Preference Elicitation Methods Research Articles

Putting the Choice in Choice Tasks: Incorporating Preference Elicitation Tasks in Health Preference Research.

Choice-based preference elicitation methods such as the discrete choice experiment (DCE) present hypothetical choices to respondents, with an expectation that these hypothetical choices accurately reflect a 'real world' health-related decision context and that consequently the choice data can be held to be a true representation of the respondent's health or treatment preferences. For this to be the case, careful consideration needs to be given to the format of the choice task in a choice experiment. The overarching aim of this paper is to highlight important aspects to consider when designing and 'setting up' the choice tasks to be presented to respondents in a DCE. This includes the importance of considering the potential impact of format (e.g. choice context, choice set presentation and size) as well as choice set content (e.g. labelled and unlabelled choice sets and inclusion of reference alternatives) and choice questions (stated choice versus additional questions designed to explore complete preference orders) on the preference estimates that are elicited from studies. We endeavoure to instil a holistic approach to choice task design that considers format alongside content, experimental design and analysis.

Increased farmer willingness to pay for quality cassava (Manihot esculenta Crantz) planting materials: evidence from experimental auctions in Cambodia and Lao PDR

AbstractVarietal turnover is a major mechanism for farmer adaptation to rapidly changing environmental and market conditions. Understanding factors related to varietal turnover is critical for safeguarding food security in the face of increasingly rapid changes to production contexts. Preference elicitation methods like experimental auctions reveal farmers’ willingness to pay (WTP) for seeds with different characteristics. We engaged farmers growing cassava (Manihot esculenta Crantz) across a wide geographic scope in Cambodia (n = 321, 4 provinces) and Lao PDR (n = 391, 5 provinces) in modified second-price experimental auctions to determine WTP for three cassava seed classes: farmer-produced planting stems of an undescribed variety, farmer-produced planting stems of an elite variety with lower susceptibility to Cassava Mosaic Disease (CMD), and virus-free tested planting stems of the same elite variety. In Lao PDR, mean auction bids for a bundle of 20 planting stems were 0.54, 0.92, and 1.40 USD for farmer, elite, and elite tested stems, while in Cambodia the analogous mean bids were 0.58, 0.77, and 1.16 USD, respectively. A significantly higher WTP associated with elite variety and tested stems in both countries was influenced by both geographic location (province) and individual socioeconomic farmer characteristics. In Lao PDR this included lower WTP of women, higher WTP of ethnic minorities, and, for improved stems, lower WTP of farmers with increased cassava growing experience. In Cambodia fewer socioeconomic variables affected WTP. Our results indicate significant increases in WTP for clean planting materials and new varieties, supporting market-based clean seed approaches as a component of regional control strategies for the CMD epidemic currently threatening a global agri-food value chain.

Patient preferences for active ulcerative colitis treatments and fecal microbiota transplantation.

Fecal microbiota transplantation (FMT) is a promising treatment for active ulcerative colitis (UC). Understanding patient preferences can identify treatment features that may impact treatment decisions, improve shared decision-making, and contribute to patient-centered care, which is especially important in the context of novel treatments like FMT. We aimed to quantify preferences for active UC treatments, specifically FMT and biologics, and identify patient characteristics associated with different preference patterns. This is a cross-sectional survey study. We administered a discrete choice experiment (DCE) survey to elicit preferences in a sample of Canadian adults with UC. DCE data were analyzed using a main-effects mixed logit model and used to predict uptake of hypothetical scenarios reflecting alternative combinations of treatment features. Latent class modeling identified heterogeneity in patient preference patterns. Participants' (n = 201) mean age was 47.1 years (SD: 14.5 years), 58% were female, and most (84%) had at least some post-secondary education. Almost half were willing to undergo FMT. When considering treatments for active UC, the most important attributes were chance of remission and severity of rare unknown side effects. All else equal, participants were most likely to uptake treatment that involves oral capsules/pills. Participants in the class with the highest utility for chance of remission were younger, had more severe disease, and 58% indicated that they would be willing to undergo FMT. We identified characteristics of UC patients who are more likely to be interested in FMT using preference elicitation methods. Patient-centered care can be enhanced by knowing which patients are more likely to be interested in FMT, potentially improving satisfaction with and adherence to treatments for active UC to maximize the effectiveness of treatment while considering heterogeneity in patient preferences.

