG (2010). Discussion of Predictive Comparison of Joint Longitudinal-Survival Modeling: A Case Study Illustrating Competing Approaches, by Hanson, Branscum and Johnson Lifetime Data Analysis

Li Yun, Taylor Jeremy MG (2010). Predicting Treatment Effects Using Biomarkers Data in a Meta-Analysis of Clinical Trials Statistics in Medicine

A G Shuman, P Entezami; A S Chernin, N E Wallace, J M G Taylor, N D Hogikyan, (2010). Demographics and Efficacy of Head and Neck Cancer Screening Otolaryngology-Head and Neck Surgery

Proust-Lima C, Taylor JMG, Secher S, Sandler H, Kestin L, Pickles T, Bae K, ALlison R, Williams S (2009). Confirmation of a low alpha/beta ratio for prostate cancer treated by external beam radiation therapy monotherapy using a post-treatment repeated measures model for PSA dynamics International Journal of Radiation: Oncology - Biology - Physics

Ye W, Taylor JMG Lin X (2009). Simulating Pseudo Data for a Two-stage Approach in Joint Modeling of Longitudinal Measurements and Time-to-Event Data Biometrics

Proust-Lima C, Taylor JMG (2009). Validation of a Dynamic Prognostic Tool for Prostate Cancer Recurrence Using Repeated Measures of Post-Treatment PSA Biostatistics

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.