Biostatisticians and medical writers at enterprise pharmaceutical companies have a huge responsibility on their shoulders: ensuring that the Clinical Study Report (CSR) of the clinical trial is accurate, and ready to be submitted to the regulatory authorities for review. Managing this process within an enterprise can be especially challenging when teams are spread across multiple time zones, working in silos with no standardized processes.
CSR submissions often experience delays, both internal and external. An alarming 22% of these submissions don’t even make the cut due to errors. However, delays come at a heavy price. Fortunately, The answer may lie in automated tools that offer analytical solutions and automated data quality control to streamline processes and minimize manual errors.
This article explores how biostatisticians can benefit from the use of the latest analytics technologies to deliver timely, reliable CSRs.
What is a Clinical Study Report?
A CSR clinical trial is a scientific document that summarizes the entire clinical trial process, including study design, methods, results, and conclusions. It provides detailed information on the trial’s objectives, patient population, treatment protocols, and statistical analyses. CSR is a crucial document in the drug development process, as it plays a significant role in obtaining regulatory approval.
Clinical Study Reports (CSRs) provide a detailed account of the data and outcomes from medical studies used to evaluate the therapeutic effectiveness of drugs.
CSRs are required by regulators such as the FDA or EMA for product approval, and come in four main formats, depending on the purpose of the study.
The four CSR formats:
- Full CSRs – offer comprehensive clinical and statistical descriptions, alongside efficacy and safety data.
- Abbreviated CSRs – usually contain only abbreviated methods and efficacy sections.
- Synoptic CSRs – provide complete safety information without any evaluation of efficacy or clinical pharmacology
- Supplemental CSRs – supply further detail to a full study report. Tables, figures, listings and appendices are included in these reports to facilitate understanding of the data they contain.
The successful completion of a CSR is crucial for the development of therapeutic products.
What Happens When the CSR Does Not Get Approved?
The cost of a rejected CSR can be staggering for a pharmaceutical company.
Rejection can cause significant delays in the drug’s approval process, resulting in lost revenue and increased development costs. In some cases, the regulatory authorities may require additional trials to be conducted, which can add millions of dollars to the drug’s development costs.
The Challenges of Producing an Accurate and Comprehensive CSR
Producing an accurate and comprehensive CSR clinical trial is a complex and challenging process due to the following reasons:
- Data management – way more data collected today
Due to the widespread use of high-tech sensors and wearables in clinical trials, data management has become a complex endeavor in recent years because the amount of data collected is significantly higher even from a single study. The colossal amounts of data generated in a study must be diligently gathered, observed, handled and processed. Ensuring the accuracy and completeness of the data is critical to producing a reliable CSR. - Lack of standardization
Ensuring that data is collected and documented in a standardized format is critical to ensure meeting the strict FDA demands and other extensive regulations that a study must follow. When enterprises have multiple teams working on the same study across the globe and they work in silos without standardized processes, you have unclear data and ultimately poor quality reports that are rejected. - Insufficient quality control methods
Achieving regulatory approval requires ensuring the accuracy and completeness of the CSR. Even today, pharmaceutical giants rely on an unreliable method for quality control – double programming analysis datasets, tables, listings, and figures. This approach is ineffective as it fails to identify cross-table inconsistencies or unanticipated changes to data during clinical trials. To ensure success, we must explore more efficient methods! - Time and resource constraints
Producing an accurate and comprehensive CSR requires significant resources and time. The process of producing a CSR involves multiple teams, including programmers, biostatisticians, clinical researchers, data managers, and medical writers. Coordination between these teams can be challenging, resulting in delays and errors.
How Are Automated Data Quality Control Solutions Used?
Addressing the challenges of preparing an accurate and comprehensive CSR by incorporating automated data quality control can be a game-changer. The analytical solutions available today can streamline processes, reduce manual errors, and ensure that data is consistent and accurate across teams.
Data Inconsistencies Detection and Correction
One way automated data quality control solutions can help is by detecting and correcting inconsistencies in data. These solutions can automatically identify outliers, incomplete records, and data entry errors. This means that the data analyzed is of high quality, resulting in reliable results that are less likely to be challenged by regulatory authorities.
Standardization
Another way automated data quality control solutions can help is by standardizing data across teams. By enforcing standardized processes, teams across the globe can produce consistent and accurate data. This, in turn, helps to reduce delays in the CSR process and ensures that the final report is of high quality and meets regulatory requirements.
Visibility
Furthermore, automated analytical solutions can provide real-time data insights, providing biostatisticians with study data transparency to monitor and track data quality at each stage of the clinical trial. This can identify issues early on, allowing teams to take corrective measures quickly, minimizing delays and errors. Automation tools provide biostatisticians with complete knowledge of all data cross-check processes and the team members who carried them out. This helps prevent any extra discrepancies, ensuring that biostatisticians do not waste their time on competing versions of tables or duplicating efforts.
Ensure Consistent, Accurate, and Reliable Data
The use of automated data quality control solutions is vital in the production of high-quality clinical study reports. By streamlining processes and reducing manual errors, analytical solutions ensure that data is consistent, accurate, and reliable across teams. This, in turn, minimizes delays and improves the chances of regulatory approval. As a head biostatistician, incorporating analytical solutions into your CSR process is a step towards success.