Using the Online Elicitation of Personal Utility Functions Approach to Derive a Patient-Based 5-Level Version of EQ-5D Value Set: A Study in 122 Patients With Rheumatic Diseases From Germany

ObjectivesTraditional preference elicitation methods, such as discrete choice experiments or time trade-off, usually require large sample sizes. This can limit their applicability in patient populations, where recruiting enough participants can be challenging.The objective of this study was to test a new method, called the Online elicitation of Personal Utility Functions (OPUF) approach, to derive an EQ-5D-5L value set from a relatively small sample of patients with rheumatic diseases. MethodsOPUF is a new type of online survey that implements compositional preference elicitation techniques. Central to the method are 3 valuation steps: (1) dimension weighting, (2) level rating, and (3) anchoring. An English demo version of the OPUF survey can be accessed at https://valorem.health/eq5d5l. From the responses, a personal EQ-5D-5L utility function can be constructed for each participant, and a group-level value set can be derived by aggregating model coefficients across participants. ResultsA total of 122 patients with rheumatic disease from Germany completed the OPUF survey. The survey was generally well received; most participants completed the survey in less than 20 minutes and were able to derive a full EQ-5D-5L value set. The precision of mean coefficients was high, despite the small sample size. ConclusionsOur findings demonstrate that OPUF can be used to derive an EQ-5D-5L value set from a relatively small sample of patients. Although the method is still under development, we think that it has the potential to be a valuable preference elicitation tool and to complement traditional methods in several areas.

Patient Preference Studies in Hematologic Malignancies: A Systematic Review

Background: New therapies have brought about an unprecedented expansion of treatment options for patients with hematologic malignancies that require a greater understanding patient preferences, both to inform treatment decisions and direct further patient-focused drug development. We sought to synthesize existing literature evaluating the preferences of patients with hematologic malignancies to consolidate relevant knowledge and identify future directions for research. Methods: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO 2022 CRD42022364647). We searched for articles published in English from PubMed and Embase. Included articles reported primary data on the preferences of patients with hematologic malignancies or patient proxies. Review articles, articles utilizing only secondary data, and abstracts without full text were excluded. Two authors independently reviewed all abstracts and assessed studies for full-text eligibility. Conflicts were resolved by group discussion. Risk of bias (quality) and conflict of interest were assessed. Data extraction included population, method of preference elicitation, attributes included, and relative strength of preferences. Results: A total of 3,520 studies were identified, 2,960 abstracts were screened following removal of duplicates, 239 underwent full-text review, and 74 were included in data extraction. Among the 74 studies, 59 (80%) were published in the last 10 years. In total, 16,986 patients or patient proxies were represented across selected studies. Studies included patients with lymphoma (n=33, 45%), myeloma (n=25, 34%), acute myeloid (n=15, 20%) and lymphoid (n=12, 16%) leukemia, and chronic lymphoid (n=11, 15%) and myeloid leukemia (n=10, 14%). Most studies included patients from Europe (n=30), the United States (n=26), or Canada (n=10). Nearly all (n=70, 95%) included only adults. Most studies were cross-sectional (n=60, 81%), qualitative (n=13, 18%), or mixed-methods (n=13, 18%). Only 1 study was a prospective randomized trial. Preference elicitation methods included study-specific surveys (n=31, 42%), discrete choice experiments (n=28, 38%), and qualitative interviews (n=19, 26%). Most studies elicited patient preferences for treatment outcomes (n=50, 68%) followed by communication (n=14, 19%), and route of administration of therapies (n=7, 9%). Attributes included in treatment outcome studies varied but many included overall/progression free survival (n=30, 60%), side effects (n=28, 56%), route of administration (n=16, 32%), or cost (n=11, 22%). Strength of preference for outcomes was heterogeneous across studies though overall survival was frequently most important to patients. Most studies sought to inform clinical decisions (n=49, 66%), followed by policy (n=13, 18%) and regulatory decision-makers (n=6, 8%). Most studies were of high (n=44, 59%) or moderate (n=26, 35%) quality. Funding for the studies came from pharmaceutical companies (n=25, 34%), government grants (n=20, 27%), and foundations (n=18, 24%). Some studies had apparent (n=16, 22%) or likely (n=9, 12%) conflicts of interest. Only 4 studies (5%) reported implementation outcomes such as acceptability or feasibility. Conclusions: Numerous well-designed studies have been published describing the preferences of patients with hematologic malignancies. Most of these studies were cross-sectional and relied on direct elicitation or discrete choice experiments to elicit preferences. Treatment outcome preferences varied widely across studies and patient populations. This heterogeneity supports the essential role for shared decision-making and individualization of treatment recommendations for each patient. Although many studies sought to generate data to inform clinical decisions, studies evaluating how patient preference data inform individual treatment decisions are largely absent. Future work is needed to support the clinical implementation of preference measures and to standardize relevant attributes for individual diseases and populations.

