Multidimensional Thresholding for Individual-Level Preference Elicitation.

OBJECTIVES: Multiple methods are available for collecting health preference information. However, information on the design and analysis of novel methods is limited. This article aims to provide the first introduction into the design and analysis of multidimensional thresholding (MDT). METHODS: We introduce MDT as a 2-step approach: First, participants rank the largest possible improvements in all considered attributes by their importance. Second, participants complete a series of systematically combined trade-off questions. Hit-and-Run sampling is used for obtaining preference weights. We also use a computational experiment to compare different MDT designs. RESULTS: The outlined MDT can generate preference information suitable for specifying a multiattribute utility function at the individual level. The computational experiment demonstrates the method's ability to recover preference weights at a high level of precision. While all designs in the computation experiment perform comparably well on average, the design outlined in the paper stands out with a high level of precision even if differences in relative attribute importance are large. CONCLUSION: MDT is suitable for preference elicitation, in particular if sample sizes are small. Future research should help improve the methods (e.g., remove the need for an initial ranking) to increase the potential reach of MDT.

Two Methods, One Story? Comparing Results of a Choice Experiment and Multidimensional Thresholding From a Clinician Preference Study in Aneurysmal Subarachnoid Hemorrhage.

OBJECTIVES: An increasing number of methods are used to elicit health preference information. It is unclear whether different elicitation methods produce similar results and policy advice. Here, we compared the results from a discrete choice experiment (DCE) and multidimensional thresholding (MDT) that were conducted in the same sample. METHODS: Clinicians (N = 350) completed a DCE and MDT to elicit their preferences for 4 attributes related to the medical management of subarachnoid hemorrhage after aneurysm repair. Preference weights were compared between the DCE and MDT using a complete combinatorial convolution test. Additionally, data from the DCE and MDT were used to compute preference-based net treatment values for 16 hypothetical treatment profiles versus 1000 simulated comparators. The implied treatment recommendations were compared between the DCE and MDT. RESULTS: Preference weight distributions and median weights did not differ significantly between the DCE and MDT for any attribute: likelihood of delayed cerebral ischemia (medians 0.48 vs 0.40; P = .41), risk of lung complications (medians 0.27 vs 0.30; P = .52), risk of hypotension (medians 0.10 vs 0.11; P = .55), and risk of anemia (medians 0.07 vs 0.07; P = .50). The DCE and MDT produced similar treatment net value distributions (P > .05) and implied the same treatment recommendations in 82.3% of cases. CONCLUSIONS: The DCE and MDT elicited similar preference distributions and produced the same treatment recommendations for most tested cases. However, the share of people supporting the average treatment recommendation differed. More research is needed to determine how these findings would compare with those in other populations (in particular, patients) and applications.

The relevance of fatigue to relapse rate in multiple sclerosis: Applying patient preference data to the OPTIMUM trial.

BACKGROUND: In the OPTIMUM trial in patients with relapsing MS, treatment differences in annualized relapse rate (ARR, 0.088) and change in fatigue at week 108 (3.57 points, measured using the Fatigue Symptoms and Impacts Questionnaire-Relapsing Multiple Sclerosis, symptom domain (FSIQ-RMS-S)) favored ponesimod over teriflunomide. However, the importance of the fatigue outcome to patients was unclear. OBJECTIVE: To assess the importance of the OPTIMUM FSIQ-RMS-S results using data from an MS discrete choice experiment (DCE). METHODS: The DCE included components to correlate levels of physical and cognitive fatigue with FSIQ-RMS-S scores. Changes in relapses/year and time to MS progression equivalent to the treatment difference in fatigue in OPTIMUM were determined for similar fatigue levels as mean baseline fatigue in OPTIMUM. RESULTS: DCE participants would accept 0.06 more relapses/year or a 0.15-0.17 year decrease in time to MS progression for a 3.57-point difference in physical fatigue on the FSIQ-RMS-S. To improve cognitive fatigue by 3.57-points on the FSIQ-RMS-S, DCE participants would accept 0.09-0.10 more relapses/year or a 0.24-0.28 year decrease in time to MS progression. CONCLUSION: MS patients would accept 0.06 more relapses/year to change their fatigue by a similar magnitude as the between-treatment difference observed in the OPTIMUM trial.

