Related Experiment Videos
Lessons learned from a prospective meta-analysis
S E Margitić1, T M Morgan, M A Sager
1Department of Public Health Sciences, Bowman Gray School of Medicine, Winston-Salem, NC 27157-1063, USA.
Journal of the American Geriatrics Society
|April 1, 1995
Summary
Planning a prospective meta-analysis (PMA) for older hospitalized patients requires careful consideration. This approach offers advantages over individual trials but presents unique challenges in data collection, coordination, and maintaining scientific integrity.
Area of Science:
- Geriatric Medicine
- Clinical Trials Methodology
- Health Services Research
Background:
- Hospitalized older adults are at high risk for functional decline.
- Existing interventions show varied effectiveness, necessitating pooled data analysis.
- The Hospital Outcomes Project for the Elderly (HOPE) aimed to synthesize evidence on interventions to reduce functional decline.
Purpose of the Study:
- To detail the planning and execution of a prospective meta-analysis (PMA) for interventions targeting functional decline in hospitalized elderly.
- To identify and discuss the unique challenges and lessons learned from the HOPE PMA.
Main Methods:
- A prospective meta-analysis (PMA) design was employed, pooling data from concurrently run clinical trials.
- Data were collected and analyzed by a central Data Coordinating Center across six healthcare sites.
- Participants were acutely ill hospitalized individuals aged 65-75 years receiving various geriatric interventions.
Main Results:
- The PMA design facilitated the compilation of data from distinct yet related interventions.
- Coordination of common outcome data collection and analysis was managed by the Data Coordinating Center.
- Interventions included exercise, physical therapy, multidisciplinary geriatric units, post-discharge care, nursing programs, and delirium management.
Conclusions:
- Prospective meta-analyses offer advantages over independent trials and retrospective analyses.
- Key challenges include maintaining scientific integrity, achieving consensus on data collection and quality, defining the coordinating center's role, and establishing data ownership and publication policies.
- Addressing these design and operational challenges proactively is crucial for successful PMA implementation.