A preference elicitation approach for the ordered weighted averaging criterion using solution choice observations

Decisions under uncertainty or with multiple objectives usually require the decision maker to formulate a preference regarding risks or trade-offs. If this preference is known, the ordered weighted averaging (OWA) criterion can be applied to aggregate scenarios or objectives into a single function. Formulating this preference, however, can be challenging, as we need to make explicit what is usually only implicit knowledge. We explore an optimization-based method of preference elicitation to identify appropriate OWA weights. We follow a data-driven approach, assuming the existence of observations, where the decision maker has chosen the preferred solution, but otherwise remains passive during the elicitation process. We then use these observations to determine the underlying preference by finding the preference vector that is at minimum distance to the polyhedra of feasible vectors for each of the observations. Using our optimization-based model, weights are determined by solving an alternating sequence of linear programs and standard OWA problems. Numerical experiments on risk-averse preference vectors for selection, assignment and knapsack problems show that our passive elicitation method compares well against having to conduct pairwise comparisons and performs particularly well when there are inconsistencies in the decision maker’s choices.

Impact of information and deliberation on the consistency of preferences for prioritization in health care – evidence from discrete choice experiments undertaken alongside citizens’ juries

BackgroundPublic preferences are an important consideration for priority-setting. Critics suggest preferences of the public who are potentially naïve to the issue under consideration may lead to sub-optimal decisions. We assessed the impact of information and deliberation via a Citizens’ Jury (CJ) or preference elicitation methods (Discrete Choice Experiment, DCE) on preferences for prioritizing access to bariatric surgery.MethodsPreferences for seven prioritization criteria (e.g. obesity level, obesity-related comorbidities) were elicited from three groups who completed a DCE: (i) participants from two CJs (n = 28); (ii) controls who did not participate in the jury (n = 21); (iii) population sample (n = 1,994). Participants in the jury and control groups completed the DCE pre- and post-jury. DCE data were analyzed using multinomial logit models to derive “priority weights” for criteria for access to surgery. The rank order of criteria was compared across groups, time points and CJ recommendations.ResultsThe extent to which the criteria were considered important were broadly consistent across groups and were similar to jury recommendations but with variation in the rank order. Preferences of jurors but not controls were more differentiated (that is, criteria were assigned a greater range of priority weights) after than before the jury. Juror preferences pre-jury were similar to that of the public but appeared to change during the course of the jury with greater priority given to a person with comorbidity. Conversely, controls appeared to give a lower priority to those with comorbidity and higher priority to treating very severe obesity after than before the jury.ConclusionBeing informed and undertaking deliberation had little impact on the criteria that were considered to be relevant for prioritizing access to bariatric surgery but may have a small impact on the relative importance of criteria. CJs may clarify underlying rationale but may not provide substantially different prioritization recommendations compared to a DCE.

