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.
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.
|Chen, Ling; Sun, Jianguo; Xiong, Chengjie (2016) A multiple imputation approach to the analysis of clustered interval-censored failure time data with the additive hazards model. Comput Stat Data Anal 103:242-249|
|Staley, Lyndsay A; Ebbert, Mark T W; Parker, Sheradyn et al. (2016) Genome-wide association study of prolactin levels in blood plasma and cerebrospinal fluid. BMC Genomics 17 Suppl 3:436|
|Staley, Lyndsay A; Ebbert, Mark T W; Bunker, Daniel et al. (2016) Variants in ACPP are associated with cerebrospinal fluid Prostatic Acid Phosphatase levels. BMC Genomics 17 Suppl 3:439|
|Ingber, Adam P; Hassenstab, Jason; Fagan, Anne M et al. (2016) Cerebrospinal Fluid Biomarkers and Reserve Variables as Predictors of Future ""Non-Cognitive"" Outcomes of Alzheimer's Disease. J Alzheimers Dis 52:1055-64|
|Gordon, Brian A; Blazey, Tyler; Su, Yi et al. (2016) Longitudinal Î²-Amyloid Deposition and Hippocampal Volume in Preclinical Alzheimer Disease and Suspected Non-Alzheimer Disease Pathophysiology. JAMA Neurol 73:1192-1200|
|Babulal, Ganesh M; Ghoshal, Nupur; Head, Denise et al. (2016) Mood Changes in Cognitively Normal Older Adults are Linked to Alzheimer Disease Biomarker Levels. Am J Geriatr Psychiatry 24:1095-1104|
|Jack Jr, Clifford R; Knopman, David S; ChÃ©telat, GaÃ«l et al. (2016) Suspected non-Alzheimer disease pathophysiology--concept and controversy. Nat Rev Neurol 12:117-24|
|Ebbert, Mark T W; Staley, Lyndsay A; Parker, Joshua et al. (2016) Variants in CCL16 are associated with blood plasma and cerebrospinal fluid CCL16 protein levels. BMC Genomics 17 Suppl 3:437|
|Lucey, Brendan P; Mcleland, Jennifer S; Toedebusch, Cristina D et al. (2016) Comparison of a single-channel EEG sleep study to polysomnography. J Sleep Res 25:625-635|
|Cummings, Jeffrey; Aisen, Paul S; DuBois, Bruno et al. (2016) Drug development in Alzheimer's disease: the path to 2025. Alzheimers Res Ther 8:39|
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