Illustrating Emerging Good Practices for Quantitative Benefit-Risk Assessment: A Hypothetical Case Study of Systemic Biologic Treatments for Plaque Psoriasis.

OBJECTIVES: Quantitative benefit-risk assessment (qBRA) is a structured process to evaluate the benefit-risk balance of treatment options to support decision making. The ISPOR qBRA Task Force was recently established to provide recommendations for the design, conduct, and reporting of qBRA. This report presents a hypothetical case study illustrating how to apply the Task Force's recommendations toward a qBRA to inform the benefit-risk assessment of brodalumab at the time of initial marketing approval. The qBRA evaluated 2 dosing regimens of brodalumab (210 mg or 140 mg twice weekly) compared with weight-based dosing of ustekinumab and placebo. METHODS: We followed the 5 steps recommended by the Task Force. Attributes included treatment response (≥75% improvement in Psoriasis Area and Severity Index), suicidal ideation and behavior, and infections. Performance data were drawn from pivotal clinical trials of brodalumab. The qBRA used multicriteria decision analysis and preference weights from a hypothetical discrete choice experiment. Sensitivity analyses examined the robustness of benefit-risk ranking to uncertainty in clinical effect and preference estimates, consideration of a subgroup (nail psoriasis), and the maintenance phase of treatment (52 weeks instead of 12). RESULTS: Results from this hypothetical qBRA suggest that brodalumab 210 mg had a more favorable benefit-risk profile compared with ustekinumab and placebo. Ranking of brodalumab compared with ustekinumab was dependent on brodalumab's dose. Sensitivity analyses demonstrated robustness of benefit-risk ranking to uncertainty in clinical effect and preference estimates, as well as choice of attributes and length of follow-up. CONCLUSION: This case study demonstrates how to implement the ISPOR Task Force's good practice recommendations on qBRA.

Current Health State Affected Patient Preferences More Than Disease Status: A Discrete Choice Experiment in Multiple Myeloma.

OBJECTIVES: To examine how disease status and current health state influence treatment preferences of patients with multiple myeloma (MM). METHODS: Participants with MM from France, Germany, and the United Kingdom completed a web-based survey that included a discrete choice experiment (DCE) and EQ-5D assessment. The DCE elicited preferences for 8 attributes: increased life expectancy, increased time to relapse, pain, fatigue, risk of infection, administration (route and duration), frequency of administration, and monitoring. Multinomial logit models were used to analyze DCE preference data and to calculate life expectancy trade-offs. RESULTS: Three hundred participants with MM (newly diagnosed, transplant eligible, n = 108; newly diagnosed, transplant ineligible, n = 105; relapsed-refractory, n = 87) completed the survey. The most valued attributes were pain, fatigue, and increased life expectancy. Participants would want an additional 2.7 years of life expectancy (95% confidence interval [CI] 2.4-3.1 years) to tolerate extreme pain and an additional 2.0 years of life expectancy (95% CI 1.6-2.3 years) to tolerate constant fatigue. Participants in a better health state (third EQ-5D score quartile [0.897]) required less additional life expectancy than participants with a worse health state (first EQ-5D score quartile [0.662]) to tolerate extreme pain (2.3 years [95% CI 1.9-2.6 years] vs 3.0 years [95% CI 2.6-3.4 years]; P = .007). There was little difference in treatment preferences between newly diagnosed and relapsed-refractory patients for pain, fatigue, and increased life expectancy. CONCLUSIONS: Current health state influenced treatment preferences of patients with MM more than disease status and should be considered when making treatment decisions.

Quantitative Benefit-Risk Assessment in Medical Product Decision Making: A Good Practices Report of an ISPOR Task Force.