Preferences in the Design and Delivery of Neurodevelopmental Follow-Up Care for Children: A Systematic Review of Discrete Choice Experiments.

Neurodevelopmental disorders are a significant cause of morbidity. Early detection of neurodevelopmental delay is essential for timely diagnosis and intervention, and it is therefore important to understand the preferences of parents and clinicians for engaging with neurodevelopmental surveillance and follow-up care. Discrete choice experiment (DCE) may be an appropriate method for quantifying these preferences. This review systematically examined how DCEs have been designed and delivered in studies examining neurodevelopmental care of children and identified the preferred attributes that have been reported. PubMed, Embase, CINAHL, and Scopus databases were systematically searched. Studies were included if they used DCE to elicit preferences for a neurodevelopmental follow-up program for children. Two independent reviewers conducted the title and abstract and full-text screening. Risk of bias was assessed using a DCE-specific checklist. Findings were presented using a narrative synthesis. A total of 6618 records were identified and 16 papers were included. Orthogonal (n=5) and efficient (n=5) experimental designs were common. There was inconsistent reporting of design-related features. Analysis was primarily completed using mixed logit (n=6) or multinomial logit (n=3) models. Several key attributes for neurodevelopmental follow-up care were identified including social, behavioral and emotional support, therapy, waiting time, and out-of-pocket costs. DCE has been successfully used as a preference elicitation method for neurodevelopmental-related care. There is scope for improvement in the design and analysis of DCE in this field. Nonetheless, attributes identified in these studies are likely to be important considerations in the design and implementation of programs for neurodevelopmental care.

Discrete choice experiment versus swing-weighting: A head-to-head comparison of diabetic patient preferences for glucose-monitoring devices.

Limited evidence exists for how patient preference elicitation methods compare directly. This study compares a discrete choice experiment (DCE) and swing-weighting (SW) by eliciting preferences for glucose-monitoring devices in a population of diabetes patients. A sample of Dutch adults with type 1 or 2 diabetes (n = 459) completed an online survey assessing their preferences for glucose-monitoring devices, consisting of both a DCE and a SW exercise. Half the sample completed the DCE first; the other half completed the SW first. For the DCE, the relative importance of the attributes of the devices was determined using a mixed-logit model. For the SW, the relative importance of the attributes was based on ranks and points allocated to the 'swing' from the worst to the best level of the attribute. The preference outcomes and self-reported response burden were directly compared between the two methods. Participants reported they perceived the DCE to be easier to understand and answer compared to the SW. Both methods revealed that cost and precision of the device were the most important attributes. However, the DCE had a 14.9-fold difference between the most and least important attribute, while the SW had a 1.4-fold difference. The weights derived from the SW were almost evenly distributed between all attributes. The DCE was better received by participants, and generated larger weight differences between each attribute level, making it the more informative method in our case study. This method comparison provides further evidence of the degree of method suitability and trustworthiness.

