The success of clinical trials we handle at Biotrial depends on the quality and integrity of our data. In this article, we look at the methods and best practices employed by Biotrial to maintain the quality and integrity of preclinical and clinical trial data.
Ensuring data integrity and quality is an important component of the pharmaceutical industry’s responsibility to demonstrate the safety, efficacy, and quality of drugs. It is also a key element to enable regulators to protect public health. Data integrity is critical throughout the “data lifecycle”, from the initial creation of the data through any transfers, processing, analysis, or reporting stages. The Food and Drug Administration (FDA) uses the ALCOA acronym to define expectations for data, indicating data should be: Attributable, Legible, Contemporaneous, Original, and Accurate.
Due to our reliance on data and the interdependent nature of our teams as a Full-Service CRO, systems must be in place to uphold integrity and quality as we collect, transfer, process, and report data back to our sponsors.
At the heart of data integrity lies meticulous data collection, recording, and storage. Following the correct processes for Data Collection helps ensure data is trustworthy and durable. This has led Biotrial to write precise and consistent protocols for these steps. We utilize standardized data collection forms and electronic data capture (EDC) systems to ensure consistency and accuracy while mitigating the risk of errors and discrepancies that could compromise the data. Users are provided precise guidelines and training to use the data collection tools.
Data Quality and integrity are both audited throughout a clinical trial study. At Biotrial, we use two specific methods: Source Data Verification (SDV) and Data Validation Methods SDV is a crucial process that ensures the accuracy and completeness of data collected during clinical trials. Trained monitors and Clinical Research Associates (C.R.A.) carefully cross-check the data against source documents to identify any missing data and inconsistencies. This process may be either carried out at investigation sites or remotely using the appropriate software. Risk-based monitoring is also often implemented to allow monitors and CRAs to focus on the key trial data and on-site support activities. When risk-based monitoring strategies are implemented, it shifts a greater proportion of data review and validation to the Data Management and Statistical Monitoring groups, as described below. Whatever the monitoring strategy chosen, these activities are performed throughout the entire study. Data validation methods focus on identifying errors, omissions, and inconsistencies within the data. They are based on the programming of the predefined checks, production of review listings (medical review), and Quality Control (QC) performed by the Data Managers to ensure the accuracy of data. Interactions between Data Managers, Medical Monitors, CRAs, and site staff are key to these validation steps. In cases where risk-based monitoring is implemented, the role of the distant data reviewers becomes even more essential in ensuring data quality and integrity.
Independent safety monitoring committees are a crucial element in ensuring participant safety during clinical trials. For a clear picture of how these groups can guard the integrity and quality of data, let's explore the relevance of two notable groups used to audit Biotrial's study results: the Data Monitoring Committee (DMC) and the Safety Monitoring Board (SMB). A DMC is composed of external experts who assess and evaluate the safety, accuracy, and efficacy of a study’s proceedings. The committee will meet during the trial to review the results of an interim or futility analysis. The external nature of the DMC committee reduces the risk of bias and conflicts of interest that could lead to integrity failures and compromise the validity of the study. Similarly, the SMB (or DSMB) is comprised of independent experts who work separately from the DMC. They will review emerging safety data of the trial regularly and, regarding data integrity, will share the concerns of the DMC, constantly auditing data gathered throughout the study for any indication of safety risks. Both groups identify safety risks early and make recommendations or call for study termination if problems are indicated. They not only help ensure the validity of the trial but address the quality through the exploration of the statistical significance of the study and other possible implications.
As a CRO, Biotrial is responsible for translating clinical trial data into value for our sponsors. High-quality data should be delivered in a way that is accessible and relevant to the sponsors and authorities specific needs. Fortunately, data visualization is an effective tool for transforming complex clinical trial data into insightful and digestible visuals for sponsors. By presenting data visually in addition to written reports, our sponsors can quickly grasp critical insights, leading to informed decisions and increased confidence in the ability to provide a trustworthy answer to the questions asked by the protocol – e.g., is this treatment safe? Is this treatment providing a benefit? Effective data visualization aids in the interpretation of study results and supports decision-making, which directly influences the data quality overall.
To ensure the validity of study results, the data management party must have upheld high standards of quality and integrity throughout the project’s lifecycle. At Biotrial, experts undergo regular training to comply with regulatory guidelines set by authorities including the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Best practices not only involve compliance with regulations but also consider adherence to standards such as Good Clinical Practice (GCP) to protect participant rights, data privacy, and trial integrity. By adhering to these guidelines, a CRO can preserve integrity and ensure that the trial outcomes are scientifically sound and trustworthy.
Data integrity and quality are paramount in clinical trials as they directly impact the reliability of the results obtained. Rigorous data collection, source data verification, and adherence to regulatory standards are the keys to maintaining data integrity. Furthermore, Data Visualization along with the establishment of independent monitoring committees, contribute to the overall success of a clinical trial. By upholding the highest standards of data integrity, clinical trials can make significant strides toward improving healthcare and saving lives.