Clinical Data Management Delivery of the Quality Data

Clinical trial is a crucial stage of drug development, which involves a long run process and thus produces huge amount of scientific data. This data will eventually make a new product useful and marketable in disease therapy. The clinical data management process relates to a sequence of activities which include development of a data management plan followed by study set up (CRF designing, database setup, etc.) and training. CDM is an on-going process and begins with the protocol development and ends at the completion of the statistical analysis. It involves the collection, recording, processing, analysing and reporting of the data; monitoring data quality and data safety; maintaining audit trial; database closure; data storage while ensuring the security and confidentiality throughout the process. The principal objective of data management is to deliver high quality data with minimizing the possibilities of errors or omission of data. To fulfil this purpose, best possible practices are adopted to make sure that the data processed or which is about to be processed is complete, reliable and correctly analysed. Thus, developing a data management plan (DMP) is the first step to be taken during the early setup of the study. DMP must define all the components of the data management process. Each component must specify the work to be performed and the responsible staff for work. It should ensure that the guidelines/SOPs will be followed as per regulatory framework. DMP remains live throughout the life cycle of a clinical study, to address any updates/changes made during conduct of the study. Shortly after, or along with the development of the clinical protocol, Case Report Forms (CRFs) are developed to cover all the appropriate data for analysis specified by the protocol. The CRF is handled by CDM team as the first step in rendering the protocol activities into data being generated. The quality of data relies on the CRFs. The CRF should be clear and concise as well as self-explanatory. The data fields should be clearly defined. Extraneous or redundant data should be strictly avoided as it adversely affects the data quality. Separate sets in the CRF should be developed for each visit, e.g. Screening, Randomization, Adverse Drug Monitoring, Follow up visits, etc. CRF completion guidelines (the filling instructions) should be provided along with CRF to avoid errors during data acquisition. S7bRXgVK
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