Eliciting preferences for cancer screening tests: Comparison of a discrete choice experiment and the threshold technique

ObjectiveTo compare results of three preference elicitation methods for a cancer screening test. MethodsParticipants (undergraduate students) completed a discrete choice experiment (DCE) and a threshold technique (TT) task. Accuracy (false positives, false negatives), benefits (lives saved), and cost for a cancer screening test were used as attributes in the DCE and branching logic for the TT. Participants were also asked a direct elicitation question regarding a hypothetical screening test for breast (women) or prostate (men) cancer without mortality benefit. Correlations assessed the relationship between DCE and TT thresholds. Thresholds were standardized and ranked for both methods to compare. A logistic regression used the thresholds to predict results of the direct elicitation. ResultsDCE and TT estimates were not meaningfully correlated (max ρ = 0.17). Participant rankings of attributes matched only 20% of the time (58/292). Neither method predicted preference for being screened (ps > 0.21). ConclusionsThe DCE and TT yielded different preference estimates (and rank orderings) for the same participant. Neither method predicted patients’ desires for a screening test. Practice implicationsClinicians, patients, policy makers, and researchers should be aware that patient preference results may be sensitive to the method of eliciting preferences.

Does the rule matter? A comparison of preference elicitation methods and voting rules based on data from an Austrian regional parliamentary election in 2019

Based on data collected in connection with the 2019 parliamentary election in the Austrian region of Styria, we analyze (the use of) different voting rules. Following previous empirical studies in the literature, we use the results of an empirical survey to show that the choice of a voting rule will impact the outcome of an election (if revealed preferences are sincere), at least in certain parts of the social ranking. In addition, we observe a certain desire for voting rules using more fine-grained preference information. In that context, we investigate the degree of consistency in the voters’ declaration of preferences, something of relevance when different voting rules, that require different levels of information, are used. Finally, we discuss the occurrence of strategic behavior that can be observed in the data.

5613464 INTEGRATION OF PHARMACOKINETIC AND PHARMACODYNAMIC MODELS TO MULTI-CRITERIA DECISION ANALYSIS (MCDA) FOR PROSPECTIVE ASSESSMENT OF THE RISK-BENEFIT RATIO OF ORAL IRON CHELATORS IN PAEDIATRIC PATIENTS WITH TRANSFUSION-DEPENDENT HAEMOGLOBINOPATHIES

Background: Current therapies for the treatment of thalassaemia and other hereditary rare haemoglobinopathies are symptomatic and aim at controlling or reducing iron overload by chelation of iron from a very early age. Iron chelation ultimately prevents complications or comorbidities that can significantly reduce patient survival. Given the processes underlying iron homeostasis and current understanding of the chelation mechanism of available chelating agents, it may be possible to implement alternative, more efficient approaches for evidence generation in support of the dose rationale and their use across different populations affected by transfusion-dependent haemoglobinopathies. Aim: This study aimed to characterise the benefit-risk balance (BRB) of deferiprone in different populations, including children < 2 years old and paediatric sickle cell patients, using a nonlinear mixed effects modelling approach in conjunction extrapolation concepts and multicriteria decision analysis (MCDA). Methods: Based on the underlying pharmacokinetic-pharmacodynamic relationships as well as on the incidence of adverse events, extrapolation concepts were applied to predict the BRB of treatment in young adults, paediatric patients younger than 2 years, and paediatric sickle cell disease patients receiving deferiprone. Using individual patient-level data from a randomised controlled clinical trial (DEEP-2, EudraCT 2012-000353-31), preference elicitation methods were applied to select and prioritise endpoints or outcomes to be used in the evaluation of the BRB across the populations of interest. Subsequently, model parameter estimates obtained from each model/endpoint were used in a set of clinical simulation scenarios in which the effect of chelation therapy was evaluated during 12 months in a virtual cohort of patients. Results: Five endpoints were selected and ranked by the experts involved in the preference elicitation process (Fig.1). ‘Absence or prevention of complications (diabetes/hypothiroidism)’ and ‘occurrence of agranulocytosis’ were the most important criteria, while ‘incidence of arthralgia’ and ‘occurrence of neutropenia’ were considered less important. Clinical trial simulations revealed that the overall benefit score of deferiprone was higher for the treatment of paediatric SCD patient compared with the reference population (i.e. DEEP-2 study). Small but statistically significant differences were observed for the use of DFP in children younger than 2 years old and adolescents or young adults. Table 1 summarises the clinical and demographic baseline characteristics of the DEEP-2 study and virtual cohort of patients included in the different simulation scenarios. Conclusion: This study has shown that MCDA can be utilised in combination with modelling and simulation principles to prospectively assess the effect of interventions in subgroups of patients for which clinical data generation is challenging or not feasible. It also offers an opportunity to evaluate the implications of dose adjustment as well as changes in the clinical management of these patients, providing a less empirical basis for dose selection and treatment choices in rare paediatric conditions such as transfusion-dependent haemoglobinopathies. Table 1 - Demographics of the populations in the MCDA analysis. Reference population reflects the actual patients in the DEEP-2 study, whilst scenarios reflect the baseline characteristics from the virtual patient population. Population Age (years) Weight (kg) Blood consumption (mL/kg/year) Baseline ferritin (ng/mL) Reference population (DEEP-2 study) 8.9 (2.7-17.2) 26.5 (12.1-53.5) 155.4 (17.6-404.3) 1991 (586.2-6488.7) Scenario 1: Adolescents and young adults 17.6 (11.3-33.8) 58 (29-83.6) 139.7 (28.5-347.6) 1933 (432-10106) Scenario 2: Children < 2 years old 1.3 (0.6-1.9) 6.3 (1.1-9.3) 162.2 (21-382.9) 726.3 (253.3-1757) Scenario 3: Paediatric sickle cell patients 14.5 (12.4-17.8) 39 (30-62.4) 139.5 (23.8-338.2) 1598.4 (476.3-8993.9)

