This paper is prepared to discuss the common issues in data management, such as building and training of data management team, standard operation procedure, document management, execution and communication, strategies to correct and prevent mistakes, and measures to improve the quality and efficiency of clinical trials and data management.
Clinical Trials as Topic ; Data Collection ; standards ; Information Storage and Retrieval ; standards

Quality and integrity of clinical trials and associated data management is a basis on the scientific and rightly assessments of drug safety and efficacy. While both normalization and standardization of clinical trial procedures assure quality of clinical trials and the relevant data processes, they will drive and improve the efficiency and reliability of real-world deliverables in clinical trials in turn. Currently, the comprehensive standards and practices of clinical trials and associated data management are globally established better, and US and EMA have enacted and implemented adequate guidances and regulations well. China is in the initial stage of development of relevant regulations regarding clinical trials and associated data management. This review will focus on the above-mentioned global regulations and standards of clinical data management in the views of good clinical data management standpoints, making references to improve the Chinese regulative system of clinical data management.
China ; Clinical Trials as Topic ; standards ; Data Collection ; standards ; Reproducibility of Results

This list of clincal data management documentation is to ensure standardized and adequate archival of trial documents and records in clinical data management, which is applicable to all of phase I-IV clinical trials.
Clinical Trials as Topic ; Data Collection ; standards ; Documentation ; standards

Case report form (CRF) is a key document for data collection in clinical trials. A well-designed CRF is required for database construction, data accuracy, data query/cleaning, CRF completion and statistical analysis. A well-defined process or SOP should be in place for CRF design. Data collection should fully meet the demand of study protocol. The layout of CRF should be clear with well-structured fields and standard coding for fields.
Clinical Trials as Topic ; standards ; Data Collection ; standards ; Documentation ; standards ; Research Design ; standards

Content validity is the degree to which an instrument has an appropriate sample of items for the construct being measured and is an important procedure in scale development. Content validity index (CVI) is the most widely used index in quantitative evaluation. There are 2 kinds of CVI: I-CVI and S-CVI. A method to compute a modified kappa statistic (K*) can be used to adjust I-CVI for chance agreement. S-CVI/UA and S-CVI/Ave are both scale level CVI with different formulas. Researchers recommend that a scale with excellent content validity should be composed of I-CVIs of 0.78 or higher and S-CVI/UA and S-CVI/Ave of 0.8 and 0.9 or higher, respectively. The characteristics and qualifications of the experts, process and main results of content validity evaluation should be reported in scale-related manuscript.
Data Collection ; Humans ; Psychometrics ; methods ; Reproducibility of Results ; Surveys and Questionnaires ; standards

There is no a systemic performance metrics for clinical data management. While the CDMC in China starts to develop the quality metrics for clinical data management, it is essential to think over the performance and pursue metrics implementation of clinical data management in China. This article provides the basic concept, development and implementation of the performance metric in clinical data management.
China ; Clinical Trials as Topic ; standards ; Data Collection ; standards ; Information Storage and Retrieval ; standards

CDISC standard has become a set of global data standards that can be used in clinical study, covering the full life cycle of clinical researches. After nearly 20 years of development and continuous version upgrades, CDISC standard can improve the quality and efficiency of clinical research and drug review, and to facilitate all stakeholders involved in researches to exchange the study data and communicate the outcomes. CDISC standard has been or is to be adopted as standard format in data submission by multiple regulatory authorities, and more widely implemented by the global pharmaceutical community. CDISC standard is gradually adopted in China. The feasibility and roadmap of CDISC standard as the Chinese data submission format requirements are undergoing exploration and piloting further.
Biomedical Research ; standards ; China ; Clinical Trials as Topic ; standards ; Data Collection ; standards

Although the importance of clinical data is drawing more attention in drug development in China, the clinical data management is not good enough in the clinical trials right now. With the development of internet and progress of information technology, especially with the setup of the state innovation strategy for drug development, it is necessary and urgent to improve the clinical data quality. Good data quality is the primary basis of technical evaluation of drug at the marketing authorization. So Center for Drug Evaluation of CFDA has made some endeavors to enhance data management in the clinical trials in recent years. This article is focused on these aspects of data managment.
China ; Data Collection ; standards ; Drug Evaluation ; standards ; Information Storage and Retrieval ; standards ; Internet

Establishment of quality management system (QMS) plays a critical role in the clinical data management (CDM). The objectives of CDM are to ensure the quality and integrity of the trial data. Thus, every stage or element that may impact the quality outcomes of clinical studies should be in the controlled manner, which is referred to the full life cycle of CDM associated with the data collection, handling and statistical analysis of trial data. Based on the QMS, this paper provides consensus on how to develop a compliant clinical data management plan (CDMP). According to the essential requirements of the CDM, the CDMP should encompass each process of data collection, data capture and cleaning, medical coding, data verification and reconciliation, database monitoring and management, external data transmission and integration, data documentation and data quality assurance and so on. Creating and following up data management plan in each designed data management steps, dynamically record systems used, actions taken, parties involved will build and confirm regulated data management processes, standard operational procedures and effective quality metrics in all data management activities. CDMP is one of most important data management documents that is the solid foundation for clinical data quality.
Clinical Trials as Topic ; Data Collection ; standards ; Database Management Systems ; standards ; Information Storage and Retrieval ; standards

Source data and its source documents are the foundation of clinical research. Proper source data management plays an essential role for compliance with regulatory and GCP requirements. Both paper and electronic source data co-exist in China. Due to the increasing use of electronic technology in pharmaceutical and health care industry, electronic data source becomes an upcoming trend with clear advantages. To face new opportunities and to ensure data integrity, quality and traceability from source data to regulatory submission, this document demonstrates important concepts, principles and best practices during managing source data. It includes but not limited to: (1) important concepts of source data (e.g., source data originator, source data elements, source data identifier for audit trail, etc.); (2) various modalities of source data collection in paper and electronic methods (e.g., paper CRF, EDC, Patient Report Outcomes/eCOA, etc.); (3) seven main principles recommended in the aspect of data collection, traceability, quality standards, access control, quality control, certified copy and security during source data management; (4) a life cycle from source data creation to obsolete is used as an example to illustrate consideration and implementation of source data management.
China ; Data Collection ; standards ; Documentation ; standards ; Information Storage and Retrieval ; methods ; standards