ABSTRACT
BACKGROUND: The Gynecologic Oncology Group (GOG) is a multi-institution, multi-discipline Cooperative Group funded by the National Cancer Institute (NCI) to conduct clinical trials which investigate the treatment, prevention, control, quality of survivorship, and translational science of gynecologic malignancies. In 1982, the NCI initiated a program of on-site quality assurance audits of participating institutions. Each is required to be audited at least once every 3 years. In GOG, the audit mandate is the responsibility of the GOG Quality Assurance Audit Committee and it is centralized in the Statistical and Data Center (SDC). Each component (Regulatory, Investigational Drug Pharmacy, Patient Case Review) is classified as Acceptable, Acceptable, follow-up required, or Unacceptable. PURPOSE: To determine frequently occurring deviations and develop focused innovative solutions to address them. METHODS: A database was created to examine the deviations noted at the most recent audit conducted at 57 GOG parent institutions during 2004-2007. Cumulatively, this involved 687 patients and 306 protocols. RESULTS: The results documented commendable performance: Regulatory (39 Acceptable, 17 Acceptable, follow-up, 1 Unacceptable); Pharmacy (41 Acceptable, 3 Acceptable, follow-up, 1 Unacceptable, 12 N/A): Patient Case Review (31 Acceptable, 22 Acceptable, follow-up, 4 Unacceptable). The nature of major and lesser deviations was analyzed to create and enhance initiatives for improvement of the quality of clinical research. As a result, Group-wide proactive initiatives were undertaken, audit training sessions have emphasized recurring issues, and GOG Data Management Subcommittee agendas have provided targeted instruction and training. LIMITATIONS: The analysis was based upon parent institutions only; affiliate institutions and Community Clinical Oncology Program participants were not included, although it is assumed their areas of difficulty are similar. CONCLUSIONS: The coordination of the GOG Quality Assurance Audit program in the SDC has improved data quality by enhancing our ability to identify frequently occurring deviations and develop innovative solutions to avoid or minimize their occurrence in the future.