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 selected literature review of the effect of Covid-19 on preferences

This article surveys the rapidly growing literature that examined the influence of Covid-19 on preferences. Based on 33 studies, the article examines how the pandemic impacted altruism, cooperation, trust, inequity aversion, risk-taking, and patience/time discounting. Even though the survey suggests the effect of the pandemic on preferences is heterogeneous, some noticeable patterns can be observed in the literature. First, in the case of incentivized preference elicitation, there is weak evidence that the pandemic positively influenced altruism and had no significant impact on time preferences or patience. Second, many studies that used balanced panel data and incentivized preference elicitation mechanisms do not find a significant effect of the pandemic on preferences. Last, studies that used unincentivized methods to elicit preferences show relatively higher variability in results when compared to the studies that used incentivized methods for preference elicitation. The organized synthesis and several noticeable patterns can help future research focusing on preference stability during Covid-19 and other unfavorable events.

A z-Tree implementation of the Dynamic Experiments for Estimating Preferences [DEEP] method

Among the myriad preference elicitation methods used in experimental economics and finance, adaptive elicitation methods are a (relatively) recent innovation. Here we present a ready-made and user-friendly z-Tree application for the elicitation of risk- and time-preference parameters from the most prominent adaptive elicitation method, Dynamic Experiments for Estimating Preferences (Toubia et al., 2013). In addition to the software application, we include the code and statistical scripts for data processing when using this method that enables econometric estimation of the individual and aggregate risk- and time-preference parameters.

Developing an Australian utility value set for MacNew-7D health states.

A new preference-based measure (MacNew-7D) has recently been developed to allow condition-specific data to be used to capture the quality of life in health economic evaluations in cardiology; however, a general population value set has not yet been developed. This study developed a population utility value set for the MacNew-7D heart disease-specific instrument. The discrete choice experiments (DCE) technique was chosen as the preference-elicitation method. The DCE asked respondents to compare two options and to state their preferences. The survey was conducted using an online panel of respondents, with quota sampling using age groups, sex and jurisdictions to achieve representativeness of the Australian population. The total design consisted of 200 choice sets, of which each respondent answered eight. Additionally, each respondent answered two quality control choice sets. The best-fitting models were selected on the basis of consistency, parsimony, and goodness of fit. In total, 1903 respondents were included in the analyses. The MacNew-7D utility value set ranged from -0.4456 to 1.000 for health states defined by the classification system. The best-fitting model retained all levels for five dimensions and collapsed one adjacent level for the other two dimensions. Findings were robust to sensitivity analyses related to the inclusion or exclusion of dominancy and repeat tasks. Findings indicated that the MacNew-7D utility value set is likely suitable for estimating quality-adjusted life years derived from the MacNew heart disease health-related quality-of-life questionnaire. This value set was derived from an Australian population-based sample and may not be generalisable to dissimilar populations.

