Faculty Profile

Jeremy M G Taylor, PhD

Jeremy M G Taylor, PhD

  • Professor, Department of Biostatistics

  • Pharmacia Research Professor, Department of Biostatistics

  • Professor, Department of Radiation Oncology

  • Professor, Department of Computational Medicine and Bioinformatics

  • Associate Director for Biostatistics, Comprehensive Cancer Center
  • M4509 SPH II
  • 1415 Washington Heights
  • Ann Arbor, Michigan 48109-2029

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 PhD 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.

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

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.

  • Estes J, Mukherjee B, Taylor JMG: “Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability.” Stat Biosci, pp 1-19, 2018
  • Hoban CW, Beesley LJ, Bellile EL, Sun Y, Spector ME, Wolf GT, Taylor JMG, Shuman A: “Individualized Outcome Prognostication for Patients with Laryngeal Cancer.”  Cancer, 124(4):706-716, 2018, PMC5800991.
  • Cheng W, Taylor JMG, Mukherjee B, Vokonas P, Park SK: “Improving estimation in linear regression incorporating external information from an established reduced model.” Stat Med, 37(9):1515-1530, 2018, PMC5889759.
  • Suresh K, Taylor JMG, Spratt DE, Daignault S, Tsodikov A: “Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model.” Biom J, 59(6):1277-1300, 2017, PMID: 28508545.
  • Shen J, Wang L, Taylor JMG: “Estimation of the Optimal Regime in Treatment of Prostate Cancer Recurrence from Observational Data Using Flexible Weighting Models.” Biometrics, 73(2):635-645, 2017, PMC5466876.
  • Conlon ASC, Taylor JMG, Elliott MR: “Surrogacy Assessment Using Principal Stratification and a Gaussian Copula Model.” Stat Methods Med Res, 26(1):88-107, 2017, PMC4272338.
  • Prince V, Bellile EL, Sun Y, Wolf GT, Hoban CW, Shuman AG, Taylor JMG: “Individualized Risk Prediction of Outcomes for Oral Cavity Cancer Patients” Oral Oncology, (63):66-73, 2016, PMC5193389.
  • Rice JD, Taylor JMG: “Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models.”  Stat Biosci, 8(2):333-350, 2016, PMC5173294.
  • Beesley L, Bartlett J, Wolf G, Taylor JMG: “Multiple Imputation of Missing Covariates for the Cox Proportional Hazards Cure Model.” Stat Med, 35(26): 4701-4717, 2016 PMC5053880.
  • Rizopoulos D, Taylor JMG, van Rosmalen J, Steyerberg EW, Takkenberg JJM: “Personalized Screening Intervals for Biomarkers Using Joint Models for Longitudinal and Survival Data.”  Biostatistics, 17(1):149-64, 2015, PMC46790704.
  • Boonstra PS, Mukherjee B and Taylor JMG: “Data-Adaptive Shrinkage via the Hyperpenalized EM Algorithm”.  Statistics in Biosciences,7(2):417-431, 2015 PMC4728141.
  • Boonstra PS, Shen J, Taylor JMG, Braun TM, Griffith KA, Daignault SD, Kalemkerian GP, Lawrence TS. Schipper MJ: “A Statistical Evaluation of Dose Expansion Cohorts in Phase I Clinical Trials”.  JNCI, 107(3). doi: 10.1093/jnci/dju429. 2015, PMC4565529.
  • Conlon ASC, Taylor JMG, Sargent DJ: “Multi-State Models for Colon Cancer Recurrence and Death with a Cured Fraction.”  Stat Med, 33(10):1750-66, 2014, PMC3999912.
  • McShane LM, Cavenagh MM, Lively TG, Eberhard DA, Bigbee WL, Williams PM, Mesirov JP, Polley MYC, Kim KY, Tricoli JV, Taylor JMG, Shuman DJ, Simon RM, Doroshow JH, Conley BA: “Criteria for the Use of Omics-Based Predictors In Clinical Trials.”  Nature, 502(7471):317-20, 2013, PMC4180668.
  • Taylor JMG, Park Y, Ankerst DP, Proust-Lima C, Williams S, Kestin L, Bae K, Pickles T, Sandler H: “Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models.”  Biometrics, 69(1): 206–213, 2013, PMC3622120.
  • Boonstra PS, Mukherjee B, Taylor JMG: “Bayesian shrinkage methods for partially observed data with many predictors.” Annals of Applied Statistics, 7(4):2272-92, 2013, PMC3891514.
  • Park YS, Taylor JMG, Kalbfleisch J: "Pointwise Nonparametric Maximum Likelihood Estimator of Stochastically Ordered Survivor Functions".  Biometrika, 99(2): 327-343, 2012, PMC3635706, (Snedecor Award Paper, 2013).
  • Foster, JC, Taylor, JMG, and Ruberg, SJ: “Subgroup Identification from Randomized Clinical Trial Data.”  Stats Med, 30(24): 2867-80, 2011, PMC3880775.

  • Royal Statistical Society
  • American Statistical Association
  • International Biometrics Society
  • Institute of Mathematical Statistics
  • International Statistical Institute
  • Radiation Research Society