CORE C: The staff of the Biostatistics Core will be responsible for providing statistical support to the research of this program. The Biostatistics Core is under the supervision of Dr. Timothy D. Johnson of the Biostatistics Department at the University of Michigan, School of Public Health. The core provides assistance in the design, analysis and interpretation of preclinical and clinical experiments of the program project. Core personnel will interact with project investigators to ensure that appropriate designs and methods of analysis are used. Design issues involve dose selection, randomization, time of measurements and sample size. For analysis of data, the core will ensure that efficient methods are used. Standard graphical, group comparison and correlation methods of analysis will be used for initial investigation of the experimental data. Mixed models methods will be used for efficient use of data in experiments involving repeated measures. Core personnel are experienced in the design and analysis of both animal and clinical data. This will ensure that all data obtained from imaging measurements, tumor histology, net cell kill associated with therapy and patient outcome will be collected efficiently and analyzed appropriately.

Public Health Relevance

Overall, this research effort will provide the rationale for the use of state-of-the-art imaging registration techniques and quantitative imaging techniques for the management of clinical cancer patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA087634-10
Application #
8445397
Study Section
Special Emphasis Panel (ZCA1-GRB-P)
Project Start
Project End
2015-02-28
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
10
Fiscal Year
2013
Total Cost
$132,056
Indirect Cost
$64,964
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Nataraj, Gopal; Nielsen, Jon-Fredrick; Fessler, Jeffrey (2016) Optimizing MR Scan Design for Model-Based T1, T2 Estimation from Steady-State Sequences. IEEE Trans Med Imaging :
Larson, Eric D; Lee, Won-Mean; Roubidoux, Marilyn A et al. (2016) Automated Breast Ultrasound: Dual-Sided Compared with Single-Sided Imaging. Ultrasound Med Biol 42:2072-82
Berisha, Visar; Wisler, Alan; Hero, Alfred O et al. (2016) Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure. IEEE Trans Signal Process 64:580-591
Hero, Alfred O; Rajaratnam, Bala (2016) Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining. Proc IEEE Inst Electr Electron Eng 104:93-110
Galbán, Craig J; Ma, Bing; Malyarenko, Dariya et al. (2015) Multi-site clinical evaluation of DW-MRI as a treatment response metric for breast cancer patients undergoing neoadjuvant chemotherapy. PLoS One 10:e0122151
Brisset, Jean-Christophe; Hoff, Benjamin A; Chenevert, Thomas L et al. (2015) Integrated multimodal imaging of dynamic bone-tumor alterations associated with metastatic prostate cancer. PLoS One 10:e0123877
Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A (2015) Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA). IEEE Trans Med Imaging 34:578-88
Zhao, Feng; Fessler, Jeffrey A; Wright, Steven M et al. (2014) Regularized estimation of magnitude and phase of multi-coil b1 field via Bloch-Siegert B1 mapping and coil combination optimizations. IEEE Trans Med Imaging 33:2020-30
Boes, Jennifer L; Hoff, Benjamin A; Hylton, Nola et al. (2014) Image registration for quantitative parametric response mapping of cancer treatment response. Transl Oncol 7:101-10
Watanabe, Takanori; Kessler, Daniel; Scott, Clayton et al. (2014) Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. Neuroimage 96:183-202

Showing the most recent 10 out of 85 publications