Multiple Risk Factor Intervention Trial

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.

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Quantifying Data Quality for Clinical Trials Using Electronic Data Capture

The heritage family study : quality assurance and quality control

Single vs. double data entry in CAST.

The cost and efficiency of training clinical centre staff and of duplicate data entry in clinical trials is reviewed. Training is an essential component of quality assurance programmes and it is usually carried out at regular intervals in long-term clinical trials. Initial training of staff and regular retraining is important to assure standardization and it can lead to reduced trial costs. Interim training for new staff and for remedial purposes is less efficient than regularly scheduled training sessions. Regional centres for training and the use of computer aided instruction are two ways such interim training can be made more efficient and standardized. Duplicate data entry or verification can result in a substantial reduction in data entry errors depending on the nature of the data being keyed. Selective verification should especially be considered for important fields for which consistency checks cannot be performed, that are alphabetic or that are several characters in length. Quality assurance procedures should be implemented to monitor data entry accuracy in clinical trials.

Dale H. Hultgren

Papers

Forms control and error detection procedures used at the Coordinating Center of the Multiple Risk Factor Intervention Trial (MRFIT).

A data dictionary design for clinical trials