The Division of Biostatistics in the School of Public Health at the University of Minnesota (John Connett, PI), in collaboration with the Divisions of Epidemiology and Pulmonary Medicine, proposes to establish and operate the Data Coordinating Center (DCC) for the Chronic Obstructive Pulmonary Disease Clinical Research Network. The goals of the Network are to identify preventive and therapeutic interventions to reduce mortality, exacerbations, and disability in patients with moderate-to-severe COPD. Clinical trials undertaken by the Network must have relevance to clinical practice for the treatment of this common and serious chronic disease, and must provide efficient answers to questions regarding treatment alternatives. As described in RFA HL-03-002, four-to-six clinical centers and the DCC will launch and complete 4-5 clinical trials in a 5-year period, with 2-3 protocols in operation simultaneously. As the DCC for the Network we will perform the following key functions: 1) establish the Network's organizational structure and facilitate internal communications; 2) provide statistical input on study design; 3) develop and maintain Manuals of Procedures; 4) establish a distributed data entry/data management system; 5) train and certify clinical center personnel; 6) create subcontracts with central laboratories and reading centers, as needed; 7) generate randomization schedules and reports to monitor data quality, recruitment progress, retention, outcomes, and adverse events; 8) carry out data analyses for the investigators and contribute to manuscripts and scientific presentations. Our group brings to this project over 16 years of experience in the design, conduct, and analysis of multicenter clinical trials of COPD and emphysema, including the Lung Health Studies I, II, and the Feasibility of Retinoid Therapy for Emphysema (FORTE) study. We have assembled a solid and productive team of investigators and professional staff with relevant expertise in biostatistics, clinical trials, epidemiology, and pulmonary medicine, and ample experience in data management, data quality control, and statistical analysis.
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