The Biostatistics Core provides expertise in statistical science to ensure scientific rigor in study design, statistical analysis, and interpretation of cancer research studies. The Biostatistics Core is grouped in the Cross Disciplinary Research Core Cluster which, in addition to the Biostatistics Core, includes the Pharmacology, Genomics, and Biobanking and Correlative Sciences Cores. Members of the Core have expertise in a wide range of scientific disciplines including design and analysis of clinical trials, observational studies, in vitro and in vivo studies, biomarker discovery and validation projects, bioinformatics projects in collaboration with the Genomics Core and metabolomics studies in collaboration with the Pharmacology Core. They collaborate with cancer center investigators in the planning phase of studies to ensure that their hypotheses are testable and that the data will be adequate to address the study questions. They provide expertise on data collection and management and devise analysis plans consistent with the study objectives. At the completion of the study they evaluate the data for conformity with the assumptions of the statistical tests, provide estimates of the endpoints with confidence intervals, develop appropriate statistical models, assess the adequacy of the models and prepare written reports to the investigator. The Biostatistics Core has an important role in cancer research at KCI. During the current grant period (12/1/2010 -- 11/30/2014), the Core's collaborative activities resulted in 121 clinical protocols and 263 new cancer research proposals submitted for peer reviewed, external funding. Of these, 178 were submitted to the NCI, 36 to the DOD, and 4 to the Susan G. Komen Foundation. In addition, Biostatistics Core members were co-authors on 87 publications in peer reviewed journals. Core biostatisticians collaborate with investigators from each of the KCI research programs and work with staff from other KCI cores. The Core maintains a variety of statistical applications including general-purpose statistical software, R, SAS and Stata/MP, as well as software for statistical power calculations including PASS and nQuery Advisor. The Core shares an Ingenuity Pathway Analysis (IPA) license with the Genomics Core. Application software resides on a high-performance, multi-processor Dell 820 server. Access to the server is restricted to members of the Biostatistics Core and authorized guests. For intensive computing projects, members of the Core use the WSU high-performance grid, four servers of which have been contributed to KCI, running under Linux, for the use of Core members. These computers are accessible to other authorized KCI members whose research requires high performance computing.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Center Core Grants (P30)
Project #
5P30CA022453-35
Application #
9221284
Study Section
Subcommittee I - Transistion to Independence (NCI)
Project Start
Project End
Budget Start
2016-12-01
Budget End
2017-11-30
Support Year
35
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Wayne State University
Department
Type
DUNS #
001962224
City
Detroit
State
MI
Country
United States
Zip Code
48202
Campbell, Douglas H; Lund, Maria E; Nocon, Aline L et al. (2018) Detection of glypican-1 (GPC-1) expression in urine cell sediments in prostate cancer. PLoS One 13:e0196017
Sexton, Rachel E; Hachem, Ali H; Assi, Ali A et al. (2018) Metabotropic glutamate receptor-1 regulates inflammation in triple negative breast cancer. Sci Rep 8:16008
Cheriyan, Vino T; Alsaab, Hashem; Sekhar, Sreeja et al. (2018) A CARP-1 functional mimetic compound is synergistic with BRAF-targeting in non-small cell lung cancers. Oncotarget 9:29680-29697
Saadat, Nadia; Liu, Fangchao; Haynes, Brittany et al. (2018) Nano-delivery of RAD6/Translesion Synthesis Inhibitor SMI#9 for Triple-negative Breast Cancer Therapy. Mol Cancer Ther 17:2586-2597
Dedigama-Arachchige, Pavithra M; Acharige, Nuwan P N; Pflum, Mary Kay H (2018) Identification of PP1-Gadd34 substrates involved in the unfolded protein response using K-BIPS, a method for phosphatase substrate identification. Mol Omics 14:121-133
Burl, Rayanne B; Ramseyer, Vanesa D; Rondini, Elizabeth A et al. (2018) Deconstructing Adipogenesis Induced by ?3-Adrenergic Receptor Activation with Single-Cell Expression Profiling. Cell Metab 28:300-309.e4
Desai, Pinkal; Wallace, Robert; Anderson, Matthew L et al. (2018) An analysis of the association between statin use and risk of endometrial and ovarian cancers in the Women's Health Initiative. Gynecol Oncol 148:540-546
Thakur, Manish K; Ruterbusch, Julie J; Schwartz, Ann G et al. (2018) Risk of Second Lung Cancer in Patients with Previously Treated Lung Cancer: Analysis of Surveillance, Epidemiology, and End Results (SEER) Data. J Thorac Oncol 13:46-53
Ma, Huiyan; Ursin, Giske; Xu, Xinxin et al. (2018) Body mass index at age 18 years and recent body mass index in relation to risk of breast cancer overall and ER/PR/HER2-defined subtypes in white women and African-American women: a pooled analysis. Breast Cancer Res 20:5
Mitrea, Cristina; Wijesinghe, Priyanga; Dyson, Greg et al. (2018) Integrating 5hmC and gene expression data to infer regulatory mechanisms. Bioinformatics 34:1441-1447

Showing the most recent 10 out of 826 publications