Quantifying data quality for clinical trials using electronic data capture.
Repository Usage Stats
BACKGROUND: Historically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes. METHODS AND PRINCIPAL FINDINGS: The National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC trials. We characterize the average source-to-database error rate (14.3 errors per 10,000 fields) for the first year of use of the new evaluation method. This error rate was significantly lower than the average of published error rates for source-to-database audits, and was similar to CRF-to-database error rates reported in the published literature. We attribute this largely to an absence of medical record abstraction on the trials we examined, and to an outpatient setting characterized by less acute patient conditions. CONCLUSIONS: Historically, medical record abstraction is the most significant source of error by an order of magnitude, and should be measured and managed during the course of clinical trials. Source-to-database error rates are highly dependent on the amount of structured data collection in the clinical setting and on the complexity of the medical record, dependencies that should be considered when developing data quality benchmarks.
SubjectAutomatic Data Processing
Clinical Trials as Topic
Commission on Professional and Hospital Activities
National Institute on Drug Abuse (U.S.)
National Institutes of Health (U.S.)
Organizational Case Studies
Published Version (Please cite this version)10.1371/journal.pone.0003049
Publication InfoNahm, Meredith L; Pieper, Carl F; & Cunningham, Maureen M (2008). Quantifying data quality for clinical trials using electronic data capture. PLoS One, 3(8). pp. e3049. 10.1371/journal.pone.0003049. Retrieved from https://hdl.handle.net/10161/4503.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
More InfoShow full item record
Assistant Professor of Biostatistics & Bioinformatics
This author no longer has a Scholars@Duke profile, so the information shown here reflects their Duke status at the time this item was deposited.
Associate Professor of Biostatistics and Bioinformatics
Analytic Interests. 1) Issues in the Design of Medical Experiments: I explore the use of reliability/generalizability models in experimental design. In addition to incorporation of reliability, I study powering longitudinal trials with multiple outcomes and substantial missing data using Mixed models. 2) Issues in the Analysis of Repeated Measures Designs & Longitudinal Data: Use of Hierarchical Linear Models (HLM) or Mixed Models in modeling trajectories of multipl
Alphabetical list of authors with Scholars@Duke profiles.