| September 06, 2022 | By
The United States Food and Drug Administration (FDA) is increasingly requesting the inclusion of patient preference information (PPI) to inform its regulatory decision-making regarding medical devices. A recently released report offers insights on opportunities to leverage the PPI, such as how to maximize stakeholder engagement, identifying new metrics, and statistical considerations.
The article, published in Therapeutic Innovation & Regulatory Science on August 27, examines recent advances in the use of PPIs for medical device development, including lessons learned by industry, regulators and other stakeholders from information on patient preferences from Medical Device Innovation Consortium (MDIC) in the Design of Clinical Trials (PPI-CT) framework.
MDIC is a public-private partnership between the FDA, industry, patient advocates, academia, and other stakeholders aimed at advancing the regulatory science behind the development of medical devices and diagnostics. The group has been influential over the past decade by working with the FDA to develop new regulations on the use of patient preference data for regulatory purposes.
In 2015, the MDIC launched several projects to investigate how PPIs could be used to develop new products, resulting in the PPI-CT framework. Based on the framework, the report authors highlighted some steps that proponents of PPIs, and in particular device manufacturers, should consider when integrating PPIs into clinical trials.
First, they recommend assessing the regulatory interest in the use of PPIs. The authors note that officials at the Center for Devices and Radiological Health (CDRH) have shown strong interest in the use of PPIs and have in recent years published guidelines on how to use this data in regulatory applications. The authors also note that its critical sponsors engage the agency early on when developing research plans.
“A key ingredient to the success of these efforts is early buy-in from critical stakeholders on the importance of ensuring that a clinical trial design reflects the trade-offs that patients are willing to accept based on the magnitude gains and decreases in health,” they said. “Engaging these stakeholders helps ensure that patient preference studies are designed and positioned to collect PPIs that best answer relevant research questions.”
The report also offers recommendations on how to identify new endpoints for PPIs in clinical studies.
“Parameters are most useful as attributes in a patient preference study when they can be translated into attributes that are understandable and meaningful to patients. ”, wrote the authors. “Once the attributes that matter to patients (including potentially different patient subgroups) have been identified through patient preference studies or other approaches, researchers can work with their clinical trial teams and regulators to appropriately incorporate them as endpoints in the clinical trial.”
They note that conducting a patient preference study early in the product development process is a key opportunity to help researchers develop new parameters that can be used to collect PPIs. At this early stage, researchers should use a “bottom-up” approach whereby they obtain feedback from patients about what is meaningful to them in terms of successfully treating their condition, rather than simply looking for traditional endpoints.
The authors go on to state that clinical attributes such as survival and pain, which are generally considered high priority for patients, can be used as primary or secondary clinical trial endpoints. However, these are not always the endpoints that patients prioritize. For example, factors such as independence are sometimes a higher priority for patients.
With this in mind, the study authors said stakeholders should consider aligning the study’s attributes of patient preference with traditional clinical trial endpoints.
“The identification of specific elements of a composite endpoint to define the attributes of the study of patient preferences could be discussed with regulators, with the aim of aligning an approach to assess and weight the measurable factors (often symptoms) that matter most to patients,” they said. . “It is important to note that studies of patient preferences generally do not define the entire sphere of relevant parameters; rather, they can suggest additional results or help prioritize results already identified as relevant.
The authors also considered how researchers can ensure that the PPI being assessed is applicable to the study population. A key part of this is ensuring that participants are recruited in a timely manner and can provide accurate reports. The authors acknowledge that identifying the right participants and getting them to provide accurate reports due to their condition can be difficult for multiple reasons. For these patients, the authors said it might be useful to use a “confirmed diagnosis” method, which includes engaging patients who have been referred by a doctor or whose electronic medical records verify the diagnosis of the disease. studied condition.
“Because obtaining confirmed diagnoses can be time-consuming and expensive, patient preference studies may also collect data from individuals who ‘self-report’ or self-identify as suffering from ‘a certain disease,’ the study authors added. “In these cases, researchers can look for secondary data (for example, the channel through which a patient was contacted, such as a network of patient organizations) and additional information from participants (for example, information about their symptoms or their treatments which may be unique to the relevant condition) which can be used to increase diagnostic confidence.
One of the most important areas that the MDIC has worked on in recent years is the development of new statistical tools to assess studies involving small patient populations. With medical devices in general, it is not always possible to conduct large clinical trials; this is especially true if these devices are being developed for a small patient population.
In these small studies, the authors note that the Bayesian decision analysis (BDA framework developed by researchers at the Massachusetts Institute of Technology (MIT) could be used to define the statistical significance level of a clinical trial in a “systematic” approach. , quantitative, patient-centered and transparent”.
“The methodology attempts to balance the consequences of endorsing an ineffective and possibly harmful treatment (false endorsement) against the consequences of rejecting an effective treatment (false rejection) such that the overall expected utility of a clinical trial is maximized,” they said. “In addition to incorporating PPIs related to patient risk tolerance, the BDA framework can also analyze trade-offs related to patients’ time preferences (e.g., how long would patients be willing to wait for a new device? ). This framework could be particularly useful in diseases for which recruiting clinical trials may be difficult, such as rare diseases or conditions with a high mortality rate.
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