Pharmaceutical manufacturers, medical device companies, and advanced diagnostic manufacturers are increasingly incorporating patients’ and providers’ perspectives and preferences into the clinical development process to deliver products with high levels of acceptability and utility.
On August 24, 2016, the U.S. Food and Drug Administration (FDA) specifically published a guidance document for medical device approvals stating that patient input in the form of preference studies is important to consider during the FDA’s decision making for these products. Companies that develop products to address patient and provider perspectives in the product development stage are more likely to deliver products that satisfy important unmet needs in the complex and dynamic healthcare market, which increases the odds of product success. Thus, insights from these studies are often part of the FDA premarket approval submissions as evidence of product demand and clinical utility.
Well-designed healthcare preference studies can answer essential questions for innovators planning to launch successful products such as:
- Which attributes are most important for patients and physicians and by how much? What is the degree of alignment between these stakeholders regarding attribute importance?
- What are the minimum thresholds of product characteristics for viability in the marketplace (helps manufacturers make important decisions about product viability at the clinical trial stage)?
- What is the maximum acceptable risk on select product attributes (helps manufacturers make important decisions about product viability at the clinical trial stage)?
- What is the impact of the advanced diagnostic test on physician decision making (demonstrates clinical utility of the product)?
Discrete choice experiments and conjoint analysis are the most common methods of preference elicitation, mainly stated preferences, which is a survey-based technique to establish how much stakeholders value something. In a discrete choice experiment for a medical device [for example, a device to detect peripheral artery disease (PAD)], respondents are shown two or more profiles of products including several attributes (e.g., device result, data integration, training, power supply, portability, and cost). Each attribute takes on multiple levels depending on the number of profiles. For example, the attribute “data integration” could take on a value of “manual” in one profile and “automated” in the other profile. Each profile thus created has different permutations and combinations of attribute levels. Respondents are then asked to choose the most desirable product from two or more profiles shown side by side. The analysis is conducted using a regression model assessing the impact of attribute levels on choice of product. The output of a DCE is a relative preference weight for each attribute which can be used to evaluate attribute importance, minimum thresholds for product viability, and maximum acceptable risk.
Generating evidence on clinical utility is crucial when bringing a diagnostic test to market. A decision impact study is a type of conjoint analysis study used to evaluate the impact of a diagnostic test on clinician decision making. In a decision impact study, clinicians are presented with multiple profiles, each representing a patient. Similar to a DCE, these profiles have patient attributes (e.g., test result, standard of care test result, age) and corresponding levels, based on which clinicians are asked whether or not they would make particular patient management decisions. Like a DCE, the analysis is conducted using a regression model which calculates a preference weight for each attribute, thereby evaluating the impact of test result on the management decision compared to the SOC test.
To conclude, preference studies have abundant value in product market access and commercialization of drugs, medical devices, and diagnostics. They can and should be conducted at several different timepoints in the product life cycle including discovery, clinical development, marketing authorization, product launch and reimbursement, and postmarketing. Information from preference studies can be used to demonstrate the superiority of a product in filling unmet patient needs and generate a value story for the product, which can have a positive impact on payer coverage. With the emergence of more and more drugs, medical devices, and diagnostics in the marketplace, preference studies have become a cornerstone of manufacturers’ evidence development strategy to bring their products to market.
Manasi Datar, B.Pharm, MS, PhD
Manasi Datar has nine years of experience in healthcare research including Health Economics and Outcomes Research, Product Market Access and Commercialization, Payer and Provider Research, and Patient Preference and Satisfaction.