End-To-End Management Of Clinical Trial Data: A Quick Guide
Clinical data management is collecting, organizing, and managing research data for clinical research. The data management process ensures that the clinical research meets compliance standards and internal protocols in the research center. The end-to-end management of clinical trial data is critical for ensuring accuracy and consistency throughout the entire study lifecycle.
Researchers must follow certain best practices to ensure proper management of this type of data. These practices start with database design, Case Report Form (CRF) design, data entry, and reporting. This article gives you a quick overview of clinical trial data management (CTDM).
Clinical Data Standards
A critical aspect of the end-to-end clinical trial data management process is adherence to clinical data standards. The Food and Drug Administration (FDA) and other regulatory boards set these standards and help provide consistent data quality across different studies. Some of these include Electronic Data Capture (EDC), Common Terminology Criteria for Adverse Events (CTCAE), and Clinical Data Interchange Standards Consortium (CDISC).
Today, clinical research organizations must adhere to the CDISC data collection standards, ensuring the consistency of clinical research data and making it easier to analyze and interpret. The CDISC standards helped in overcoming legacy data standards. Organizations could design their forms and study formats in this free-for-all format, making it difficult to compare data between multiple studies.
The Clinical Data Management Process
The clinical data management process typically follows the following steps:
1. Reviewing Study Documents
The first step in the clinical data management process is to review all relevant documents related to the study—protocols, investigator’s brochures, informed consent forms, etc. During this stage, you’ll need to check items for study, such as the data to collect, the frequency of collection, and the units to use.
You also design a CRF during this stage. CRF is an electronic or paper-based form that captures the patient data you collect in the study. It includes demographics, health history, and other related details. The CRF should be user-friendly, concise, and self-explanatory.
2. Database Designing
Researchers must design a database to facilitate data collection and management. The database should include study details such as definitions, acceptable values range, intervals, CRF layouts, and other relevant information related to the clinical trial data.
The database design should be comprehensive to accommodate data from all sources used in the clinical trial process. Typically, tools used in database designing have in-built regulatory compliance features.
3. Data Collection and Monitoring
Data collection and monitoring is the most time-consuming task in the data management process. During this stage, data from different sources are collected, entered into the database, and checked for errors. During this stage, it’s essential to ensure that all the data collected comply with the clinical trial protocol and regulatory requirements.
To further enhance the efficiency of data collection and monitoring, AI patient recruitment tools are being increasingly adopted. These tools use advanced algorithms to sift through vast databases of patient information, quickly identifying and recruiting eligible participants based on predefined criteria. This not only speeds up the recruitment process but also ensures a higher accuracy in matching participants to the specific requirements of the clinical trial.
4. CRF Tracking
CRF tracking can help researchers manually monitor the completion of all the CRFs in a clinical trial. It tracks the status of each CRF, from collection to data entry. This ensures that all the required data is collected promptly and there are no gaps or delays in data entry. In case of missing and unclear data or incomplete CRFs, researchers must contact the investigator and collect the required data.
5. Data Entry
During this stage, all the collected data is entered into an electronic database and validated for accuracy and consistency with the study protocol. This stage only applies to paper CRFs that must be cleaned and entered manually.
It is essential to ensure that a double-key data entry system is used where two independent operators should enter the same data set into the database and then compare them for errors. Data from eCRF and other telehealth tools can be captured automatically and entered directly into the database.
6. Data Validation
The entered data is then validated by comparing it with the source documents, such as lab test results and specified protocols. During this stage, any discrepancies between the actual reading and what has been entered into the database are identified and corrected before data analysis.
The researchers use written programs to test for data accuracy and any abnormalities or missing values. The validation process includes range, logic, and consistency checks between data sources.
7. Medical Coding
Medical coding converts patient information into standardized codes for easy analysis and retrieval. This includes assigning standard codes to diagnoses, medications, and laboratory results so researchers easily compare between studies. The World Health Organization–Drug Dictionary Enhanced (WHO-DDE) is the standard to follow when coding medications.
8. Database Locking
Once the data is collected and validated, it must be locked before data analysis. In database locking, the researchers lock specific fields for further modification or updating so that any changes made to those fields will not affect the analysis results. The locked databases are stored for future reference in case of any queries about study data.
Conclusion
Managing clinical trial data is crucial to the success of a trial, ensuring accuracy, reliability, and regulatory compliance. Following clinical data standards and best practices, like thorough database design and double-key data entry, can minimize errors, leading to more dependable and reproducible results.
Additionally, proper data management makes it easier to analyze and interpret the collected data, improving study outcomes. Therefore, a well-organized and structured data management process is essential to maintain the accuracy, consistency, and integrity of clinical trial data from start to finish.
