The new article pays additional attention to the specific practical considerations pertaining to the use of patient preference information.
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The Food and Drug Administration (FDA or the Agency), the US regulating authority in the sphere of healthcare products, has published a guidance document dedicated to incorporating voluntary patient preference information over the Total Product Life Cycle (TPLC). The guidance provides a general overview of the applicable regulatory requirements, as well as additional clarifications and recommendations to be taken into consideration by medical device manufacturers and other parties involved in order to ensure compliance.
At the same time, provisions of the guidance are non-binding in their legal nature, nor are they intended to introduce new rules or impose new obligations. The authority explicitly states that an alternative approach could be applied, provided such an approach is in line with the existing regulatory framework and has been agreed with the authority in advance.
Introduction to Valid Scientific Evidence and Patient Preference Studies
Under the general rule, the FDA relies on valid scientific evidence, including Patient Preference Information (PPI), to assess whether a medical device is safe and effective. In the context of quantitative PPI studies, the Agency evaluates several key study qualities to ensure the data can contribute to meaningful decision-making. These qualities help the FDA determine whether the study can accurately reflect patient preferences regarding the benefits and risks of medical devices.
Patient-Centeredness
According to the guidance, patient preference studies should prioritize the patient’s perspective, ensuring that the focus is on measuring patient preferences regarding benefits and risks rather than those of healthcare professionals. A patient-centered approach requires that patients involved in the study are well-informed about their condition and treatment options.
In some cases, it may be appropriate to consider the preferences of care-partners or healthcare professionals, but the central focus should remain on the patient.
Relevance to Patients
As further explained by the authority, in order to effectively capture patient preferences, studies must cover critical aspects of benefits, risks, and uncertainties relevant to the clinical context. These elements should align with endpoints and outcomes measured in clinical studies to maintain consistency and relevance.
For example, when clinical endpoints involve surrogate biomarkers like Hemoglobin A1c for diabetic patients, the study should help patients understand how changes in such biomarkers correspond to more serious outcomes. Ensuring that patients comprehend the importance of clinical parameters is crucial for eliciting accurate preferences.
Methods for Eliciting Patient Preferences
There are both qualitative and quantitative methods for collecting PPI, each suitable for different points in the product life cycle and research objectives. Quantitative methods often involve structured surveys, such as discrete choice experiments or best-worst scaling, which can quantify patient preferences.
In contrast, qualitative methods, like one-on-one interviews, focus groups, and Delphi panels, provide descriptive insights into patient experiences and can help inform the design of clinical trials and device features early in the product life cycle. For quantitative surveys, sponsors must provide a detailed protocol outlining the methodology and ensuring the investigation is scientifically sound.
When using qualitative methods, it’s important to follow good research practices to generate valid scientific evidence.
Representative Study Population
To ensure that PPI study results can be generalized to the broader population, the study population must be representative of the full spectrum of patients for whom the device is intended. This requires careful consideration of the study’s enrollment criteria and recruitment methods.
If patient preferences vary across subgroups (e.g., based on disease stage or demographic factors), the study should include enough participants from each subgroup to ensure meaningful analysis. It is also important to note that small sample sizes may limit the generalizability of the results, as they may not capture the full range of patient preferences.
Sponsors should aim to recruit a sufficiently large and diverse patient population to support reliable and representative findings.
Reflecting Heterogeneity of Patient Preferences
Patients with the same condition may have widely differing preferences based on their unique circumstances, such as age, gender, socioeconomic status, disease severity, and available treatment options. Therefore, PPI studies should account for this heterogeneity, ensuring that the full spectrum of patient preferences is represented.
This can help identify subgroups of patients who may be more inclined to accept specific risks in exchange for benefits, which can be important in the FDA’s benefit-risk assessments. Quantitative methods can help capture this preference variability and estimate the size of subgroups within the overall patient population.
This information is useful for tailoring device assessments to meet the needs of different patient groups.
Selection of Attributes and Attribute Levels
The attributes selected for a PPI study should be relevant to both the FDA’s decision-making process and the patients themselves. Sponsors are encouraged to engage with the FDA early in the study design process to determine which attributes are most critical.
Attributes that are not relevant to the decision at hand can distort the results and reduce the value of the study for regulatory purposes. Additionally, the levels of each attribute (e.g., risk percentages or treatment outcomes) should be well-defined, clinically relevant, and presented in a way that patients can easily understand.
Attributes should be distinct and non-overlapping to ensure clarity, and risk levels should reflect real-world clinical outcomes. Pre-testing attribute descriptions with the target patient population can help ensure they are appropriate and understandable.
Effective Communication of Benefit, Risk, and Uncertainty
The authority acknowledges that communicating health-related information, particularly risks and benefits, can be challenging due to varying levels of health numeracy among patients. PPI studies must present quantitative information in ways that patients can easily understand.
Formats for presenting information include natural frequencies (e.g., “20 in 1000 people”), percentages, verbal descriptors, and pictographs. Using multiple formats together can improve patient comprehension.
It is essential to avoid cognitive biases in how information is presented. For instance, using both positive and negative frames (e.g., “20% chance of adverse events” vs. “80% chance of no adverse events”) can reduce bias in patient responses. Pre-testing communication methods can further ensure that patients fully understand the benefit-risk tradeoffs being presented.
Study Comprehension and Minimizing Cognitive Bias
Ensuring that patients fully comprehend the medical information presented in a PPI study is critical to obtaining valid results. Sponsors should include comprehension assessments within the study to verify that participants understand the information as intended.
Additionally, efforts should be made to minimize cognitive biases, such as framing effects or ordering effects, which could influence patients’ responses. For example, presenting the same information in different ways (e.g., focusing on survival rates versus mortality rates) can lead to different responses.
PPI studies should aim to reduce such biases by carefully designing survey questions and response formats.
Logical Soundness and Data Quality
According to the document, a well-designed PPI study should include internal validity tests to ensure that the data collected is logical and consistent. Data quality checks, such as consistency tests and assessments of recoding effects, help ensure that study results are reliable.
Sponsors should describe these quality checks in their study protocols and use them to inform the analysis and interpretation of study results.
Robustness of Analysis
As it is stated by the authority, Statistical analysis plays a key role in interpreting PPI study results. It is important to account for sources of uncertainty in the analysis and to report estimates with confidence intervals or standard errors.
Sensitivity analysis can help determine how changes in specific parameters affect the study’s conclusions, which is crucial for robust decision-making.
Study Conduct and Participant Compliance
The validity of PPI studies depends on strict adherence to the study protocol by both research staff and participants. Research staff should be trained to ensure proper administration of the study, and patients should receive tutorials and quizzes to confirm their understanding of the study’s questions.
Documenting quiz results can provide evidence of patients’ comprehension, supporting the validity of the study’s findings.
Adherence to Good Research Practices
Finally, adherence to established good research practices, as recommended by recognized health preference research organizations such as the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), enhances the credibility of PPI studies.
Following these guidelines ensures that the study design, conduct, and analysis meet high scientific standards.
Conclusion
In summary, the present draft guidance outlines the FDA’s recommendations and practical considerations for conducting patient preference studies. It highlights the importance of patient-centeredness, relevance, methodological rigor, and effective communication in capturing meaningful patient preferences to support FDA device evaluations.
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