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Data Management In Clinical Research


Efficient collecting, transferring, and managing of data during a clinical trial is crucial and helps advance the drug development process in all clinical research organization.




Approval of novel drugs by the authorities is contingent upon trust and belief that clinical trial data are of sufficient integrity. From database build to database clasp, clinical data management (CDM), the process of collecting and managing subject and/or trial data in compliance with regulatory standards is a crucial component of any clinical research. When done efficiently, CDM of disease trials leads to the generation of statistically sound data and accelerates the drug development process.


Introduction to CDM


From a business point of view, drug developers want to ensure that the data delivered to regulatory bodies is dependable; from an ethical perspective, clinical data inform treatment decisions and ultimately affect patient's health. Due to both of the reasons, clinical data quality and integrity are crucial. Though it seems that data management only happens after the data are collected, the process begins before the study protocol is finalized. Since CDM is best viewed as a process that is very important throughout the entire clinical study, it is important to involve the CDM team from the beginning, starting with protocol development.


CDM PROCESS


A Case Report Form CRF is designed by the CDM team for data collection from protocol-specific activities. The CRF may exist in either a paper version or as electronic data capture. The CRF will be annotated with coded terms where the data collected for each question is to be stored in the database.


Next a Data Management Plan is made which details how the data are to be handled according to how the study is thought to run. A DMP describes the CDM activities to be followed in the trial, The DMP is intended to standardize procedures and ensure that all CDM personnel understand the plan.


Next, a Data Validation Manual is developed. This document contains the edit and check programs for disparity identification. Entry of data that are not validated may prompt a request for clarification. This process is called discrepancy management or query resolution, is put into place to investigate the reason for the discrepancies; ideally, discrepancies should be resolved quickly. Discrepancies should be reviewed regularly by the CDM team to ensure that they are being resolved in time.


Coding of all medical terms allows standardization when it comes time for the data to be analyzed and reviewed. Information on adverse events, medical history, and medications must be coded in a uniform manner.


Finally, a database clasp is put in place after all data management activities are completed to ensure there was no manipulation of study data after revealing of the treatment groups and during the final analysis.


CDM LEADERSHIP


As with any team, there is always a leader. The CDM team is headed by a data manager who is responsible for looking after the entire CDM process and coordinating data management activities. The data manager is a key player in early discussions about data collection options.


Although most clinical trial data are now entered into the database via EDC by authorised personnel. Although the roles and responsibilities may vary slightly for specific study, the aforementioned team members are considered the minimum requirements.


TIPS FOR EFFICIENT DATA MANAGEMENT


After a query is issued, the study staff will have to go back and address the query, which takes significantly more time than simply being thorough and consistent in what is being graded from visit to visit. It seems like common sense, but the fewer queries, the better.


Another thing to consider is the true meaning of “relevant” medical history. As far as data management in clinical trials is concerned, medical history occupies a significant amount of time and is often a low lying endpoint, as it does not affect the key endpoints. A lot of time is spent cleaning medical records that have little relevance to the study instead of recent or more pertinent medical histories given the disease state.


Lastly, adverse events occurring in one part of body are coded differently from those occurring anywhere else . Ensure that adverse events or medical history terms used in recording are complete and precise.


CONCLUSION


Careful clinical data management is very important to the integrity of a clinical research. Involving the CDM team early on ensures that a solid data management plan is set forth from the start.


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