Additionally, ePRO systems, in which data are directly entered by the research subject, may be difficult or even impossible to assess for data quality because the information may not be validly and reliably retrieved however, such issues lie outside the scope of our study.
#Inform edc dcri duke manual
Manual SDV can detect abstraction errors, but is labor intensive and highly sensitive to the vagaries of locating the pertinent text or value in medical records, leading to variability and measurement error. Unfortunately, abstraction error rates are not usually quantified in clinical trials. We hypothesized that for EDC to substantially improve quality, it would have to facilitate improvements to the process of medical record abstraction. We sought to explore the effects, if any, of EDC on data quality. Possible detrimental effects of EDC have not been investigated. Web-based EDC can only affect the latter, through structured data collection, valid value lists, and on-screen checks for values that are missing, out of range, or inconsistent. The comparison of published source-to-database and CRF-to-database error rates suggests that most errors occur when data are transferred from source to CRF during medical record abstraction or transcription.
#Inform edc dcri duke trial
Although EDC proponents frequently claim that clinical trial data quality improves with use of such systems, studies supporting this contention have yet to appear in the peer-reviewed literature, and it is not clear whether traditional methods of ascertaining data quality suffice for EDC trials.Įxploring the effects of EDC on data quality With EDC, there is no paper CRF to compare to the source, leading to differences in data collection processes and resulting data quality. In contrast, the average error rate for published CRF-to-database comparison audits was 14 errors per 10,000 fields –. Although the SDV process is not usually quantified during trial operations, our literature review identified 42 articles that provided source-to-database error rates, primarily from registries – the average error rate across these publications was 976 errors per 10,000 fields. SDV compares original data, such as the medical record, with the study CRF.
#Inform edc dcri duke verification
In compliance with Good Clinical Practices (GCP), trial sponsors typically perform source document verification (SDV) of recorded data. Determining actual data quality requires an assessment of all possible sources of error, including data measurement, recording, abstraction, transcription, entry, coding, or cleaning. Thus, the commonly reported “database error rate” is merely an estimate of errors introduced during data entry and cleaning at best equal to, but likely less than, the total “true” error rate. Other errors, including measurement error, recording error, or transcription mistakes that occur when transferring data from source documents to CRFs lie outside the scope of traditional CRF-to-database audits. Such audits do not assess the percentage of correct data rather, they identify additional errors introduced during data processing.
The average error rate in the published literature for CRF-to-database audits is 14 errors per 10,000 fields. There are numerous examples, both published – and unpublished, of database audits that compare database listings to CRFs. In addition to providing objective information about processes, audits can prevent future errors by identifying problematic work patterns or behaviors. Audits may also indicate location and distribution of errors, which are usually categorized in a manner meaningful to the study (e.g., critical versus noncritical) or the organization (e.g., systematic versus random errors, or according to root causes). Relatively few new data collection systems or methodologies, with the exception of electronic patient reported outcomes (ePRO) –, are well-characterized with respect to data quality.ĭata quality for paper-based clinical trials is traditionally assessed through audits that compare database listings against data recorded on paper case report forms (CRFs), thereby providing an estimate of the database error rate. If novel technologies are to be successfully integrated into clinical trials, their effects on data quality must be fully understood. Research sponsors and clinical research organizations (CROs) are transitioning from paper-based data collection to electronic data capture (EDC) systems.
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