Benefit-risk assessment is commonly conducted by drug and medical device developers and regulators, to evaluate and communicate issues around benefit-risk balance of medical products. Quantitative benefit-risk assessment (qBRA) is a set of techniques that incorporate explicit outcome weighting within a formal analysis to evaluate the benefit-risk balance. This report describes emerging good practices for the 5 main steps of developing qBRAs based on the multicriteria decision analysis process. First, research question formulation needs to identify the needs of decision makers and requirements for preference data and specify the role of external experts. Second, the formal analysis model should be developed by selecting benefit and safety endpoints while eliminating double counting and considering attribute value dependence. Third, preference elicitation method needs to be chosen, attributes framed appropriately within the elicitation instrument, and quality of the data should be evaluated. Fourth, analysis may need to normalize the preference weights, base-case and sensitivity analyses should be conducted, and the effect of preference heterogeneity analyzed. Finally, results should be communicated efficiently to decision makers and other stakeholders. In addition to detailed recommendations, we provide a checklist for reporting qBRAs developed through a Delphi process conducted with 34 experts.

A simulated maximum likelihood procedure for analyzing imprecise trade-off thresholds between the benefits and harms of medicines.

Stated preference studies in which information on the willingness to trade-off between the benefits and harms of medicines is elicited from patients or other stakeholders are becoming increasingly mainstream. Such trade-offs can mathematically be represented by a weighted additive function, with the weights, whose ratios determine how much an individual is willing to trade-off between the treatment attributes, being the response vector for the statistical analysis. One way of eliciting trade-off information is through multi-dimensional thresholding (MDT), which is a bisection-based approach that results in increasingly tight bounds on the values of the weights ratios. While MDT is cognitively less demanding than other, more direct elicitation methods, its use complicates the statistical analysis as it results in weights data that are region censored. In this article, we present a simulated maximum likelihood (SML) procedure for fitting a Dirichlet population model directly to the region-censored weights data and perform a series of computational experiments to compare the proposed SML procedure to a naive approach in which a Dirichlet distribution is fitted to the centroids of the weights boundaries obtained with MDT. The results indicate that the SML procedure consistently outperformed the centroid-based approach, with the centroid-based approach requiring three bisection steps per trade-off to achieve a similar precision as the SML procedure with one bisection step per trade-off. Using the newly proposed SML procedure, MDT can be applied with smaller sample sizes or with fewer questions compared to the more naïve centroid-based approach that was applied in previous applications of MDT.

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.

Maintenance inhaler therapy preferences of patients with asthma or chronic obstructive pulmonary disease: a discrete choice experiment.

BACKGROUND: A variety of maintenance inhaler therapies are available to treat asthma and COPD. Patient-centric treatment choices require understanding patient preferences for the alternative therapies. METHODS: A self-completed web-based discrete choice experiment was conducted to elicit patient preferences for inhaler device and medication attributes. Selection of attributes was informed by patient focus groups and literature review. RESULTS: The discrete choice experiment was completed by 810 patients with asthma and 1147 patients with COPD. Patients with asthma most valued decreasing the onset of action from 30 to 5 min, followed by reducing yearly exacerbations from 3 to 1. Patients with COPD most and equally valued decreasing the onset of action from 30 to 5 min and reducing yearly exacerbations from 3 to 1. Both patients with asthma and patients with COPD were willing to accept an additional exacerbation in exchange for a 15 min decrease in onset of action and a longer onset of action in exchange for a lower risk of adverse effects from inhaled corticosteroids. Patients with asthma and COPD valued once-daily over twice-daily dosing, pressurised inhalers over dry powder inhalers and non-capsule priming over single-use capsules, although these attributes were not valued as highly as faster onset of action or reduced exacerbations. CONCLUSIONS: The most important maintenance inhaler attributes for patients with asthma and COPD were fast onset of symptom relief and a lower rate of exacerbations. Concerns about safety of inhaled corticosteroids and device convenience also affected patient preferences but were less important.

Reporting Quality of Marginal Rates of Substitution in Discrete Choice Experiments That Elicit Patient Preferences.

