Given our current technological landscape, it is difficult to imagine that approximately 20 years ago, electronic data capture (EDC) was just emerging as a novel tool for clinical trial management. Prior to this, paper collection of data at study sites, followed by duplicative manual data entry and analysis by the sponsor was the norm. Technological innovation of computer systems has made capturing and analyzing data for clinical trials easier, faster and more robust. This has resulted in improvements in data cleanliness and the speed at which the data can be analyzed while enhancing the assurance of patient safety. These advancements have also made it possible to collect even more data from more sources than ever before. Consequently, the implementation of a data analytics system on top of these many data sources used in a clinical trial can provide a transformative landscape for the future management of clinical trials and may facilitate reducing time to database lock, provided it is well designed and thought out.
Typically now, a patient-based clinical trial (generally, phases II, III and IV) will have multiple computerized data collection sources. These often include an EDC system where patient related data is collected, a clinical trial management system (CTMS), where investigator and site level information is managed and collected (often by a contracted clinical research organization), an interactive response technology system (IRT or IxRS) where patient visit registration and drug dispensation occur, as well as various other safety and lab databases. In addition to these standard sources, some trials may use wearable devices to collect health data or use apps orother electronics to collect patient reported outcomes.
Implementing an Overarching Analytics System
With the ever increasing number of possible data sources, enhanced transparency into the data over the course of a trial becomes vital and this is where an overarching analytics system can provide immense benefit. Many sponsors opt to significantly or fully outsource the execution and management of trials to contract research organizations (CROs), thereby shifting the role of the sponsor clinical study team from one of day-to-day management to that of high level operational oversight. When this approach is taken, contracts for the trial’s various data sources are frequently owned by the CROs. Control of timing, start-up efficiency as well as future modifications to systems and processes can become measured and complicated, ultimately resulting in restrictions to the sponsor gaining transparency and real-time access into the data of their trial. In addition, operational reporting needs are not always met due to limitations in system output capabilities; data tracking and formatting can be prone to errors and cumbersome (consider the ubiquitous excel spreadsheet, a traditional reporting output), reports can contain outdated information and issue detection and analysis can be delayed after an event incident which might have otherwise been prevented had there been an early signal detection methodology in place.
As the pharmaceutical industry advances with innovative data capture techniques for clinical trials, it becomes critical to have complete transparency into the data as a part of the clinical trial process. Given the complicated and evolving backdrop of clinical trials, using a well-designed data analytics platform to consolidate relevant information into one access point can reduce risk, increase the success and practicality of sponsor oversight and contribute to the sustainability of the overall business. In addition, a thoughtfully planned analytics system can enable early signal detection, providing heightened assurance of patient safety, allowing early insights into enrollment issues, identifying areas of site training concerns, providing awareness into better case report form design, as well as acting as a single, real-time source of truth for clinical trial data.
The Essential Components of an Analytics System
A well designed analytics system should take the following questions into consideration:
- Will there be a separate budget to facilitate the building of an overarching analytics system or will individual study budgets contribute to the costs? Will there be additional costs to access source data from a vendor?
- Which function will own the system and training?
- Will the system be used for all trials or only specific trials?
- Are the source systems capable of connecting directly to the analytics system or are flat file data transfers necessary (and at what frequency)? Does the system allow for quick integration of data?
- Will all available source data be integrated or source systems or leverage only the data that matters? Once the data is in the analytics system, is it accurate?
- Is the data in a format that is easily understandable and are reports providing meaningful information?
- Is the system flexible enough to be customized as required, understanding that there are differing needs and nuances to all trials, even within the same therapeutic area?
- Is the system designed to help study teams make quick and educated decisions based on the data they are seeing? Are there alerting mechanisms, or threshold indicators to help identify areas of potential concern?
- Does the system allow portfolio views of the data and can data be archived?
Some may question the use and cost benefit of implementing a separate analytics system if a CRO already has a similar tool available. In this case, a sponsor should consider how their analytics system will be used differently than the CROs and how to handle potential data conflicts between the two systems. Since using data analytics as a means of trial oversight is a relatively new concept in the industry, the technology is obviously in flux and at varying stages of maturity. A CRO’s system may still be in an early development phase and simply not robust or customizable enough to meet a sponsor’s need. CROs with this technology will also often only allow sponsors to access certain areas of the system, or charge additional fees for more detailed reporting and metrics. In addition, the data available from the CRO may not truly be real-time. A sponsor owned analytics tool should facilitate oversight into CRO management of the trial – ensuring complete and up to date data is maintained in the source systems. It allows for better insight into and oversight of a trial, enabling control over the data as well as the development and functionality of the system and portends the future of the industry.
Considering the Risks
Clearly there are several key considerations that need to be made when implementing an analytics tool and implementing such a system is not without risk. The cost of implementation may hinder the scope of trials that can be included, key players may not fully embrace or appreciate the long term benefits of the technology, change management may be difficult, particularly if it means a shift in the way day-to-day work is done. There must be robust training and it is essential to have buy in from stakeholders. If designed well, the impact of a comprehensive analytics system to the business can be far-reaching and well worth the initial and ongoing investment. It should make the job of all involved in the execution of a trial more efficient by prioritizing the issues that need to be addressed. As the design and capabilities of analytics systems evolve over time, the industry will likely see a move toward this technology as a primary management tool and it will likely result in shifting how clinical trial management is currently done. This is already subtly being observed in CROs and sponsors that are using analytics tools to move toward a risk-based monitoring approach to managing trials. As the industry moves toward better management and analysis of the innumerable points of data that is collected, the safer it will be for patients, the better equipped it will be to foster positive trial outcomes, and the faster and less costly it will be for drugs to be brought to market.
Senior Manager, Resource and Analytics
*The views expressed in this article are solely those of the author