Using Patient Preferences in Health Technology Assessment: Evaluating Quality-Adjusted Survival Equivalents (QASE) for the Quantification of Non-health Benefits.

Interest in using patient preference (PP) data alongside traditional economic models in health technology assessment (HTA) is growing, including using PP data to quantify non-health benefits. However, this is limited by a lack of standardised methods. In this article, we describe a method for using discrete choice experiment (DCE) data to estimate the value of non-health benefits in terms of quality-adjusted survival equivalence (QASE), which is consistent with the concept of value prevalent among HTA agencies. We describe how PP data can be used to estimate QASE, assess the ability to test the face-validity of QASE estimates of changes in mode of administration calculated from five published DCE oncology studies and review the methodological and normative considerations associated with using QASE to support HTA. We conclude that QASE may have some methodological advantages over alternative methods, but this requires DCEs to estimate second-order effects between length and quality of life. In addition, empirical work has yet to be undertaken to substantiate this advantage and demonstrate the validity of QASE. Further work is also required to align QASE with normative objectives of HTA agencies. Estimating QASE would also have implications for the conduct of DCEs, including standardising and defining more clear attribute definitions.

Treatment preferences of adults and adolescents with alopecia areata: A discrete choice experiment.

PRODUCTS with janus kinase (JAK) inhibition have been shown to promote hair regrowth in patients with alopecia areata (AA). To guide drug-approval and treatment decisions, it is important to understand patients' willingness to accept the potential risks of JAK inhibition in exchange for potential benefits. We quantified the treatment preferences of adult (≥18 years) and adolescent patients (12-17 years) with AA in the US and Europe to determine the trade-offs they are willing to make between benefits and risks. Preferences for oral AA treatment attributes were elicited using a discrete choice experiment consisting of 12 tasks in which patients chose between two hypothetical treatment alternatives and no treatment. Benefits included the probability of 80%-100% scalp hair regrowth (Severity of Alopecia Tool score ≤ 20) and achieving moderate-to-normal eyebrow and eyelash hair. Treatment-related risks included 3-year probabilities of serious infection, cancer, and blood clots. Preference estimates were used to calculate the maximum level of each risk that patients were willing to accept for increases in treatment benefits. The most important attribute to both adults (n = 201) and adolescents (n = 120) was a 50% probability of achieving hair regrowth on most or all the scalp; however, adolescents placed greater relative importance on this attribute than did adults. Adults were averse to the risks of serious infection, cancer, and blood clots, whereas adolescents were averse to the risk of cancer. For a 20% increase in the probability of 80%-100% scalp hair regrowth, adults were willing to accept a mean (95% confidence interval) 3-year risk of serious infection, cancer, and blood clots of 7.4% (5.5-9.3), 2.5% (1.9-3.1), and 9.3% (6.4-12.2). Adolescents were willing to accept a 3-year risk of cancer of 3.3% (2.4-4.2). Patients with AA in the US and Europe are willing to accept substantial risks to obtain an effective treatment.

Multimethod quantitative benefit-risk assessment of treatments for moderate-to-severe osteoarthritis.

OBJECTIVE: Demonstrate how benefit-risk profiles of systemic treatments for moderate-to-severe osteoarthritis (OA) can be compared using a quantitative approach accounting for patient preference. STUDY DESIGN AND SETTING: This study used a multimethod benefit-risk modelling approach to quantifiably compare treatments of moderate-to-severe OA. In total four treatments and placebo were compared. Comparisons were based on four attributes identified as most important to patients. Patient Global Assessment of Osteoarthritis was included as a favourable effect. Unfavourable effects, or risks, included opioid dependence, nonfatal myocardial infarction and rapidly progressive OA leading to total joint replacement. Clinical data from randomized clinical trials, a meta-analysis of opioid dependence and a long-term study of celecoxib were mapped into value functions and weighted with patient preferences from a discrete choice experiment. RESULTS: Lower-dose NGFi had the highest weighted net benefit-risk score (0.901), followed by higher-dose NGFi (0.889) and NSAIDs (0.852), and the lowest score was for opioids (0.762). Lower-dose NGFi was the highest-ranked treatment option even when assuming a low incidence (0.34% instead of 4.7%) of opioid dependence (ie, opioid benefit-risk score 808) and accounting for both the uncertainty in clinical effect estimates (first rank probability 46% vs 20% for NSAIDs) and imprecision in patient preference estimates (predicted choice probability 0.26, 95% confidence interval [CI] 0.25-0.28 vs 0.21, 95% CI 0.19-0.23 for NSAIDs). CONCLUSION: The multimethod approach to quantitative benefit-risk modelling allowed the interpretation of clinical data from the patient perspective while accounting for uncertainties in the clinical effect estimates and imprecision in patient preferences.

An overview of critical decision-points in the medical product lifecycle: Where to include patient preference information in the decision-making process?

BACKGROUND: Patient preference (PP) information is not effectively integrated in decision-making throughout the medical product lifecycle (MPLC), despite having the potential to improve patients' healthcare options. A first step requires an understanding of existing processes and decision-points to know how to incorporate PP information in order to improve patient-centric decision-making. OBJECTIVES: The aims were to: 1) identify the decision-making processes and decision-points throughout the MPLC for industry, regulatory authorities, and reimbursement/HTA, and 2) determine which decision-points can potentially include PP information. METHODS: A scoping literature review was conducted using five scientific databases. Semi-structured interviews were conducted with representatives from seven European countries and the US, including industry (n = 24), regulatory authorities (n = 23), reimbursement/HTA (n = 23). Finally, validation meetings with key stakeholders (n = 11) were conducted. RESULTS: Six critical decision-points were identified for industry decision-making, three for regulatory decision-making, and six for reimbursement/HTA decision-making. Stakeholder groups agreed that PP information is not systematically integrated, either as obligatory information or pre-set criteria, but would benefit all the listed decision-points in the future. CONCLUSION: Currently, PP information is not considered as obligatory information to submit for any of the MPLC decision-points. However, PP information is considered an important component by most stakeholders to inform future decision-making across the MPLC. The integration of PP information into 15 identified decision-points needs continued discussion and collaboration between stakeholders.