This application is for continuation of a program that first began in 2002, for training in cancer biostatistics. The need for well-trained statistical scientists in biomedical research is massive and it is not being met by the number of graduates being produced by biostatistics departments across the US.
The aim of the training program is to increase the participation in cancer research of the new generation of biostatisticians who are educated not only in the powerful methods of modern statistics, but also in the biology and epidemiology of cancer, the current body of knowledge about the etiology of the disease, its detection, prevention, natural history and treatment. This training program will provide biostatisticians with the requisite scientific knowledge to understand current issues in cancer research, and training in statistical and epidemiological techniques and research methodology related to cancer. The goals of the training program are to give students who are obtaining a Ph.D. in Biostatistics or a related field (i) a solid understanding of cancer biology, (ii) experiece and ability to communicate and collaborate with cancer scientists, (iii) understanding of recent developments in cancer requiring innovative statistical research and (iv) independent research skills. The interdisciplinary program that will enable the trainees to obtain knowledge and experience in an area of cancer research and to participate as a biostatistician in an active research program under the direction of a mentor in biostatistics and a cancer scientist. The cancer research experience will be facilitated by the Cancer Center Biostatistics Unit. In addition to the biostatistics courses, the trainees will be required to take courses in cancer epidemiology, biology and genetics. The strong programmatic activities include two specifically designed courses on biostatistical issues in cancer, a journal club, a bi-annual retreat, visits to cancer research labs and meetings with invited visitors. The training program is based in the Department of Biostatistics, which was rated by the National Research Council in 2010 as the top Biostatistics department in the US. The training program is for 4 pre doctoral trainees. The training program is supported by 17 primary faculty from the departments of Biostatistics, Statistics and Epidemiology and 20 supporting faculty from the University of Michigan Comprehensive Cancer Center.

Public Health Relevance

Biostatisticians are of crucial importance to many aspects of Cancer research. They develop and provide new designs and new ways to validly analyze the increasingly complex data that is being collected in cancer research. While students in this training program receive top rate training in statistics they will learn about the science of cancer prevention, treatment and research. This training program will help seed the next generation of cancer biostatisticians.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Institutional National Research Service Award (T32)
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Subcommittee G - Education (NCI)
Program Officer
Damico, Mark W
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University of Michigan Ann Arbor
Biostatistics & Other Math Sci
Schools of Public Health
Ann Arbor
United States
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Conlon, A S C; Taylor, J M G; Sargent, D J (2014) Multi-state models for colon cancer recurrence and death with a cured fraction. Stat Med 33:1750-66
Conlon, Asc; Taylor, Jmg; Elliott, Mr (2014) Surrogacy assessment using principal stratification and a Gaussian copula model. Stat Methods Med Res :
Taylor, Jeremy M G; Shen, Jincheng; Kennedy, Edward H et al. (2014) Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication. Stat Med 33:257-74
Conlon, Anna S C; Taylor, Jeremy M G; Elliott, Michael R (2014) Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal. Biostatistics 15:266-83
Hamstra, Daniel A; Conlon, Anna S C; Daignault, Stephanie et al. (2013) Multi-institutional prospective evaluation of bowel quality of life after prostate external beam radiation therapy identifies patient and treatment factors associated with patient-reported outcomes: the PROSTQA experience. Int J Radiat Oncol Biol Phys 86:546-53
Foster, Jared C; Taylor, Jeremy M G; Nan, Bin (2013) Variable selection in monotone single-index models via the adaptive LASSO. Stat Med 32:3944-54
Mason, Heidi; DeRubeis, Mary Beth; Foster, Jared C et al. (2013) Outcomes evaluation of a weekly nurse practitioner-managed symptom management clinic for patients with head and neck cancer treated with chemoradiotherapy. Oncol Nurs Forum 40:581-6
Conlon, Anna S C; Taylor, Jeremy M G; Sargent, Daniel J et al. (2011) Using cure models and multiple imputation to utilize recurrence as an auxiliary variable for overall survival. Clin Trials 8:581-90
Foster, Jared C; Taylor, Jeremy M G; Ruberg, Stephen J (2011) Subgroup identification from randomized clinical trial data. Stat Med 30:2867-80
Shojaie, Ali; Michailidis, George (2010) Network enrichment analysis in complex experiments. Stat Appl Genet Mol Biol 9:Article22

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