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How Much Risk Will Patients Accept?

How Much Risk Will Patients Accept?
A pilot project focused on Parkinson's disease will use patient preference and risk tolerance to set statistical significance levels in clinical trial design. How will this impact the future of medical device trials?

A pilot project focused on Parkinson's disease will use patient preference and risk tolerance to set statistical significance levels in clinical trial design. How will this impact the future of medical device trials?

A first-of-its-kind research project is aiming to translate patients' risk tolerances and preferences into innovative clinical trial designs. That means that statistical significance levels could differ based on a disease's severity and patients' willingness to accept riskier treatments.

The collaboration between the Medical Device Innovation Consortium (MDIC), The Michael J. Fox Foundation for Parkinson's Research (MJFF), FDA, RTI Health Solutions, and the Massachusetts Institute of Technology (MIT) is starting with a pilot focused on Parkinson's disease. According to the MDIC release, the goal is to use patient preference information to determine clinical trial design significance levels--what MDIC termed "therapy-specific patient-values."

The effort is part of an industry-wide shift toward seeking and using patient input. FDA finalized guidance on how patient preference information can be used in regulatory decision making earlier this year; CDRH has a Patient Preference Initiative; and there have been various efforts to speed the regulatory process for breakthrough devices. 

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While patient preference has been used for retrospective trial analysis, this will be the first prospective use of patient preference.

Stephanie Christopher, program manager, patient centered benefit-risk outcome at MDIC, and Dawn Bardot, vice president of technology innovation at MDIC, explained in an e-mail to MD+DI that this project is turning the idea of patient centricity into a concrete reality. "This is one place along the total product lifecycle in which patient centricity can be incorporated," they wrote.

The research will revolve around a hypothetical medical device to treat Parkinson's disease. RTI Health Solutions will work first with a focus group of representatives from the MJFF Patient Council to learn about patient risk preferences, the balance of risks and benefits patients are willing to experience, and what would be most valuable to them, according to Christopher and Bardot. That knowledge will then be used to design a patient preference survey that will be sent to thousands of patients using the MJFF Fox Insight platform, they explained. In addition, a team from MIT will use Bayesian analysis to determine what the trial endpoints and p-value significance levels should be, based on the responses collected from patients.

"People with a disease like Parkinson's can be more than research subjects; we can be partners in the design and focus of clinical trials. We welcome this opportunity to work with MDIC, the FDA and other partners to create a mechanism for the patient voice to be heard," Anne Cohn Donnelly, co-chair of the MJFF Patient Council working group for this project, said in an MJFF blog post.

Will this change medical device trials as we know them? The project is an opportunity for MDIC to evaluate stakeholders' interest in these types of trials, Christopher and Bardot wrote. "At the end of the day, we want to create scientifically rigorous and reproducible methods for incorporating patient preferences into regulatory decision-making and across the medical device product development lifecycle," they added.

According to FDA's patient preference information guidance, any patient preference information used during the regulatory decision making process will be described in device product labeling. 

"By designing clinical trials that reflect patients' urgency and risk tolerance, scarce resources can be allocated more efficiently, bringing more therapies to more patients faster and cheaper," Christopher and Bardot told MD+DI.


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