부산대학교 도서관

High data quality is a precondition for valid scientific conclusions. Indicators should therefore routinely be used to evaluate data quality within the life cycle of health studies. In this project, 15 representatives of seven German population-based cohort studies assessed 51 quality indicators that were proposed in a guideline for networked medical research. The applicability of the indicators to primary data collections was assessed. In addition, their importance was evaluated using a scale ranging from 1 (essential) to 4 (not important). Moreover, their implementation in data quality assessments in the participating studies was evaluated. Comments on potential improvements could be made. Forty-three indicators were rated as applicable. Of these, 29 received a mean importance score of 2 (important) or better, nine received a mean importance score of 1.5 or better. The latter represent a potential core set of data quality indicators for cohort studies. Most indicators that were rated as highly important were used in data quality assessments of the participating studies. Points of criticism regarding the guideline related to its structure and the understandability of some indicators. It was concluded that further improvement of the data quality indicator set will increase its usefulness and applicability in primary data collections. In practice, a small subset of data quality indicators may suffice to capture the most important aspects of data quality in cohort studies.