BACKGROUND: Discrete choice experiments (DCEs) are commonly used to elicit patient preferences as marginal rates of substitution (MRSs) between treatment or health service attributes. Because these studies are increasing in importance, it is vital that uncertainty around MRS estimates is reported. OBJECTIVE: To review recently published DCE studies that elicit patient preferences in relation to MRS reporting and to explore the accuracy of using other reported information to estimate the uncertainty of the MRSs. METHODS: A systematic literature review of DCEs conducted with patients between 2014 and July 2019 was performed. The number of studies reporting coefficients, MRSs, standard errors (SEs), and confidence intervals was recorded. If all information was reported, studies were included in an analysis to determine the impact of estimating the SEs of MRSs using coefficients and assuming zero covariance, to determine the impact of this assumption. RESULTS: Two hundred and thirty-two patient DCEs were identified in the review; 34.1% (n = 79) reported 1 or more MRS and, of these, only 62.0% (n = 49) provided an estimate of the uncertainty. Of these studies, 16 contained enough information for inclusion in the analysis, providing 116 datapoints. Actual SEs were smaller than estimated SEs in 75.0% of cases (n = 87), and estimated SEs were within 25% of the actual SE in 59.5% of cases (n = 69). CONCLUSION: Uncertainty of MRS estimates is unreported in a substantial proportion of recently published DCE studies. Estimating the SE of a MRS by solely using the SEs of the utility coefficients is likely to lead to biased estimates of the precision of patient trade-offs.

Personalized benefit-risk assessments combining clinical trial and real-world data provide further insights into which patients may benefit most from therapy: Demonstration for a new oral antiplatelet therapy.

PURPOSE: Quantitative benefit-risk (B-R) assessments are used to characterize treatment by combining key benefits and risks into a single metric but have historically been done for the "average" patient. Our aim was to conduct an individualized assessment for the oral antiplatelet vorapaxar by combining trial and real-world data to further personalize the treatment profiles. METHODS: Using linked UK health care databases, we developed risk prediction equations for key ischemic and bleeding events using Cox proportional hazards models. Trial hazard ratios, relative to placebo, were applied to baseline risk estimates to compute expected attributable risks, summed to derive a per-patient net clinical benefit (NCB). High risk subgroups were defined a priori, and Gaussian mixture models (GMM) were fit to characterize the NCB distribution and identify subgroups with similar NCBs. RESULTS: NCB was consistently positive for all subgroups, likely due to the outcome correlation, and would remain positive with a 12-fold increase in bleeding risk. GMMs identified three distinct NCB subgroups. Compared with the middle/lower NCB subgroups, those with a higher NCB tended to be older, female, and have higher CV disease burden. CONCLUSIONS: Personalized B-R assessments are feasible and clinically valuable and can be used to better predict who would benefit most from therapy.

The Use of MCDA in HTA: Great Potential, but More Effort Needed.

The potential for multi-criteria decision analysis (MCDA) to support health technology assessment (HTA) has been much discussed, and various HTA agencies are piloting or applying MCDA. Alongside these developments, good practice guidelines for the application of MCDA in health care have been developed. An assessment of current applications of MCDA to HTA in light of good practice guidelines reveals, however, that many have methodologic flaws that undermine their usefulness. Three challenges are considered: the use of additive models, a lack of connection between criteria scales and weights, and the use of MCDA in economic evaluation. More attention needs to be paid to MCDA good practice by researchers, journal editors, and decision makers and further methodologic developments are required if MCDA is to achieve its potential to support HTA.

Assessing Rationality in Discrete Choice Experiments in Health: An Investigation into the Use of Dominance Tests.

