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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
1T32GM096982-01A1
Application #
8267567
Study Section
Special Emphasis Panel (ZGM1-BRT-X (TR))
Program Officer
Flicker, Paula F
Project Start
2012-07-01
Project End
2017-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
1
Fiscal Year
2012
Total Cost
$89,309
Indirect Cost
$4,245
Name
Stanford University
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Janson, Lucas; Schmerling, Edward; Clark, Ashley et al. (2015) Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions. Int J Rob Res 34:883-921
Poultsides, George A; Tran, Thuy B; Zambrano, Eduardo et al. (2015) Sarcoma Resection With and Without Vascular Reconstruction: A Matched Case-control Study. Ann Surg 262:632-40
Janson, Lucas; Fithian, William; Hastie, Trevor J (2015) Effective degrees of freedom: a flawed metaphor. Biometrika 102:479-485
Schmerling, Edward; Janson, Lucas; Pavone, Marco (2015) Optimal Sampling-Based Motion Planning under Differential Constraints: the Driftless Case. IEEE Int Conf Robot Autom 2015:2368-2375
Schmerling, Edward; Janson, Lucas; Pavone, Marco (2015) Optimal Sampling-Based Motion Planning under Differential Constraints: the Drift Case with Linear Affine Dynamics. Proc IEEE Conf Decis Control 2015:2574-2581
Gholami, Sepideh; Janson, Lucas; Worhunsky, David J et al. (2015) Number of Lymph Nodes Removed and Survival after Gastric Cancer Resection: An Analysis from the US Gastric Cancer Collaborative. J Am Coll Surg 221:291-9
Starek, Joseph A; Gomez, Javier V; Schmerling, Edward et al. (2015) An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning. Rep U S 2015:2072-2078
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