The Biometery Core of the Center for Colon Cancer Research (CCCR) provides statistical and mathematical support to target, senior, and junior CCCR investigators. Assistance and support are in the designing of the scientific experiments, studies, and surveys;developing statistical and mathematical models appropriate for the research problems;performing appropriates statistical analyses of data arising from the projects;assessing the validity of the methods used in the analyses of data and developing new methods when appropriates;critiquing and improving statistical portions of manuscripts for publications and grant proposals;writing publication-quality reports pertaining to the results of the statistical analyses for grant applciations and manuscripts for publications;and performing basic research to develop new statistical methodology of relevance to cancer researchers. Biometry Core personnel actively participate in the research of investigators through one-on-one consultations and by providing feedback during research To increase awareness of CCCR investigators on the important and crucial role of Statistics in scientific research, the Biometry Core will provide training workshops on statistical methodology relevant to cancer research. Increasing the statistical knowledge of CCCR investigators will also further enhance collaborative research and improve statistical consultation sessions. Through the statistical support provided by the Biometry Core to CCCR investigators, it is hoped that the validity and utility of the colon cancer research performed by the investigators will be enhanced. This will have the consequence of contributing in masking further inroads to the search for a cure and the prevention of colon cancer. Strategies for financially sustaining the Core post 2017 are to implement a Service-for-Fee arrangement with investigators who seek Core services and to have line item components in the budget of project grants and/or grant proposal for Core personnel.
Comprehensive statistical analysis of biological data is essential for making firm conclusions. The CCCR supports a core that provides investigators with the ability to plan experiments and analyze results in a statistically rigorous fashion.
|Oliver, David; Ji, Hao; Liu, Piaomu et al. (2017) Identification of novel cancer therapeutic targets using a designed and pooled shRNA library screen. Sci Rep 7:43023|
|Farmaki, Elena; Kaza, Vimala; Papavassiliou, Athanasios G et al. (2017) Induction of the MCP chemokine cluster cascade in the periphery by cancer cell-derived Ccl3. Cancer Lett 389:49-58|
|Brown, Jacob L; Rosa-Caldwell, Megan E; Lee, David E et al. (2017) Mitochondrial degeneration precedes the development of muscle atrophy in progression of cancer cachexia in tumour-bearing mice. J Cachexia Sarcopenia Muscle 8:926-938|
|Alexander, M; Burch, J B; Steck, S E et al. (2017) Case-control study of candidate gene methylation and adenomatous polyp formation. Int J Colorectal Dis 32:183-192|
|McDermott, Martina S J; Chumanevich, Alexander A; Lim, Chang-Uk et al. (2017) Inhibition of CDK8 mediator kinase suppresses estrogen dependent transcription and the growth of estrogen receptor positive breast cancer. Oncotarget 8:12558-12575|
|Zhang, Yu; Davis, Celestia; Shah, Sapana et al. (2017) IL-33 promotes growth and liver metastasis of colorectal cancer in mice by remodeling the tumor microenvironment and inducing angiogenesis. Mol Carcinog 56:272-287|
|Chandrashekaran, Varun; Seth, Ratanesh K; Dattaroy, Diptadip et al. (2017) HMGB1-RAGE pathway drives peroxynitrite signaling-induced IBD-like inflammation in murine nonalcoholic fatty liver disease. Redox Biol 13:8-19|
|Farmaki, E; Chatzistamou, I; Kaza, V et al. (2016) A CCL8 gradient drives breast cancer cell dissemination. Oncogene 35:6309-6318|
|Narsale, Aditi A; Puppa, Melissa J; Hardee, Justin P et al. (2016) Short-term pyrrolidine dithiocarbamate administration attenuates cachexia-induced alterations to muscle and liver in ApcMin/+ mice. Oncotarget 7:59482-59502|
|Peña, Edsel A (2016) Asymptotics for a Class of Dynamic Recurrent Event Models. J Nonparametr Stat 28:716-735|
Showing the most recent 10 out of 40 publications