The primary objective of this core, Administration, Data Management, and Biostatistics, is to support the Growth Control of Multiple Myeloma Program Project. All projects and cores of the P01 will require research and administrative coordination, management of patient-related data, and statistical analysis of data. Core A will encompass all of these services and provide them in a coordinated fashion so that all projects and cores contribute to a comprehensive approach, maximizing available resources. Provision of these services is fundamental to effective synergy among the basic, clinical, and translational studies that are the backbone of the Program Project. Core A's specific aims are as follows.
Aim 1. Provide administrative support to all projects and cores, such that research activities are coordinated with appropriate interaction to accomplish goals of the overall P01. Administrative activities will include scheduling and management related to advisory meetings and regular meetings among investigators, coordination with the UAMS Office of Research and Sponsored Programs and Grants Accounting to ensure compliance with NIH guidelines and requirements, coordination with CRAB to ensure that data analysis needs are met, and coordination with the Data Management division of the Myeloma Institute to ensure accurate and timely collection of clinical trial data.
Aim 2. Manage the overall data infrastructure. This includes maintaining the clinical database; electronic transfer of data over secure pathways; tracking samples and verifying that the necessary material is sent to the correct laboratory at the correct time; ensuring security and confidentiality of all data; and coordinating the assimilation of necessary data for analysis, including verifying eligibility, consenting patients to protocols, and ensuring protocol compliance.
Aim 3. Provide biostatistical support to link study design, data collection, measurement, and analysis to the research hypotheses and questions under investigation in the P01. In the early stages, biostatisticians will work with investigators to define study hypotheses and populations and experimental parameters to answer the research questions of interest, reduce systematic bias, and ensure a high likelihood of detection of biologically meaningful effects. At the analysis stage, biostatisticians will identify and implement quantitative methods to address scientific questions of interest and provide valid statistical inferences about evidence supporting the various study hypotheses.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA055819-12
Application #
7278220
Study Section
Subcommittee G - Education (NCI)
Project Start
Project End
Budget Start
2006-07-01
Budget End
2007-06-30
Support Year
12
Fiscal Year
2006
Total Cost
$632,186
Indirect Cost
Name
University of Arkansas for Medical Sciences
Department
Type
DUNS #
122452563
City
Little Rock
State
AR
Country
United States
Zip Code
72205
Rasche, L; Alapat, D; Kumar, M et al. (2018) Combination of flow cytometry and functional imaging for monitoring of residual disease in myeloma. Leukemia :
Went, Molly; Sud, Amit; Försti, Asta et al. (2018) Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma. Nat Commun 9:3707
Mehdi, Syed J; Johnson, Sarah K; Epstein, Joshua et al. (2018) Mesenchymal stem cells gene signature in high-risk myeloma bone marrow linked to suppression of distinct IGFBP2-expressing small adipocytes. Br J Haematol :
Rasche, Leo; Angtuaco, Edgardo J; Alpe, Terri L et al. (2018) The presence of large focal lesions is a strong independent prognostic factor in multiple myeloma. Blood 132:59-66
Went, M; Sud, A; Law, P J et al. (2017) Assessing the effect of obesity-related traits on multiple myeloma using a Mendelian randomisation approach. Blood Cancer J 7:e573
McDonald, James E; Kessler, Marcus M; Gardner, Michael W et al. (2017) Assessment of Total Lesion Glycolysis by 18F FDG PET/CT Significantly Improves Prognostic Value of GEP and ISS in Myeloma. Clin Cancer Res 23:1981-1987
Rasche, Leo; Weinhold, Niels; Morgan, Gareth J et al. (2017) Immunologic approaches for the treatment of multiple myeloma. Cancer Treat Rev 55:190-199
Rasche, L; Chavan, S S; Stephens, O W et al. (2017) Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun 8:268
Jethava, Yogesh S; Mitchell, Alan; Epstein, Joshua et al. (2017) Adverse Metaphase Cytogenetics Can Be Overcome by Adding Bortezomib and Thalidomide to Fractionated Melphalan Transplants. Clin Cancer Res 23:2665-2672
Schinke, Carolina; Hoering, Antje; Wang, Hongwei et al. (2017) The prognostic value of the depth of response in multiple myeloma depends on the time of assessment, risk status and molecular subtype. Haematologica 102:e313-e316

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