Approaches to data management☆

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.

The clinical data management system is widely used to manage the data that is collected during the clinical trials. This system offers various comfortable methods via which the data can be collected, managed and stored easily for further use. The points regarding the clinical data management that are covered in this review article are the basic introduction covering the general aspects of the clinical data management system and various clinical data management softwares plus the use and the importance of case report forms in CDM and the data management procedures that are being followed up for the proper management of the data so that it is easily accessible by the personnel. Also, the use of various softwares in the clinical data management process has been discussed depicting how the softwares perform various functions to keep the data in a managed, secured and an accessible form. As an endpoint, various future challenges and options are considered which give a detailed idea about the growth of clinical data management in the Pharmaceutical Industry.

Multisite randomized clinical trials allow for increased research collaboration among investigators and expedite data collection efforts. As a result, government funding agencies typically look favorably upon this approach. As the field of complementary and alternative medicine (CAM) continues to evolve, so do increased calls for the use of more rigorous study design and trial methodologies, which can present challenges for investigators.

To describe the processes involved in the coordination and management of a multisite randomized clinical trial of a CAM intervention.

Key aspects related to the coordination and management of a multisite CAM randomized clinical trial are presented, including organizational and site selection considerations, recruitment concerns and issues related to data collection and randomization to treatment groups. Management and monitoring of data, as well as quality assurance procedures are described. Finally, a real world perspective is shared from a recently conducted multisite randomized clinical trial of reflexology for women diagnosed with advanced breast cancer.

The use of multiple sites in the conduct of CAM-based randomized clinical trials can provide an efficient, collaborative and robust approach to study coordination and data collection that maximizes efficiency and ensures the quality of results.

Multisite randomized clinical trial designs can offer the field of CAM research a more standardized and efficient approach to examine the effectiveness of novel therapies and treatments. Special attention must be given to intervention fidelity, consistent data collection and ensuring data quality. Assessment and reporting of quantitative indicators of data quality should be required.

Data management system development for the first Asthma Clinical Research Network (ACRN) study began at the data coordinating center (DCC) in May 1995 with the requirement for delivery of a production system by November 1995. Special methods had to be used to establish an internet local area network (LAN), place clinical client systems, and achieve an accelerated software development cycle. The development of a fully integrated data management system prior to the start of the study was not possible. Therefore an early analysis focused on identifying discrete groupings of data management functions that would allow development of distinct database modules to provide specific functionality such as subject randomization, subject registration, and data entry. The modules were categorized as either being associated with clinical centers or the DCC so that the clinical center modules could be developed and delivered to meet the start date of the study. In the second phase of development during the relatively slow patient-enrollment period, the DCC functional modules were delivered discretely over time. While at the time this development model was a necessity due to limited DCC resources, it continues to be used today as it permits the DCC to implement studies more rapidly and efficiently for the ACRN. This paper describes the methodologies used to develop an internet-based LAN, establish clinical center client systems, establish DCC client and server operations, and develop a data management system. It describes the circumstances that contributed to the development of these systems and the special methodologies developed. The technical aspects of the data management system and LAN are presented as well as a description of the requirements and constraints analysis used to develop the hardware and software systems. Control Clin Trials 2001; 22:135S–155S

The Coordinating Centre (CC) of the Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico (GISSI) used telecommunication technology to develop a computerized network system for the data management of the GISSI studies. Through a personal computer (PC), a communication program, a modem and a telephone line, the investigator in each participating centre can connect with a micro-computer at the CC, to recruit/randomize patients and download reports on the progress of the trial. In the first case, the investigator is required to answer a set of predefined questions, and thereby the system automatically checks eligibility criteria and randomly assigns the patient to a treatment arm. In the second case, once the investigator has made a choice from a list of standard reports and the relative query on CC central database, the generation, the formatting and the transfer of the selected report to the PC are executed automatically on line. The main advantages of this system are a reduction in number of mistakes in data completion and in the human and economic resources required, as well as the real time updating of participating centres. The system was successfully adopted in the GISSI-3 trial (200 Coronary Care Units and 19 394 patients enrolled), in the European arm of the CORE trial and it is currently being used in the GISSI-Prevenzione trial.