Quantifying data quality for clinical trials using electronic data capture.
Abstract
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.
Type
Journal articleSubject
Automatic Data ProcessingClinical Audit
Clinical Trials as Topic
Commission on Professional and Hospital Activities
Humans
National Institute on Drug Abuse (U.S.)
National Institutes of Health (U.S.)
Organizational Case Studies
Research Design
United States
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https://hdl.handle.net/10161/4503Published Version (Please cite this version)
10.1371/journal.pone.0003049Publication Info
Nahm, 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.
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Show full item recordScholars@Duke
Meredith Nahm Zozus
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.
Carl F. Pieper
Professor of Biostatistics & 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
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