Core--Data management

Georgetown University, Washington DC

Investigators

Linked publications & trials

Abstract

Effective data collection and management are critical to the success of the proposed research projects. The Cancer Genetics Program at the Lombardi Cancer Center has demonstrated success in performing large- scale data management with rigorous quality assurance and privacy procedures. The overall objectives of the Data Management Core (DMC) are to: (a) centralize data collection and management procedures to increase the efficiency and reduce the cost of implementing the proposed research projects; and (b) provide quality assurance for all study procedures and study data. The core leaders will work closely with the TTURC-PI and project Principal Investigators and biostatisticians to achieve the following specific aims: (1) create and maintain a data management system for each study; (2) oversee instrument pre-testing and administration; (3) provide data entry and quality assurance; (4) create and transfer SAS data sets for analysis; (5) ensure privacy and confidentiality of study data; (6) store and archive study data; and (7) prepare research operations reports for review by the Principal Investigators and the Steering Committee. The DMC will be responsible for all aspects of the data management including systems analysis and design programming, data entry, validation and quality assurance. Common measures will be standardized across projects. The DMC will pilot-test measures using mechanisms relevant to the specific instrument. The DMC will conduct centralized, ongoing training of project and core-staff involved in data collection and management. Hard copy data will be entered using form-based Computer Assisted Data Entry (CADE) and telephone interviews will be entered in form-based Computer Assisted Telephone Interviewing (CATI). Responses will be fixed and open text fields will be limited to minimize data entry error. Both prospective (e.g., training, pilot-testing) and retrospective (e.g., checks of data, review of error rates) quality assurance mechanisms will be used. Data management systems for each study will be developed in Access 97 on a secure Windows NT server. To ensure privacy, all data will be maintained using an automatically generated index number. Data encryption will be used when transferring data. Staff training will include confidentiality procedures. Data will be backed up weekly.