The Basics of Data Management in Clinical Trials
Introduction
Clinical Data Management is a process of collecting, entering, validating or cleaning the data obtained in the Clinical trial.
Pharmaceutical industries rely on electronically captured data for the evaluation of medicines, there is a need to follow good practices in CDM and maintain standards in electronic capture data.
Main Objectives of Clinical Data Management
While there are various benefits associated with clinical data collection, its primary goals can be distilled into three main objectives.
1. Data Collection
Regardless the data is in paper form or available electronically, clinical data management helps ensure that information is properly collected. Furthermore, correctly collecting and storing the data ensures that when it is needed later it will be easily accessible.
2. Data Validation
Validation is the appraisal and estimate of an item’s worth. This includes manual review for additional oversight, plus user acceptance testing (UAT), programming completed with edit checks in place, and quality controls. The aim of this is to identify how useful the data is.
3. Data Integration
Lastly, data integration allows you to put all of the data into a singular database. Having all your information in one place supports correctness and consistency.
Stages of Clinical Data Management cycle
1. Prepare
First, collect all of your necessary forms whether electronic or paper. Then review your plan and ensure your database is ready to receive information. The more organised you are at this phase of process the more seamless the rest of it will be and the necessary forms are collected.
2. Collect data
Now, you can begin the process by collecting your data. Continue to gather data throughout your study. Remember accuracy is first and foremost. It’s not a bad idea to check in periodically to make sure that your data is accurate.
3. Ensure accuracy
Make sure that the tools you are using your plan itself, and the data collected meet regulatory requirements. Data quality is a top priority So if there are any discrepancies, the sooner you can figure it out the better.
4. Keep track and preserve your data
It’s time to keep track your data. Make sure that you’re monitoring it for potential issues or risks that may pop up. This is also the right time to think about what you can do to preserve your data’s quality.
5. Integrate your data
Monitor your data for issues, and preserved its integrity. Now, it’s time to map out datasets and information together. Consistency is critical throughout the process.
6. Analyze the outcomes
Because your data is clean and consistent, you can use it to analyze the outcome and feel secure about its accuracy. That brings us to our next point that your clinical data management cycle isn’t done once you’ve analyzed the outcome. Now, it’s time to make sure your data is protected.
7. Protect your data
After completing the clinical data management cycle take time to secure your database so no information is misplaced or edited.
Roles & Responsibilities in Clinical Data Management
In a team of CDM professionals there are many roles & responsibilities that are attributed members of the team. The basic educational qualification requirement for a team member in CDM must be a graduate in Life Science & knowledge of computer applications. A few key roles are essential in all CDM teams. The roles are mentioned below and must be considered as a basic requirement for a CDM team.
Data Manager
Database Programmer/Designer
Medical Coder
Clinical Data Coordinator
Quality Control Associate
Data Entry Associate
Conclusion
The ultimate goal of CDM is to assure that data support conclusions drawn from research. Achieving this goal protects public health and confidence in marketed therapeutics. There is a gradual transition from paper-based to electronic system for CDM process to match the expectations of pharmaceutical companies and regulatory agencies for generating high quality data.
References
1. Gerritsen MG, Sartorius OE, vd Veen FM, Meester GT. Data management in multi-center clinical trials and the role of a nation-wide computer network. A 5 year evaluation. Proc Annu Symp Comput Appl Med Care. 1993:659–62. [PMC free article] [PubMed] [Google Scholar]
2. Lu Z, Su J. Clinical data management: Current status, challenges, and future directions from industry perspectives. Open Access J Clin Trials. 2010;2:93–105. [Google Scholar]
Student Name: G. Dileep Lumar
Student ID: 076/042023
Qualification: B. Pharmacy
e-MailID: gandidileepkumar5@gmail.com
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