This application is for continuation of a program that first began in 2002, for training four pre-doctoral students 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 Epidemiology (i) a solid understanding of cancer biology, (ii) experience 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, Analytics and Bioinformatics Shared Resource. 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 a specifically designed seminar course 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 will draw students primarily from the department of Biostatistics, but also from the department of Epidemiology. The training program is supported by 17 Primary Faculty from the departments of Biostatistics and Epidemiology and 13 Supporting Faculty from the University of Michigan Comprehensive Cancer Center.

Public Health Relevance

Project Goals The goals of this project are to train four pre-doctoral trainees in Cancer Biostatistics. The trainees will be from the departments of Biostatistics and Epidemiology at the University of Michigan. The trainees will be mentored by 17 Primary Faculty from the departments of Biostatistics and Epidemiology and by 13 Supporting Faculty from the University of Michigan Comprehensive Cancer Center. The training will be achieved through a variety of mechanisms. While the students obtain their primary degree, they will learn about the science of cancer through course-work, mentored involvement in cancer projects in the Cancer Center and participation in conferences. Trainees will write a cancer focused PhD thesis. Program specific activities include a Cancer-Biostatistics seminar, a journal club, laboratory tours, a bi-annual retreat and meetings with visitors.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Institutional National Research Service Award (T32)
Project #
2T32CA083654-16
Application #
9491236
Study Section
Subcommittee I - Transistion to Independence (NCI)
Program Officer
Damico, Mark W
Project Start
2002-07-01
Project End
2023-03-31
Budget Start
2018-04-01
Budget End
2019-03-31
Support Year
16
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Muenz, Daniel G; Braun, Thomas M; Taylor, Jeremy Mg (2018) Modeling adverse event counts in phase I clinical trials of a cytotoxic agent. Clin Trials 15:386-397
Hoban, Connor W; Beesley, Lauren J; Bellile, Emily L et al. (2018) Individualized outcome prognostication for patients with laryngeal cancer. Cancer 124:706-716
Bhave, Manali A; Speth, Kelly A; Kidwell, Kelley M et al. (2018) Effect of Aromatase Inhibitor Therapy on Sleep and Activity Patterns in Early-stage Breast Cancer. Clin Breast Cancer 18:168-174.e2
Manohar, Poorni M; Beesley, Lauren J; Bellile, Emily L et al. (2018) Prognostic Value of FDG-PET/CT Metabolic Parameters in Metastatic Radioiodine-Refractory Differentiated Thyroid Cancer. Clin Nucl Med 43:641-647
Cheng, Wenting; Taylor, Jeremy M G; Vokonas, Pantel S et al. (2018) Improving estimation and prediction in linear regression incorporating external information from an established reduced model. Stat Med 37:1515-1530
Kidwell, Kelley M; Seewald, Nicholas J; Tran, Qui et al. (2018) Design and Analysis Considerations for Comparing Dynamic Treatment Regimens with Binary Outcomes from Sequential Multiple Assignment Randomized Trials. J Appl Stat 45:1628-1651
Conlon, Asc; Taylor, Jmg; Elliott, M R (2017) Surrogacy assessment using principal stratification and a Gaussian copula model. Stat Methods Med Res 26:88-107
Wallner, Lauren P; Li, Yun; Furgal, Allison K C et al. (2017) Patient Preferences for Primary Care Provider Roles in Breast Cancer Survivorship Care. J Clin Oncol 35:2942-2948
Kadakia, Kunal C; Kidwell, Kelley M; Seewald, Nicholas J et al. (2017) Prospective assessment of patient-reported outcomes and estradiol and drug concentrations in patients experiencing toxicity from adjuvant aromatase inhibitors. Breast Cancer Res Treat 164:411-419
Conlon, Anna; Taylor, Jeremy; Li, Yun et al. (2017) Links between causal effects and causal association for surrogacy evaluation in a gaussian setting. Stat Med 36:4243-4265

Showing the most recent 10 out of 93 publications