The established mission of the Methodology Core for the UAB Multidisciplinary Clinical Research Center (MCRC) is to develop and provide state of the art methodology and methodological education in the collaborative support of clinical and translational research in arthritis and musculoskeletal disease (MSD) at the local, regional, national, and international level. Toward this goal, the Methodology Core will continue to provide the statistical, epidemiological, outcomes research, statistical genetics, economics/cost effectiveness, and bioinformatics leadership and expertise required to develop and perform cutting-edge clinical research in arthritis and MSD as it pursues four broad goals are to: I. Support the design, data collection, management, and analytic efforts of the MCRC projects. II. Nurture original research in methodology applicable to clinical research in arthritis and MSD. III. Develop new investigators in the area of arthritis and MSD research. IV. Provide methodology seminars, workshops, and mini-courses to introduce the newest methodological approaches to the MCRC research base.
of this core is defined by the core's mission to assist in the design, data collection, analysis and general oversight of the proposed projects, to develop new research projects from within the research base, and provide educational opportinities to the members of the research base.
|Stoll, M L; Kumar, R; Lefkowitz, E J et al. (2016) Fecal metabolomics in pediatric spondyloarthritis implicate decreased metabolic diversity and altered tryptophan metabolism as pathogenic factors. Genes Immun 17:400-405|
|Stoll, Matthew L; Cron, Randy Q (2016) The microbiota in pediatric rheumatic disease: epiphenomenon or therapeutic target? Curr Opin Rheumatol 28:537-43|
|Yang, Celeste; Bartolucci, Alfred A; Cui, Xiangqin (2015) Multigroup Equivalence Analysis for High-Dimensional Expression Data. Cancer Inform 14:253-63|
|Curtis, J R; Yang, S; Chen, L et al. (2015) Determining the Minimally Important Difference in the Clinical Disease Activity Index for Improvement and Worsening in Early Rheumatoid Arthritis Patients. Arthritis Care Res (Hoboken) 67:1345-53|
|Li, Peng; Redden, David T (2015) Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes. Stat Med 34:281-96|
|Yan, Qi; Weeks, Daniel E; CeledÃ³n, Juan C et al. (2015) Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method. Genetics 201:1329-39|
|Li, Peng; Redden, David T (2015) Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials. BMC Med Res Methodol 15:38|
|Liu, Nianjun (2015) QTL mapping - Current status and challenges: Comment on "Mapping complex traits as a dynamic system" by L. Sun and R. Wu. Phys Life Rev 13:194-5|
|Cui, Xiangqin; Yu, Shaohua; Tamhane, Ashutosh et al. (2015) Simple regression for correcting Î”Ct bias in RT-qPCR low-density array data normalization. BMC Genomics 16:82|
|Yan, Qi; Weeks, Daniel E; Tiwari, Hemant K et al. (2015) Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples. Hum Hered 80:126-38|
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