This proposal outlines a new pre-doctoral training program in biostatistics that emphasizes applications to genomics and personalized medicine. Primary faculty members are from the Departments of Health Research &Policy and Statistics at Stanford University. A group of affiliate faculty from biochemistry, genetics, cardiology and other areas provide the necessary breadth for interdisciplinary research. The trainees in the program pursue a PhD in the Department of Statistics, with a concentration in Biostatistics;this option currently not available at Stanford. In addition to fulfilling the requirements of the PhD i Statistics, the proposed program will include additional course work in biomedical sciences, mentored research experienced in a collaborative setting, and training in the responsible conduct of research.
Narrative Medicine increasingly relies on high-throughput technologies in an effort to provide personalized care, targeting therapies to the specific characteristics of each patient. To translate the output of these modern technologies into information useful for medical practice, one needs to mine and understand large and complex datasets. The program that we propose will equip students with state-of-the-art methods for experimental design and data analysis, and will train them in the art of effective collaboration with medical scientists and biologists. Trainees will be empowered to take on the challenge of analyzing novel complex datasets, and from this, extract information and knowledge with the potential for improving patient care and public health.
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|Janson, Lucas; Rajaratnam, Bala (2014) A Methodology for Robust Multiproxy Paleoclimate Reconstructions and Modeling of Temperature Conditional Quantiles. J Am Stat Assoc 109:63-77|
|Zheng, Charles Y; Pestilli, Franco; Rokem, Ariel (2014) Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain. Adv Neural Inf Process Syst 27:2699-2707|
|Sankaran, Kris; Holmes, Susan (2014) structSSI: Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data. J Stat Softw 59:1-21|