A training program is proposed to increase the participation of biostatisticians in key application areas of the biosciences. Students in this program will be educated not only in the powerful methods of modern statistics, but also in a structured sequence of courses in genetics, biology and bioinformatics. The program will have two streams: the first will continue with a further series of courses in molecular biology and bioinformatics; the second will continue with further courses, depending on the orientation of the student, in areas of epidemiology, clinical and/or behavioral research. The purpose of this training program is to provide biostatisticians with the requisite scientific knowledge and experiences to understand current issues in the biosciences, and to be able to participate as key members of interdisciplinary teams. The goals of the proposed training program are to give students who are obtaining a Ph.D. in Biostatistics (i) a solid understanding of and experience in research in the Biosciences, (ii) experience and ability to communicate and collaborate with researchers working in the Biosciences, (iii) an understanding of recent developments in the scientific disciplines that require innovative statistical research, and (iv) independent research skills. We propose to have four predoctoral trainees in year 1, and six in each of the four subsequent years. This training program is interdisciplinary in character. It will include practical experience and lab rotations to research projects in areas of the biosciences. The student will develop the ability to participate fully as a biostatistician in these projects under the mentorship of a faculty member with expertise in biostatistics and researcher from the biosciences. The program will have an emphasis on high level statistical modeling in applications in the biosciences. A new Ph.D. course will be developed in stochastic processes including applications in the biosciences. The training program is supported by participating and associated faculty from the Department of Biostatistics and eleven other Departments at the University of Michigan. ? ? ?
| Boonstra, Philip S; Mukherjee, Bhramar; Taylor, Jeremy M G et al. (2011) Bayesian modeling for genetic anticipation in presence of mutational heterogeneity: a case study in Lynch syndrome. Biometrics 67:1627-37 |
| 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 |
| Boonstra, Philip S; Gruber, Stephen B; Raymond, Victoria M et al. (2010) A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome. Genet Epidemiol 34:756-68 |
