) The purpose of the Biostatistics and Data Operations Core is to enable project investigators to meet their research objectives leading to the understanding of Growth Control of Multiple Myeloma, and ultimately, cure of this malignancy. To these ends, we request support for experienced personnel to build on past progress in developing a highly integrated, multi-disciplinary collaboration among principle investigators, biostatisticians, statistical programmers, and data managers essential to the successful achievement of our aims. The three general components of this core are (1) data collection and management, (2) database development and management, and (3) biostatistical support. The creation of efficiencies via protocol design, standardized toxicity definitions, timing of evaluations and common requisite variable lists across protocols will enable us to refine automation of data management and data collection throughout the progress of clinical protocols. We have developed and will expand the development of flexible, tailored relational databases for specialized sources of patient data while establishing open data base connectivity (ODBC) to institutional data sources already in place. ODBC links among these varied sources of data will provide rapid updating of all pertinent patient data, including automatic updating of SAS databases used in analyses. Biostatistical support provides initial design input for these efforts and conducts appropriate analyses of these efforts, thus, representing the coordinating link among Core components. Based on national guidelines for cooperative clinical trials groups, we justify the need for 2 biostatisticians, 2 statistical programmers, 1 database programmer, 1 data operations/protocol development director, and 7 data managers. Additionally, computer hardware and software is requested to facilitate the biostatistical review of the complex components of this project. Although the Myeloma and Transplant Research Center provides support for a significant portion of this core?s function, additional support through this program project is critical to the successful execution of this project.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA055819-08
Application #
6594586
Study Section
Subcommittee G - Education (NCI)
Project Start
2002-06-01
Project End
2003-05-31
Budget Start
1998-10-01
Budget End
1999-09-30
Support Year
8
Fiscal Year
2002
Total Cost
$279,549
Indirect Cost
Name
University of Arkansas for Medical Sciences
Department
Type
DUNS #
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
Rasche, Leo; Angtuaco, Edgardo; McDonald, James E et al. (2017) Low expression of hexokinase-2 is associated with false-negative FDG-positron emission tomography in multiple myeloma. Blood 130:30-34
Mikulasova, Aneta; Wardell, Christopher P; Murison, Alexander et al. (2017) The spectrum of somatic mutations in monoclonal gammopathy of undetermined significance indicates a less complex genomic landscape than that in multiple myeloma. Haematologica 102:1617-1625
Stein, Caleb K; Pawlyn, Charlotte; Chavan, Shweta et al. (2017) The varied distribution and impact of RAS codon and other key DNA alterations across the translocation cyclin D subgroups in multiple myeloma. Oncotarget 8:27854-27867
Chavan, S S; He, J; Tytarenko, R et al. (2017) Bi-allelic inactivation is more prevalent at relapse in multiple myeloma, identifying RB1 as an independent prognostic marker. Blood Cancer J 7:e535
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

Showing the most recent 10 out of 290 publications