No one plans to have a trial whose data collection needs rescuing. However, lagging enrollment rates, operational struggles, and diminished budgets can leave some trials in need of intervention. A great deal has been written about how to prevent the need for rescues (e.g., more investment in study planning and improved communication between stakeholders). Far less has been said about how to assemble a rescue team – the roles that need to be filled and the process of analysis that ensures the completion of a failing trial.
There are some common skills needed by all teams parachuted in to rescue an ailing trial, and common objectives to adhere to when bringing the project back on track.
Re-establishing Efficiencies The idea of regaining or re-establishing efficiencies is intuitive and underlies the fact that rescue studies are also lagging studies. Each day of delay due to enrollment problems or confusion with data monitoring increases the costs and the likelihood of trial failure.
Cytel's services team has articulated three high-level factors that correspond to increased efficiency: expertise, experience, and communication. While subject matter expertise may help with project specific challenges, operational experience helps project managers and others anticipate the difficulties that might arise, and to identify quickly those key factors that caused the need for the initial rescue. These operational experiences might include setting up (or transferring) clinical databases or establishing a statistical analysis plan as efficiently as possible. Communication is also essential for re-establishing trial execution efficiencies.
Patti Arsenault, Cytel’s VP of Quality Assurance, who worked on many rescue studies in her previous data management roles, identifies the following vital aspects the rescue team needs to bear in mind to keep on track. Goals: In attempting rescue studies, it is imperative that the data is shown to be reproducible and that all errors and inconsistencies are explained or eliminated. There must be a transparent process regarding what to do when reconciliation cannot occur. Documentation: Patti also cautions that watertight documentation and reporting are critical parts of rescuing a trial. After all, given the higher levels of regulatory oversight, it is essential to leave a clear audit trail to ensure that regulators know that evidence is being treated objectively. In the case of Cytel, ACES software allows regulators to track the use and access of data. Charter: Clearly outlining all processes and objectives reassures regulators that necessary precautions were taken when managing data. A thorough DSMB Charter will also account for reporting of both open and closed-door sessions, a statistical monitoring guide, and other information to testify that data was always correctly used. Biostatistics: As in all trials, a robust statistical analysis plan and rigorous statistical findings are essential. Cytel project teams provide statisticians to attend data monitoring committee meetings to ensure streamlined data and statistics. Team Versatility: Having a versatile team in place to handle the "uncertainty of an uncertainty" can correct a wayward trial. In the case of Cytel, such project teams consist of data managers, statisticians, medical writers, and statistical programmers. After making a quick assessment of key problem areas, the team jumps into action with recommendations that span training and communication, as well as how to update and streamline procedures with minimal effect on trial sites and clinical design. Despite the variety of rescue situations, Cytel believes in a fundamental paradigm that focuses on high-level values (efficiencies across four parameters) which are then broken into several basic functionalities like developing a clear charter and creating statistical analysis plans. Regulatory The other aspect of rescuing a study is the regulatory one. Sometimes there might be an added challenge to convincing regulators that a study has reestablished scientific rigor. In one example, Cytel's client needed to explain biomarker discrepancies to regulators within 30 days. This demand required a rescue team to ‘parachute’ into the process to correct perceived problems. It shows the vital element of keeping a regulatory objective in mind throughout the rescue process. Statistical expertise is key to demonstrate scientific rigor. In such cases, it is often beneficial to have a project team, to handle unexpected data complications, and to organize data collected in a manner that reflects its real potential.
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