Skip to main content
Explore URMC

David Oakes, PhD

David Oakes, PhDProfessor of Biostatistics and Statistics
Ph.D. (1972) London University

Contact Information

University of Rochester
Dept of Biostatistics and Computational Biology
265 Crittenden Boulevard, CU 420630 
Rochester, New York 14642-0630
Office: Saunders Research Building 4104
Phone: (585) 275-2405
Fax: (585) 273-1031

Research Interests

My major research interests are in the area of survival analysis, especially models for the effect of explanatory variables on survival and for multivariate survival data. Early contributions included investigation of the efficiency of Cox’s partial likelihood as compared with fully parametric models, development of approximate likelihood procedures for censored data and an extension of Kendall’s tau statistic for testing independence for bivariate data subject to censoring in both components. More recent work has largely focused on the models for bivariate survival models generated by unobserved random effects, often called frailties. There are close connections with so-called “Archimedean copula” models for bivariate data.

I am deeply involved in clinical trials of treatments for Parkinson’s disease and Huntington’s disease. I have also worked in cardiology, infectious diseases and pediatrics. I have a longstanding interest in occupational and environmental medicine.

I have advised a total of ten graduate students; three are now full professors (Ben Armstrong at London University, Amita Manatunga at Emory, and Jong Hyeon-Jeong at Pittsburgh), and Changyong Feng is Associate Professor at Rochester.

I am a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. I have served on the NIH Study Sections "Biostatistical Methods and Research Design" and “Epidemiology of Cancer”. From 1979-2008 I was an associate editor of the leading journal Biometrika, and currently serve as one of three Editors of the journal “Lifetime Data Analysis”. I have authored or co-authored over 200 publications, including methodologic papers in the statistical and biostatistical literature and collaborative works in the medical literature, in recent years mostly in Neurology journals. A selection of the methodologic works is given below. For a full c.v., which includes a complete list of all my publications, click here

Selected References

  • Oakes, D. (2018). Survival models and health sequences: discussion. Lifetime Data Analysis 24, 592-594.
  • Oakes, D. (2018). On the win-ratio statistic in clinical trials with multiple types of event. Biometrika 102, 742-745.
  • Oakes, D. (2013). An introduction to survival models: in honor of Ross Prentice. Lifetime Data Analysis 19, 442-463.
  • Oakes, D. and Feng, C.Y. (2010). Combining stratified and unstratified log-rank tests in paired survival data. Statistics in Medicine 29, 1735-1745.
  • Wang, A. and Oakes, D. (2008). Some properties of the Kendall distribution in bivariate Archimedean copula models under censoring. Statistics and Probability Letters 78, 2578-2583.
  • Oakes, D. (2008). On consistency of Kendall’s tau under censoring. Biometrika 95, 997-1001.
  • Oakes, D. (2005). On the preservation of copula structure under truncation. Canadian Journal of Statistics 33, 465-468.
  • Kolassa, J.E. and Oakes, D. (Eds.) (2003). Crossing Boundaries: Statistical Essays in Honor of Jack Hall. Institute of Mathematical Statistics Lecture Notes, Monographs Series # 43.
  • Oakes, D. (2001). Biometrika Centenary: Survival Analysis. Biometrika 88, 99-142. Reprinted in Titterington, D.M. and Cox, D.R. (Eds.) Biometrika: One Hundred Years, Oxford; Oxford U.P., pp 97-140. (2001).
  • Manatunga, A.K. and Oakes, D. (1999). Parametric analysis of matched pairs survival data. Lifetime Data Analysis 5, 371-387.
  • Oakes, D. (1999). Direct calculation of the information matrix via the EM algorithm. Journal of the Royal Statistical Society Series B 61, 479-482.
  • Oakes, D. (1995). Multiple time scales in survival analysis. Lifetime Data Analysis 1, 7-20.
  • Oakes, D. and Cui, L. (1994). A note on semiparametric inference for modulated renewal processes. Biometrika 81, 83-90.
  • Oakes, D., Moss, A.J., Fleiss, J.L., Bigger, J.T., Therneau, T., Eberly, S.W., McDermott, M.P., Manatunga, A., Carleen, E., Benhorin, J. and the Multicenter Diltiazem Post-Infarction Trial Research Group (1993). Use of compliance measures in an analysis of the effect of diltiazem on mortality and reinfarction after myocardial infarction. Journal of the American Statistical Association 88, 44-49.
  • Oakes, D. and Manatunga, A.K. (1992). Fisher information for a bivariate extreme value distribution. Biometrika 79, 827-832.
  • Oakes, D. and Dasu, T. (1990). A note on residual life. Biometrika 77, 409-410.
  • Oakes, D. (1989). Bivariate survival models induced by frailties. Journal of the American Statistical Association 84, 487-493.
  • Oakes, D. (1986). An approximate likelihood procedure for censored data. Biometrics 42, 177-182.
  • Oakes, D. (1986). Semi-parametric inference in a model for association in bivariate survival data. Biometrika 73, 353-361.
  • Oakes, D. (1985). Self-calibrating priors do not exist. Journal of the American Statistical Association 80, 390.
  • Cox, D.R. and Oakes, D. (1984). Analysis of Survival Data.  Chapman and Hall, London.
  • Oakes, D. (1982). A model for association in bivariate survival data. Journal of the Royal Statistical Society (Series B) 44, 414-422.
  • Oakes, D. (1982). A coefficient of concordance for censored data. Biometrics 38, 451-455.
  • Oakes, D. (1981). Survival times: aspects of partial likelihood (with discussion). International Statistical Review 49, 235-264. 
  • Anderson, S., Auquier, A., Hauck, W.W., Oakes, D., Vandaele, W. and Weisberg, H. (1980). Statistical Methods for Comparative Studies, Wiley, New York.
  • Oakes, D. (1977). The asymptotic information in censored survival data. Biometrika 64, 441-448.
  • Hawkes, A.G. and Oakes, D. (1974). A cluster process representation of self-exciting process. Journal of Applied Probability 11, 493-503.