Data collection and transcription

Children have historically been underrepresented in randomized controlled trials and multi-center studies. This is particularly true for children who reside in rural and underserved areas. Conducting multi-center trials in rural areas presents unique informatics challenges. These challenges call for increased attention towards informatics infrastructure and the need for development and application of sound informatics approaches to the collection, processing, and management of data for clinical studies. By modifying existing local infrastructure and utilizing open source tools, we have been able to successfully deploy a multi-site data coordinating and operations center. We report our implementation decisions for data collection and management for the IDeA States Pediatric Clinical Trial Network (ISPCTN) based on the functionality needed for the ISPCTN, our synthesis of the extant literature in data collection and management methodology, and Good Clinical Data Management Practices.

The National Institutes of Health requires data sharing plans for projects with over five hundred thousand dollars in direct costs in a single year and has recently released a new guidance on rigor and reproducibility in grant applications. The National Science Foundation outright requires Data Management Plans (DMPs) as part of applications for funding. However, there is no general and definitive list of topics that should be covered in a DMP for a research project. We identified and reviewed DMP requirements from research funders. Forty-three DMP topics were identified. The review uncovered inconsistent requirements for written DMPs as well as high variability in required or suggested DMP topics among funder requirements. DMP requirements were found to emphasize post-publication data sharing rather than upstream activities that impact data quality, provide traceability or support reproducibility. With the emphasis equalized, the forty-three identified topics can aid Data Managers in systematically generating comprehensive DMPs that support research project planning and funding application evaluation as well as data management conduct and post-publication data sharing.

Data entry and its verification are important steps in the process of data management in clinical studies. In Japan, a kind of visual comparison called the reading aloud (RA) method is often used as an alternative to or in addition to the double data entry (DDE) method. In a typical RA method, one operator reads previously keyed data aloud while looking at a printed sheet or computer screen, and another operator compares the voice with the corresponding data recorded on case report forms (CRFs) to confirm whether the data are the same. We compared the efficiency of the RA method with that of the DDE method in the data management system of the Japanese Registry of Renal Transplantation. Efficiency was evaluated in terms of error detection rate and expended time. Five hundred sixty CRFs were randomly allocated to two operators for single data entry. Two types of DDE and RA methods were performed. Single data entry errors were detected in 358 of 104,720 fields (per-field error rate = 0.34%). Error detection rates were 88.3% for the DDE method performed by a different operator, 69.0% for the DDE method performed by the same operator, 59.5% for the RA method performed by a different operator, and 39.9% for the RA method performed by the same operator. The differences in these rates were significant (p<0.001) between the two verification methods as well as between the types of operator (same or different). The total expended times were 74.8 hours for the DDE method and 57.9 hours for the RA method. These results suggest that in detecting errors of single data entry, the RA method is inferior to the DDE method, while its time cost is lower.

From the literature that was initially searched by electronic databases using the keywords quality, quality control and quality assurance in combination with clinical trials, surgery, pathology, radiotherapy, chemotherapy and data management, a comprehensive review is given on what quality assurance means, the various methods used for quality assurance in different aspects of clinical trials and the impact of this quality assurance on outcome and every day practice.

Objective: Multi-site data collection is complex and requires an effective data management system. This article explores data management issues encountered in the design, conduct, and analysis of a research project involving 74 community-based sites and a central data management system. Results: Once the data arrived at the central site, data integrity was maintained at a very high level. Issues encountered in our study on low back pain reflected the practice-based nature of the study and the limitations of finances, staff, and facilities. Conclusion: The task of converting a research protocol to actual procedures for data collection and data management can be very challenging. The importance of early recognition of the effort and resources needed for data management and quality-control procedures cannot be overestimated.

Well-designed data management processes are essential in ensuring the quality of data collected in multicenter clinical trials. This paper describes the data management processes and systems that were developed by the data coordinating center of the Asthma Clinical Research Network. A combination of manual and electronic processes has been designed to process clinical trial data from the point of collection to statistical analysis. A distributed database management system consisting of modular applications for separate data processing activities was developed to enter, track, verify, validate, and edit collected data. In addition, processes for monitoring and reporting data quality are discussed. Control Clin Trials 2001;22:168S–180S