Learn About Clinical Research Data Management

Clinical research data management is to discover, clean up and confirm the clinical data that does not meet the program, are not logical, or incomplete, so as to make the data more reflect the actual situation of clinical research for the subsequent statistical analysis, and carry out a series of data clean-up activities to provide a reliable data source for the true subsequent statistical analysis result.


Real Challenges of Clinical Research Data Management

  • Insufficient standardization of data structure: Although the CDISC standard has been proposed at the current regulatory level, most small and medium-sized domestic pharmaceutical enterprises are still unfamiliar with the standard and have difficulties in implementation, leading to non-standardization of data and disorder in data management.

  • Large proportion of manual operation: the existing mainstream EDC system cannot fundamentally solve the requirement of many manual verification, resulting in a lot of manual work during the data management implementation process. This really causes high costs and low efficiency.

  • The low degree of cooperation of investigators: the possibility for investigatorsnot to read the guidelines, or participate in the EDC system training is high. In addition, they may overlook the importance of responding to queries, replying at will or even do not reply, resulting in lack of standardization or data missing.


To Standardize Clinical Research Data Management is Imperative

  • At the regulatory level: In 2016, the SFDA released No. 112 and No. 113 notice, to explicitly put forward the necessity of data management and provided detailed norms for the actual operation of data management.

  • At the enterprise level: Pharmaceutical companies gradually value the quality of clinical data and have more meticulous and more stringent requirements for the operation and implementation of data management.

  • At the executive level: The clinical data itself has the characteristics of complexity and diversity, showing the inevitable trend of data management standardization. Clinical data must follow the ALCOA + principles, which integrates attribution, legibility, simultaneity, originality, accuracy, completeness, consistency, persistence and availability.