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.
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.
|Beesley, Lauren J; Bartlett, Jonathan W; Wolf, Gregory T et al. (2016) Multiple imputation of missing covariates for the Cox proportional hazards cure model. Stat Med 35:4701-4717|
|Foster, Jared C; Nan, Bin; Shen, Lei et al. (2016) Permutation Testing for Treatment-Covariate Interactions and Subgroup Identification. Stat Biosci 8:77-98|
|Wilson, Robert H; Chandra, Malavika; Scheiman, James M et al. (2016) Tissue Classification Using Optical Spectroscopy Accurately Differentiates Cancer and Chronic Pancreatitis. Pancreas :|
|Rice, John D; Taylor, Jeremy M G (2016) Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models. Stat Biosci 8:333-350|
|Kadakia, Kunal C; Snyder, Claire F; Kidwell, Kelley M et al. (2016) Patient-Reported Outcomes and Early Discontinuation in Aromatase Inhibitor-Treated Postmenopausal Women With Early Stage Breast Cancer. Oncologist 21:539-46|
|Taylor, Jeremy M G; Cheng, Wenting; Foster, Jared C (2015) Reader reaction to ""a robust method for estimating optimal treatment regimes"" by Zhang et al. (2012). Biometrics 71:267-71|
|Elliott, Michael R; Conlon, Anna S C; Li, Yun et al. (2015) Surrogacy marker paradox measures in meta-analytic settings. Biostatistics 16:400-12|
|Taylor, Jeremy M G; Conlon, Anna S C; Elliott, Michael R (2015) Surrogacy assessment using principal stratification with multivariate normal and Gaussian copula models. Clin Trials 12:317-22|
|Foster, Jared C; Taylor, Jeremy M G; Kaciroti, Niko et al. (2015) Simple subgroup approximations to optimal treatment regimes from randomized clinical trial data. Biostatistics 16:368-82|
|Conlon, A S C; Taylor, J M G; Sargent, D J (2015) Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model. Biometrics 71:460-8|
Showing the most recent 10 out of 41 publications