BACKGROUND: Dominance tests are often applied to test for the rationality in the choice behavior of participants in discrete choice experiments (DCEs). OBJECTIVES: To examine how dominance tests have been implemented in recent DCE applications in health and discuss their theoretical and empirical interpretation. METHODS: Health-related DCEs published in 2015 were reviewed for the inclusion of tests on choice behavior. For studies that implemented a dominance test, information on application and interpretation of the test was extracted. Authors were contacted for test choice sets and observed proportions of subjects who chose the dominated option. Coefficients corresponding to the choice set were extracted to estimate the expected probability of choosing the dominated option with a logistic model and compared with the observed proportion. The theoretical range of expected probabilities of possible dominance tests was calculated. RESULTS: Of 112 health-related DCEs, 49% included at least one test for choice behavior; 28 studies (25%) included a dominance test. The proportion of subjects in each study who chose the dominated option ranged from 0% to 21%. In 46% of the studies, the dominance test led to the exclusion of participants. In the 15 choice sets that were analyzed, 2 had larger proportions of participants choosing the dominated option than expected (P < 0.05). CONCLUSIONS: Although dominance tests are frequently applied in DCEs, there is no consensus on how to account for them in data analysis and interpretation. Comparison of expected and observed proportions of participants failing the test might be indicative of DCE quality.

Comparison of Oral Anticoagulants for Stroke Prevention in Nonvalvular Atrial Fibrillation: A Multicriteria Decision Analysis.

BACKGROUND: Decision on the most appropriate oral anticoagulation therapy for stroke prevention in patients with nonvalvular atrial fibrillation is difficult because multiple treatment options are available, and these vary in their clinical effects and relevant nonclinical characteristics. OBJECTIVES: To use a multicriteria decision analysis (MCDA) to compare the oral anticoagulants apixaban, dabigatran, edoxaban, rivaroxaban, and vitamin K antagonist (VKAs; specifically warfarin) in patients with nonvalvular atrial fibrillation. METHODS: We identified the evaluation criteria through a targeted literature review and clinical judgment. The final evaluation model included nine clinical events and four other criteria. We ranked possibly fatal clinical event criteria on the basis of the differences in risks of fatal events and the corresponding window of therapeutic opportunity, as observed in clinical trials. Clinical judgment was used to rank other criteria. Full criteria ranking was used to calculate centroid weights, which were combined with individual treatment performances to estimate the overall value score for each treatment. RESULTS: Using such an MCDA, dabigatran yielded the highest overall value, approximately 6% higher than that of the second-best treatment, apixaban. Dabigatran also had the highest first-rank probability (0.72) in the probabilistic sensitivity analysis. Rivaroxaban performed worse than the other non-VKA oral anticoagulants, but better than VKAs (with both having 0.00 first-rank probability). The results were insensitive to changes in model structure. CONCLUSIONS: When all key oral anticoagulant value criteria and their relative importance are investigated in an MCDA, dabigatran appears to rank the highest and warfarin the lowest.

MCDA swing weighting and discrete choice experiments for elicitation of patient benefit-risk preferences: a critical assessment.

PURPOSE: Multiple criteria decision analysis swing weighting (SW) and discrete choice experiments (DCE) are appropriate methods for capturing patient preferences on treatment benefit-risk trade-offs. This paper presents a qualitative comparison of the 2 methods. METHODS: We review and critically assess similarities and differences of SW and DCE based on 6 aspects: comprehension by study participants, cognitive biases, sample representativeness, ability to capture heterogeneity in preferences, reliability and validity, and robustness of the results. RESULTS: The SW choice task can be more difficult, but the workshop context in which SW is conducted may provide more support to patients who are unfamiliar with the end points being evaluated or who have cognitive impairments. Both methods are similarly prone to a number of biases associated with preference elicitation, and DCE is prone to simplifying heuristics, which limits its application with large number of attributes. The low cost per patient of the DCE means that it can be better at achieving a representative sample, though SW does not require such large sample sizes due to exact nature of the collected preference data. This also means that internal validity is automatically enforced with SW, while the internal validity of DCE results needs to be assessed manually. CONCLUSIONS: Choice between the 2 methods depends on characteristics of the benefit-risk assessment, especially on how difficult the trade-offs are for the patients to make and how many patients are available. Although there exist some empirical studies on many of the evaluation aspects, critical evidence gaps remain.

A multi-criteria inference approach for anti-desertification management.

