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| Data Management and Analysis Core |
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 Data Management and Analysis Core (DMAC) |
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The Data Management and Analysis core is led by Drs. Subashan Perera, PhD and Doris Rubio, PhD.
As one of the five shared resource cores proposed for an OAIC at the University of Pittsburgh, the Data Management and Analysis Core (DMAC) will provide centralized services to a cadre of investigators conducting translational intervention and mechanistic research in older adults with balance disorders. Functioning as a data-processing center, the DMAC will offer expert consultations on research methodology, measurement adaptation and evaluation, form design, data management and analysis to investigators preparing grant applications for funding. Direct support for form design, data entry and management, and analysis will be provided to research investigators through the funding from pilot studies and externally funded projects.
Through the centralized and integrated resources of this Core, the OAIC 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 likely loss of efficiency due to staff turnover. Project initiation and training time will be lessened and data quality will be enhanced. Data will be archived in repositories on secured database servers accessible to investigators via a local area network. Through these repositories, secondary analyses, pooled data analyses, meta-analyses, and hypothesis generation via data mining may readily occur. Both quantitative and qualitative methods and analysis as well as methodologic triangulation will be supported, allowing for a fuller understanding of balance disorders and their contributors. Training of researchers and their staff on form design, screening, management and analysis, and study monitoring will be conducted to aid research development and conduct. Members of the DMAC will participate in training activities for OAIC trainees and investigators including seminars and workshops and serve as mentors to trainees. Research dissemination will be promoted via collaboration with DMAC members on publications and presentations. Methodologic work on issues unique to the management and analysis of data collected to address balance disorders in the aged will also be fostered.
Cycle 3 Development Projects Awarded:- Clinically Meaningful Change in Physical Performance Measures of Mobility
Project Leader: Subashan Perera, PhD
The predictive value of physical performance measures in the elderly in identifying those at risk of future adverse outcomes is well-documented. Emerging literature indicates that changes over time of these measures are also independently informative. If these measures are to be used in clinical practice, magnitudes corresponding to a minimally clinically important within-individual change (MCID) in different subpopulations need to be determined. Our preliminary work has shown that a within-individual change of about 0.05 meters/second in gait speed and 20 meters in 6-minute walk distance (6MWD) may be considered minimally clinically important, while a change of about 0.10 meters/second in gait speed, one point in Short Physical Performance Battery (SPPB) and 50 meters in 6MWD may be considered substantial.
Specific Aims: - Evaluate effect of specific anchors and time period on MCIDs by comparing estimates of MCID to prior estimates obtained using other smaller studies
- Evaluate the effect of baseline performance on the estimates of MCID
- Assess the predictive validity of MCID criteria using outcome status at year 6, accounting for baseline performance.
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