Implementing a clinical data strategy can be complicated for any clinical trial Sponsor. According to a recent survey on data strategy launched by CROS NT, around 50% of companies have a clinical data strategy in place with a dedicated biometrics manager. However, this also means that almost half do not have strategies or dedicated resources.
The truth is there is no “manual” available which describes how to prepare a global clinical data strategy. While larger companies may be better equipped to manage biometrics, smaller companies struggle to deal with common challenges of data collection and reporting.
CROS NT’s survey reported that most companies find data standardization and data collection from multiple databases (i.e. lack of centralization) as the biggest areas of improvement in their clinical data strategy.
However, there are steps Sponsors can take to mitigate issues and optimize the development process from early phase to post-market.
Starting with Clinical Data Management
Preparing a data strategy should start as early as possible and include the clinical development team, regulatory and marketing teams as well as any third party vendors.
Two key elements of any strategy include standardization and centralization.
Within the data management team, you strategy should include the standardization of:
- CRF Templates
- Annotated CRFs
- CRF Completion Guidelines
- Data Lifecycle Plans
- Data Validation Specifications
- TA/Phase specific
When looking to outsource clinical data management, niche providers of clinical data services will often have internal committees to oversee global standards governance and harmonize data collection and processing elements on a global scale. These teams can identify, create and utilize cross-therapeutic standards.
What are the steps Sponsors can take, potentially with support from a niche clinical data services provider, to prepare a global data strategy?
- Develop and deploy clinical development standards (templates and data)
- Develop and deploy clinical development process documents
- Select vendor and solution to satisfy defined business requirements and priorities
- Develop and deploy a standard reporting system and processes
- Develop and deploy an on-going safety monitoring system using J-Review
- Develop and deploy KPI tracking, budget tracking and discuss long term solutions
- Develop a technology roadmap and implementation plan
- Enhance global libraries for paper/EDC studies and upgrade EDC technology
- Conduct analysis around the clinical process of the Clinical Study Report development and submission to identify improvement areas
Guide to Selecting an EDC System: Cost vs Functionality Matrix
When factoring in the technology aspect of your data strategy, Sponsors need to consider budgets and how to choose the best technology that will produce the best study results. Below are some recommendations from Clinical Data Managers:
- Consider an EDC solution provided as Software as a Service (SaaS) which can improve the trial process in terms of greater efficiency, better decision-making process and cost-cutting measures. The DM team can resolve discrepancies faster and reduce time and costs with immediate feedback from patients. A cloud solution can organize multi-language, global study data into a single database.
- Evaluate the level of technical support you expect with an EDC system. The more advanced systems provide excellent initial training programs and top level HelpDesk support. Baseline technology systems allow the Sponsor to work more autonomously with regards to study build and management.
- Consider how you will manage the EDC license and hosting solutions. Some systems include this in their offering while others provide various “à la carte” options.
- Some EDC systems are part of a larger integrated platform for seamless data integration among ePRO, CTMS, IRT and other clinical research systems. Falling into the advanced technology, competitive pricing category, an integrated system may be worth the investment for your program of studies.
Lastly, don’t forget your functionality checklist while evaluating EDC systems:
- Can you build your own study?
- Are there data entry, coding and randomization features?
- Are there Project Management tools?
- Are there customized reporting features?
- Are there reusable templates and forms?
- Is the system compatible with GCP and/or regulatory guidelines? Is there an audit trail?
- Can the system handle multicentre, global studies?