The purpose of the Statistical and Data Management Core (SDMC) is to provide the technical support necessary to organize and analyze data generated by the other Cores. The SDMC is responsible for all data entry and management of the data from the other Cores in a comprehensive central database and for providing advice in investigators on statistical considerations. Statistical consulting, programming, reporting and analysis are provided for all ADC investigators.
The Specific aims are: (1) to enhance the functionality of the existing database and make it available over a central PC network; (2) to incorporate a patient scheduling and tracking system into the centralized database; and (3) to provide ongoing support. The first and most important aim of the SDMC is that of enhancing the existing central data management system and making it available over a central PC network which connects all ADC investigators. This networked system will be accurate, consistent, and available for retrieval and analysis at any time. Hardware, software and services required to support this network need to be purchased; they include a 486 computer used for the file server; 13 Depca boards and their installation; and related networking software. Considerable systems design and database programming support are required. This programming effort will involve modifying existing dBase compatible applications to handle security and multi-user interactions, queries and downloading to PC's. This networked database will be enhanced by moving the data dictionary on-line. Data for each patient will be displayed in grid format; additional data elements (e.g. clinical, peri-mortem, and imaging) will be incorporated; and clinical laboratory data will be acquired with a computer interface rather than by reentering from hard copy.
The second aim i s to provide a patient scheduling and tracking system and to make it available on the network so that any authorized investigator can review a summary of the patient's past and planned visits and schedule new visits. It will be integrated with the data entry system.
The third aim i s continued statistical support for planning, analyzing, reporting, and interpreting data along with tabular and graphical display of results. Mr. Risser will support Clinical Core investigators (50% effort) and Dr. Tom Carmody will support investigators from other Cores and new science projects (30% for year 1, 20% for subsequent years).

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Center Core Grants (P30)
Project #
3P30AG012300-08S1
Application #
6484087
Study Section
Project Start
2001-08-15
Project End
2002-03-31
Budget Start
Budget End
Support Year
8
Fiscal Year
2001
Total Cost
Indirect Cost
City
Dallas
State
TX
Country
United States
Zip Code
75390
Hanfelt, John J; Peng, Limin; Goldstein, Felicia C et al. (2018) Latent classes of mild cognitive impairment are associated with clinical outcomes and neuropathology: Analysis of data from the National Alzheimer's Coordinating Center. Neurobiol Dis 117:62-71
Zhou, Zilu; Wang, Weixin; Wang, Li-San et al. (2018) Integrative DNA copy number detection and genotyping from sequencing and array-based platforms. Bioinformatics 34:2349-2355
Stallings, Nancy R; O'Neal, Melissa A; Hu, Jie et al. (2018) Pin1 mediates A?42-induced dendritic spine loss. Sci Signal 11:
Burke, Shanna L; Hu, Tianyan; Fava, Nicole M et al. (2018) Sex differences in the development of mild cognitive impairment and probable Alzheimer's disease as predicted by hippocampal volume or white matter hyperintensities. J Women Aging :1-25
Ding, Kan; Tarumi, Takashi; Zhu, David C et al. (2018) Cardiorespiratory Fitness and White Matter Neuronal Fiber Integrity in Mild Cognitive Impairment. J Alzheimers Dis 61:729-739
Wang, Qi; Guo, Lei; Thompson, Paul M et al. (2018) The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. J Alzheimers Dis 64:149-169
Wang, Tingyan; Qiu, Robin G; Yu, Ming (2018) Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks. Sci Rep 8:9161
Agogo, George O; Ramsey, Christine M; Gnjidic, Danijela et al. (2018) Longitudinal associations between different dementia diagnoses and medication use jointly accounting for dropout. Int Psychogeriatr 30:1477-1487
LoBue, Christian; Woon, Fu L; Rossetti, Heidi C et al. (2018) Traumatic brain injury history and progression from mild cognitive impairment to Alzheimer disease. Neuropsychology 32:401-409
Alosco, Michael L; Sugarman, Michael A; Besser, Lilah M et al. (2018) A Clinicopathological Investigation of White Matter Hyperintensities and Alzheimer's Disease Neuropathology. J Alzheimers Dis 63:1347-1360

Showing the most recent 10 out of 385 publications