The Data Management and Biostatistics Core (DMBC) will serve as a resource and collaborator for all projects and cores related to this program project. Specifically the DMBC will: (1) consult on the design of all projects and in the application of appropriate statistical and methodological techniques;(2) lead and collaborate in data analysis and report preparation for all cores and projects, especially in the analysis of associations among longitudinal growth/decline patterns of all disease markers across the individual projects;(3) coordinate and implement participant scheduling program across all projects and cores;(4) continue our collaboration with the WU Center for Biomedical Informatics (CBMI) to complete the transition to our bioinformatics platforms, make data collected by ACS cores and projects available to all ACS investigators, and insure the quality control of all analysis data sets for publications;(5) collaborate in the design of all forms to be used;(6) develop, apply, and implement statistical data analysis techniques appropriate for addressing the scientific aims of the program project.

Public Health Relevance

The Data Management and Biostatistics Core (DMBC) provides design, analyses, and data management resources to support all the ACS projects and cores. The relevance of the DMBC is that ACS addresses crucial public health questions to identify the earliest possible biomarker changes for Alzheimer's disease and dementia so that prevention and/or treatment can be started early.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Program Projects (P01)
Project #
5P01AG026276-09
Application #
8732592
Study Section
Special Emphasis Panel (ZAG1-ZIJ-4)
Project Start
Project End
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
9
Fiscal Year
2014
Total Cost
$132,069
Indirect Cost
$45,181
Name
Washington University
Department
Type
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Roe, Catherine M; Barco, Peggy P; Head, Denise M et al. (2017) Amyloid Imaging, Cerebrospinal Fluid Biomarkers Predict Driving Performance Among Cognitively Normal Individuals. Alzheimer Dis Assoc Disord 31:69-72
Lewczuk, Piotr; Matzen, Anja; Blennow, Kaj et al. (2017) Cerebrospinal Fluid A?42/40 Corresponds Better than A?42 to Amyloid PET in Alzheimer's Disease. J Alzheimers Dis 55:813-822
Millar, Peter R; Balota, David A; Maddox, Geoffrey B et al. (2017) Process Dissociation Analyses of Memory Changes in Healthy Aging, Preclinical, and Very Mild Alzheimer Disease: Evidence for Isolated Recollection Deficits. Neuropsychology :
Schindler, Suzanne E; Jasielec, Mateusz S; Weng, Hua et al. (2017) Neuropsychological measures that detect early impairment and decline in preclinical Alzheimer disease. Neurobiol Aging 56:25-32
Zhao, Yue; Raichle, Marcus E; Wen, Jie et al. (2017) In vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imaging. Neuroimage 148:296-304
Su, Yi; Vlassenko, Andrei G; Couture, Lars E et al. (2017) Quantitative hemodynamic PET imaging using image-derived arterial input function and a PET/MR hybrid scanner. J Cereb Blood Flow Metab 37:1435-1446
Deming, Yuetiva; Li, Zeran; Kapoor, Manav et al. (2017) Genome-wide association study identifies four novel loci associated with Alzheimer's endophenotypes and disease modifiers. Acta Neuropathol 133:839-856
Head, Denise; Allison, Samantha; Lucena, Nathaniel et al. (2017) Latent structure of cognitive performance in the adult children study. J Clin Exp Neuropsychol 39:621-635
Day, Gregory S; Lim, Tae Sung; Hassenstab, Jason et al. (2017) Differentiating cognitive impairment due to corticobasal degeneration and Alzheimer disease. Neurology 88:1273-1281
Monsell, Sarah E; Mock, Charles; Fardo, David W et al. (2017) Genetic Comparison of Symptomatic and Asymptomatic Persons With Alzheimer Disease Neuropathology. Alzheimer Dis Assoc Disord 31:232-238

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