The global clinical trials market size is expected to reach US$68.9 billion by 2026 and projected to expand at a CAGR of 5.7% during the forecast period. There is substantial development of new treatments, globalization of clinical trials and collecting and analyzing data with the use of artificial intelligence and machine learning. There has been a major shift from collecting and analyzing data on paper CRFs to actually using patient’s own devices (Bring Your Own Device) to integrate real time patient data in the database. The role of data management has also evolved from simple yet time consuming Case Report Forms designing on paper to very complex electronic case report forms that allow integrating of external data, risk based monitoring modules and extensive study conduct and closing activities. Data management can no longer be restricted to only the data management activities in 3 phases of Study Start Up, conduct and close out but needs to evolve as a key account in a clinical trial. Cost and time of delivery of data collection modules would soon be only table stakes and a lot of emphasis would be on intangible characteristics like flexibility especially with the advent of adaptive trials. CDM 3.0 is only a matter of time and data managers need to be creators rather than learners in this phase.
Paper Studies and Data Management:
• Data management in clinical trials began with recording data on case report forms as data collection modules in paper form. Though simple and straight forward, designing of paper CRFs required extreme attention to detail to capture all required data per protocol, avoid spell errors leading to a fat case book.
• Data management would be onboard the study right from the kick off meetings, working to understand the study and the sponsor requirements to pick up pace for the CRF designing.
• CRF printing, managing printing vendors for expectations is a characteristic feature for paper studies data managers.
• CRF tracking by data management on receipt and sending of CRFs, tracking missing CRFs, follow ups was an ongoing task till study conduct.
• Data review as well as reconciling the data with applicable external vendors was an uphill manual task, prone to errors.
• Discrepancy management in paper studies is a time consuming task, with offline follow ups with the site coordinators, Clinical research associates and Investigators. Handwritten data was a review parameter as well which required to and fro for confirming what is written is what it appears.
• A critical issue was also of tearing, damaging of CRFs and in the worst cases, misplaced or lost CRF shipments in transit.
• Creation of supporting documents like Data management plan, data entry guidelines, milestone checklists, transfer agreements are created and maintained by the clinical data managers from the perspective of paper studies.
• Archival of paper CRFs is also taken care of by the data managers per the data managed plan and are required to be stored per retention period in fire proof and access controlled archival hubs.
• Paper studies are advantageous for studies on a shoe string budget which may not have available on-site technology. However, maintaining a paper study for a mega trial like oncology therapy is a laborious task.
Electronic Data Capture and Data Management:
• The essence of CRF designing and data review is the same as that in paper studies the only difference being use of electronic data capture tool, instead of papers.
• With the advent of 21 CFR part 11 compliant electronic databases, it was possible to convert part and complete data capture electronically, thus reducing time, cost and duplication of data entry and review.
• The regulatory authorities were more than happy to accept project submissions in which validated and authority prescribed electronic data systems were used. It is currently mandated by the FDA that all studies need to be submitted in FDA prescribed tabulated formats (CDISC compliant) and paper submissions are no longer accepted.
• With the increasing acceptance of use of electronic data capture, the role of data managers expanded from a paper CRF designer to that of an edit check writer and also developing the data validation specification document. The data manager had to be aware of the check logic and needed to collaboratively work with the clinical programmer for database development.
• Data managers now had to be well versed with using computer applications, data entry, quick troubleshooting as well as understanding and highlighting various simple to complex technical errors or downtime issues and convert with the technical as well as database vendor to resolve them to maintain data integrity.
• The transition from paper to EDC also involved hybrid studies, where paper CRFs were entered using single or double data entry by data processor team and then validated by the data managers with the help of edit checks or manual queries in the database.
• With time, the databases also got more user-friendly and one could access the database by logging on the URL account. There was no more need of using the central/vendor server where data would be saved before transfer on to the front end database.
• This also reduced the follow ups and data could now be reviewed in real time by the data managers, site and also verified by the monitors.
• Monitoring also evolved more and the risk based monitoring has become increasingly acceptable. This puts the onus on the data management team to understand the requirements of the SDV’d and design the tier based SDVd in the EDCs with the help of clinical programmer.
• Discrepancy management in EDC is relatively simpler than paper when the edit checks are developed comprehensively, while still within the project budgets.
• EDCs also allowed data integration from external vendors like IVRS, central labs, derived value calculations from the back end (e.g. auto calculation of age from date of birth) and running coding of Adverse events and concomitant medications by setting up the coding dictionaries prescribed by the MedRa and WHO in the system.
• While designing an eCRF, repetitive data such as protocol ID, site code, patient initials and repeat forms like Adverse events and concomitant medications would be generated automatically, thus ensuring no duplication and a lean case book. The efforts by the clinical research coordinators as well as data managers were optimized.
• In EDCs, data managers also had the opportunity to restrict data updates by the sites after confirmation of cleaning completed by freezing and locking the data. This ensured, data was not updated and missed to be reviewed without the DM’s attention.
• PI could also bulk sign the CRFs and validate data entered as correct.
• Providing Training on the EDCs and access controls became vital for the data managers while using EDCs.
Road Ahead and the Role of Data management:
• With increasing acceptance of risk based monitoring, we can also think of risk based data review gaining acceptance in the near future.
• As machine learning and AI incorporate in clinical trials with increasing use of hand held devices from the patient, onus is on data management to create CRFs that can “read and transcribe” directly from the source documents, something like what we see in the grocery store when the store manager scans the bar code of the product which is then converted as details on the screen.
• AI has already arrived in site selection where companies are claiming to identify appropriate sites and also fit for the study patients in less than 30 minutes using algorithms. With this progress, the data management would be under duress to deliver databases ready to capture data within a few days and not weeks as is currently the case.
• Hence, creating libraries of standard CRFs expanding and maintaining the libraries is imperative for the data managers. Data managers cannot invest lot of time in creating CRFs from scratch; they should only tweak standard templates to suit the protocol.
• As data integration from external data is possible, so should be real time SAS analysis of the data entered in the database. Currently, sponsors are losing a lot of money and efforts when the interim data is sent for analysis and found to be way off the expected endpoints. With real time SAS analysis integration, sponsors have a choice to discontinue a trial at a much earlier stage.
• Adaptive trials are the way forward to saving time and efforts in clinical trials and sending more and more molecules to authorities for approval. Hence the data managers need to evolve into data architects and create tremendous potential for adaptive trials.
• The key is to create the most complex, robust database, yet the most simple to be used: something like the apple products.
• Lastly, data managers need to be creative and flexible while creating the databases. I understand I have been stressing a lot on databases on not on discrepancy management, however, need of the hour is to create the most intuitive, complex database that minimizes data entry errors and allows data to be transcribed directly from the source. If this is the case, the data entered in the database is as correct as the source and hence diminishes discrepancy management.