Clinical data management is a fundamental element for the success of a clinical trial. Conducting a clinical study implies large investments and, after all, the final result of all these efforts are datasets. Then, clinical trials are all about data, and trial sponsors should ensure that robust and compliant clinical data management procedures and tools are in place.
- $What Is Clinical Data Management in Clinical Trials?
- $What Are Some Clinical Data Management Best Practices?
- $What Are Some Essential Elements Involved in Clinical Data Management?
- $At What Point in a Clinical Trial Are Data Management Procedures Needed?
- $What Are the Main Clinical Data Management Services Provided by CROs?
- $How Does Sofpromed Help Sponsors in Clinical Trial Data Management?
What Is Clinical Data Management in Clinical Trials?
Clinical data management is the process of managing the data of a clinical trial. This data includes everything from participant characteristics and medical history to the treatment administered during the trial and individual participant safety and efficacy outcomes. This data must be collected, stored, organized, evaluated, and presented to scientific communities, national regulatory authorities, and pharmaceutical regulators. This comprehensive process is summed up in the term “data management”.
The purpose of data management is to ensure that the generated data support the findings of a clinical trial. In a clinical study, drug developers have to ensure that the data delivered to regulatory bodies are reliable and robust enough to prove the safety and efficacy of the new drug and to guide proper treatment decisions by doctors, both of which impact patient health.
Not only must the data meet the highest scientific standards, but it must also be managed and submitted according to the laws, regulations, and requirements of each country, including those of the regulatory bodies that oversee clinical trial execution and assess drug marketing authorizations.
What Are Some Clinical Data Management Best Practices?
Clinical data management best practices are aimed at ensuring high-quality data based on the following characteristics:
- Cleanness
- Traceability
- Readability
- Regulatory compliance
- Accessibility
- Completeness
At the same time, in a clinical trial, the goal is to gather as much relevant data as is necessary to prove that the proposed drug or treatment is safe and effective. Naturally, this requires collecting large amounts of data. Advances in the field of clinical data management have grown out of the need to handle the data produced in clinical trials accurately and efficiently. This need has only increased with the drive to “fast-track” the development of new medicines, as became especially evident during the push to develop vaccines and treatments for the Covid-19 virus.
The development of computing and digital data management technologies has been a game changer for clinical trials. Until recently, clinical trial data had to be handled on paper and involved laborious processes that were prone to inaccuracies and discrepancies.
Although legacy software applications may seem an affordable and viable solution, they have not proven to provide adequate functionality, such as for traceability and security, for clinical trials.
Many new software tools have been developed specifically for the highly sensitive and intricate data management procedures involved in clinical trials. These tools are integrated into a complete digital workflow from the data management planning process to data capture, analysis, and preparation for submission to regulators.
As clinical research has become more complex in recent years, and the demands of regulatory authorities and consumers more stringent, data management systems have turned out to be essential to the success of clinical studies.
What Are Some Essential Elements Involved in Clinical Data Management?
- Tools for capturing the data related to the items of the clinical trial protocol in multiple iterations of detail and developmental versions (e.g. Electronic Data Capture -EDC- system).
- A flexible way of structuring Case Report Forms (CRFs) to easily rewrite them according to trial protocols.
- Automated procedures for generating and customizing data entry windows to correspond to the CRFs.
- Definition of complex data validations and derivations requiring different levels of programming.
- Data cleaning and discrepancy management tools, to detect and correct erroneous data.
- A flexible internal database with the ability to reorganize data for extraction.
- Maintenance of lab reference ranges across multiple studies.
- Clinical data management systems can be built and validated according to regulatory specifications so that data management and the submitted data comply with regulations.
At What Point in a Clinical Trial Are Data Management Procedures Needed?
In clinical trials, data management comes into play from the very beginning (e.g at the outset of the project, when the clinical trial protocol is written). Although final data analysis takes place once all participants have completed their treatments and follow-up visits —when the final data is gathered and analyzed to generate the results— a data specification exercise is already needed in the initial protocol (e.g. tests defined in the schedule of clinical assessments).
Therefore, while data management may seem like the last step in a clinical trial, this is not true, and it is best practice to plan for data management early on. The manner in which data is collected and processed can impact both the global budget and the final submission timeline. Planning ahead to ensure that poor data collection and processing does not negatively impact the outcome of the trial, or the response of regulators, saves time, money, and frustration.
What Are the Main Clinical Data Management Services Provided by CROs?
The clinical data management capabilities offered by clinical research organizations (CROs) include a suite of highly specialized services to help sponsors handle the data produced during a clinical trial. The following are examples of some of the principal services offered in that regard.
eCRF design: Electronic case report forms (eCRFs) —also called Electronic Data Capture (EDC) systems— allow for electronic data entry directly into the clinical trial’s database. EDC systems can also be built with integrated query management tools to assist with communication between the sponsor/CRO’s data reviewers and the clinical site staff who enter the data. Tracking is another common feature of EDC systems so that the source of data is always clear, which is essential for drug approval. Finally, EDC systems can export data to different formats for subsequent analysis. Additional advanced functionalities can further assist in cleaning, labelling, and preparing data for submission to regulators.
CDISC services: The Clinical Data Interchange Standards Consortium (CDISC) has been particularly active in galvanizing technologies into industry-wide standards for clinical data management. This international body has developed platform-independent protocols for electronic collection, exchange, archiving, and submission of clinical and non-clinical trial data.
The most relevant and commonly used CDISC standards are the Foundational Standards, a complete suite that includes models, domains, and specifications for supporting clinical and non-clinical research processes from beginning to end. As for clinical trials, these are the Study Data Tabulation Model (SDTM), the Analysis Data Model (ADaM), and the Clinical Data Acquisition Standards Harmonization (CDASH).
The SDTM defines a standard structure for human clinical trial data and is currently required by the U.S. Food and Drug Administration (FDA) when submitting new drug applications. CDASH creates a standard format for collecting data (at the EDC level) across studies to make data submission more easily tracked and reviewed.
SAS programming: SAS is a programming language commonly used to handle the large amounts of data collected and processed during a clinical trial. Statistical programmers use SAS to sort through the data of a clinical trial, perform analyses, and draw out the conclusions.
How Does Sofpromed Help Sponsors in Clinical Trial Data Management?
As we have seen, data management is one of the most important parts of a clinical trial. It can also be one of the most complex and time-consuming processes, and, if not carried out properly, it can derail an otherwise good clinical trial. Professional data management services from CROs ensure top quality compliant data, so that all the costly investments made during the study finally translate into acceptable results for regulators.
Sofpromed is an expert in clinical trial data management, particularly handling data in oncology, cardiovascular, CNS, dermatology, infectious, and respiratory disease studies. Our specialists are highly experienced in managing the entire clinical data management process (from initial specification to final reporting), including the provision of EDC software —with all associated services—, CDISC, and statistical programming capabilities.
