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.
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.
|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|
|van Die, M Diana; Williams, Scott G; Emery, Jon et al. (2017) A Placebo-Controlled Double-Blinded Randomized Pilot Study of Combination Phytotherapy in Biochemically Recurrent Prostate Cancer. Prostate 77:765-775|
|Meurer, William J; Seewald, Nicholas J; Kidwell, Kelley (2017) Sequential Multiple Assignment Randomized Trials: An Opportunity for Improved Design of Stroke Reperfusion Trials. J Stroke Cerebrovasc Dis 26:717-724|
|Henry, N Lynn; Speth, Kelly; Lintermans, Anneleen et al. (2017) Associations Between Patient and Anthropometric Characteristics and Aromatase Inhibitor Discontinuation. Clin Breast Cancer 17:350-355.e4|
|Wilson, Robert H; Chandra, Malavika; Scheiman, James M et al. (2017) Tissue Classification Using Optical Spectroscopy Accurately Differentiates Cancer and Chronic Pancreatitis. Pancreas 46:244-251|
Showing the most recent 10 out of 93 publications