As one of the three cores proposed for the renewal of the Center for Research in Chronic Disorders (CRCD), the Data Management and Analysis Core (DMAC) will serve as a data-processing center, providing centralized services to core, pilot, and affiliate investigators (on- and off-site) to foster research in the management of treatment regimens (i.e., adherence) in chronic disorders targeting the clinical outcome areas of functional status, cognitive function, quality of life, and disorder-specific outcomes. Specifically, this core will support data entry, management and analysis and consultation for research on adherence by core researchers and the studies proposed by pilot researchers as well as the collaborative endeavors within the CRCD, including secondary analyses, pooled data analyses and meta-analysis and hypothesis generation via data mining. In addition to traditional quantitative approaches, the use of qualitative methods and analysis and triangulation will be supported. Functioning as a data-processing center, the DMAC will offer consultations on methodology, measurement adaptation and evaluation, form design, data management and analysis, as well as direct support for form design, data management, and analysis to research investigators of CRCD initiatives. Through the centralized and integrated resources of this core, the CRCD will achieve an economy of scale in the areas of form design, data management and analysis. The congruity of data management methods employed will diminish the loss of efficiency due to turnover. Project initiation and training time will be lessened and data quality will be enhanced. Data will be stored in repositories on a secured database server accessible to investigators via a wide area network. Training of researchers and staff on form design, screening, management and analysis, and study monitoring will be conducted to aid research development and conduct. Dissemination will be promoted by collaboration on presentations and publications. The DMAC will collaborate on the evaluation and modification of alternate methods of measurement in chronic disorder populations. Pooled data analyses will be performed to study the impact of chronic disorders and their interplay with patient characteristics on adherence and outcomes. The DMAC will conduct crosssectional/ longitudinal analyses of potentially modifiable patient characteristics to discern their roles as possible mediators or moderators. The use of data mining for knowledge discovery in archived data from chronic disorders populations will be expanded. Methodologic work on measurement and analysis of adherence will be fostered.
Showing the most recent 10 out of 59 publications