We propose an approach for classifying land zones into categories indicating their resilience against desertification. Environmental management support is provided by a multi-criteria inference method that derives a set of value functions compatible with the given classification examples, and applies them to define, for the rest of the zones, their possible classes. In addition, a representative value function is inferred to explain the relative importance of the criteria to the stakeholders. We use the approach for classifying 28 administrative regions of the Khorasan Razavi province in Iran into three equilibrium classes: collapsed, transition, and sustainable zones. The model is parameterized with enhanced vegetation index measurements from 2005 to 2012, and 7 other natural and anthropogenic indicators for the status of the region in 2012. Results indicate that grazing density and land use changes are the main anthropogenic factors affecting desertification in Khorasan Razavi. The inference procedure suggests that the classification model is underdetermined in terms of attributes, but the approach itself is promising for supporting the management of anti-desertification efforts.

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.

Current Practices and Challenges When Submitting Patient Experience Data for Regulatory Decisions by the US Food and Drug Administration: An Industry Survey.

OBJECTIVES: To understand industry practices and challenges when submitting patient experience data (PED) for regulatory decisions by the US Food and Drug Administration (FDA). METHODS: A two-part online survey related to collection, submission, and use of PED by FDA in regulatory decision-making (part 1) and a best-worst exercise for prioritizing potential PED initiatives (part 2) was completed by industry and contract research organization (CRO) members with ≥ 2 years of recent experience with patient-reported outcome (PRO), natural history study (NHS), or patient preference (PP) data; and direct experience with FDA filings including PED. RESULTS: A total of 50 eligible respondents (84% industry) completed part 1 of the survey, among which 46 completed part 2. Respondents mostly had PRO (86%) and PP (50%) experience. All indicated that FDA meetings should have a standing agenda item to discuss PED. Most (78%) reported meetings should occur before pivotal trials. A common challenge was justifying inclusion without knowing if and how data will be used. Most agreed that FDA and industry should co-develop the PED table in the FDA clinical review (74%), and the table should report reason(s) for not using PED (96%) in regulatory decision-making. Most important efforts to advance PED use in decision-making were a dedicated meeting pathway and expanded FDA guidance (51% each). CONCLUSIONS: FDA has policy targets expanding PED use, but challenges remain regarding pathways for PED submission and transparency in regulatory decision-making. Alignment on the use of existing meeting opportunities to discuss PED, co-development of the PED table, and expanded guidance are encouraged.

Preferences for Anaemia Treatment Attributes among Patients with Non-Dialysis-Dependent Chronic Kidney Disease.

INTRODUCTION: Information about patient preferences for the treatment of anaemia associated with chronic kidney disease (CKD) is scarce. Hence, our aim was to examine how patients with non-dialysis-dependent CKD valued attributes of alternative hypothetical anaemia treatments. METHODS: A discrete choice experiment (DCE) was conducted in adult patients who reported a clinical diagnosis of CKD-related anaemia. Treatment attributes included mode and frequency of administration, need for iron supplementation, risk of gastrointestinal side effects, risk of major cardiovascular events and impact on energy levels (as defined by the vitality section of the SF-6D health index). Logit models were used to analyse patients' preferences. RESULTS: The DCE was completed by 200 patients in four countries. Patients preferred an oral mode of administration. Patients were willing to tolerate a 5.1% (95% CI 2.0-8.3%) increase in the risk of a major cardiovascular event and an 11.7% (95% CI 5.0-18.5%) increase in the risk of gastrointestinal side effects to switch from an at-home subcutaneous injection administered once every 2 weeks to an at-home oral pill administered three times a week. Patients were willing to tolerate a 20.3% (95% CI 15.0-25.6%) increase in the risk of gastrointestinal side effects and an 8.9% (95% CI 6.1-11.7%) increase in the risk of a major cardiovascular event to transition from 'Sometimes having a lot of energy' to 'Always having a lot of energy'. CONCLUSIONS: Patients with non-dialysis-dependent CKD-related anaemia demonstrated clear treatment preferences and were willing to accept increased gastrointestinal or cardiovascular risks in exchange for more energy or an oral treatment.