The Statistical/Data Management Core (SDMC) serves as the analytic ?hub? of the EAS, and provides integration of the wide range of data collected by Cores, Projects, and pilot studies. It serves two functions that are essential for the success of the Einstein Aging Study (EAS). First, the SDMC maximizes data quality by implementing database systems that integrate and manage the data collected from the Administrative, Clinical, Neuropathology and Neuroimaging Cores, and from each of the three projects. In addition, it is responsible for secure data transfer across study sites and to/from participant ecological momentary assessment devices. The SDMC assumes responsibility for quality control procedures and for merging data across Projects and Cores. Second, the Statistical Core provides collaborative and consultative support to Project investigators on matters of study design, data analyses and interpretation of results. The Statistical Core is responsible for developing, implementing and interpreting statistical methods appropriate to specific research questions and hypotheses, and it collaborates regularly with Project investigators on scientific manuscripts.
Specific Aims of the Statistical/Data Management Core are:
Aim 1. To implement and oversee data procedures to facilitate the seamless exchange of data and ideas among Cores and Projects, and to facilitate data transfer for collaborations with investigators outside the EAS.
Aim 2. To provide a general analytic framework for hypothesis testing, model building, and integration of results and analyses across measurement constructs (e.g., exposures, mechanisms, outcomes), and to collaborate with investigators regarding the framing and testing of hypotheses and to provide expertise in the design and conduct of analyses.
Aim 3. To develop new statistical methodology and to apply existing methodology in innovative ways to help to fulfill the other aims of this Core and the Projects and to further aging research in general, with the emphasis of methods to combine ambulatory and traditional markers to identify early cognitive impairment and disease.
The Statistical/Data Management Core?s contribution to the Program Project is essential and significant because it is needed (1) To ensure a high quality of data; (2) To ensure proper analysis of data for hypothesis testing and hypothesis generation; and (3) To ensure that the data collected in each project and core are optimally utilized for both project specific and cross-project analyses. These processes are essential to insure proper dissementation of the EAS resources to the scientific and greater communities.
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| Hyun, Jinshil; Sliwinski, Martin J; Almeida, David M et al. (2018) The moderating effects of aging and cognitive abilities on the association between work stress and negative affect. Aging Ment Health 22:611-618 |
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| Sliwinski, Martin J; Mogle, Jacqueline A; Hyun, Jinshil et al. (2018) Reliability and Validity of Ambulatory Cognitive Assessments. Assessment 25:14-30 |
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| Hyun, Jinshil; Sliwinski, Martin J; Smyth, Joshua M (2018) Waking Up on the Wrong Side of the Bed: The Effects of Stress Anticipation on Working Memory in Daily Life. J Gerontol B Psychol Sci Soc Sci : |
| Li, Zeran; Del-Aguila, Jorge L; Dube, Umber et al. (2018) Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure. Genome Med 10:43 |
| Majd, Marzieh; Graham-Engeland, Jennifer E; Smyth, Joshua M et al. (2018) Distinct inflammatory response patterns are evident among men and women with higher depressive symptoms. Physiol Behav 184:108-115 |
| Bond, Dale S; Thomas, J Graham; Lipton, Richard B et al. (2018) Behavioral Weight Loss Intervention for Migraine: A Randomized Controlled Trial. Obesity (Silver Spring) 26:81-87 |
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