Suitability of Preference Methods Across the Medical Product Lifecycle: A Multicriteria Decision Analysis

ObjectivesThis study aimed to understand the importance of criteria describing methods (eg, duration, costs, validity, and outcomes) according to decision makers for each decision point in the medical product lifecycle (MPLC) and to determine the suitability of a discrete choice experiment, swing weighting, probabilistic threshold technique, and best-worst scale cases 1 and 2 at each decision point in the MPLC. MethodsApplying multicriteria decision analysis, an online survey was sent to MPLC decision makers (ie, industry, regulatory, and health technology assessment representatives). They ranked and weighted 19 methods criteria from an existing performance matrix about their respective decisions across the MPLC. All criteria were given a relative weight based on the ranking and rating in the survey after which an overall suitability score was calculated for each preference elicitation method per decision point. Sensitivity analyses were conducted to reflect uncertainty in the performance matrix. ResultsFifty-nine industry, 29 regulatory, and 5 health technology assessment representatives completed the surveys. Overall, “estimating trade-offs between treatment characteristics” and “estimating weights for treatment characteristics” were highly important criteria throughout all MPLC decision points, whereas other criteria were most important only for specific MPLC stages. Swing weighting and probabilistic threshold technique received significantly higher suitability scores across decision points than other methods. Sensitivity analyses showed substantial impact of uncertainty in the performance matrix. ConclusionAlthough discrete choice experiment is the most applied preference elicitation method, other methods should also be considered to address the needs of decision makers. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.

PCR166 Online Elicitation of Personal Utility Functions (OPUF) – An Open, Modular Health Valuation Platform

Commonly used preference elicitation methods, such as TTO or DCE, usually require data from hundreds of participants. Conducting health valuation studies thus becomes time and resource intensive, and eliciting preferences from patient, alongside HTA, for example, is often deemed infeasible altogether. This severely limits the availability of relevant (patient) preference information to decision makers. Here, we report on the current state of the Online elicitation of Personal Utility Function (OPUF) method; a new approach for valuing health and well-being.

OP35 Suitability Of Preference Methods Across The Medical Product Lifecycle: A Multicriteria Decision Analysis

IntroductionTo understand the importance of the preference methods criteria to stakeholders at each decision point in the Medical Product Lifecycle (MPLC) and to determine the suitability of commonly applied preference methods (Discrete Choice Experiment [DCE], swing weighting [SW], probabilistic threshold technique [PTT], Best-Worst Scaling case 1 [BWS1], Best-Worst Scaling case 2 [BWS2]) for a given decision-point.MethodsNineteen preference methods criteria of an existing performance matrix were incorporated in an online survey of industry, regulatory, and health technology assessment (HTA) stakeholders. All methods criteria were given a relative weight based on the SW ranking and point allocation task in the survey. Based on this relative weight and the performance matrix values, an overall suitability score was calculated for each method per critical decision point along the MPLC. Several sensitivity analyses were conducted for which the performance matrix was adapted.ResultsIn total 59 industry, 29 regulatory, and 5 HTA representatives completed the survey. In general, ‘estimating trade-offs between characteristics’, and ‘estimating weights for treatment characteristics’ were important preference method criteria throughout all MPLC decision points, while other preference method criteria were most important only for specific MPLC stages. Both BWS1 and BWS2 seem equally suitable across decision points, DCEs seem most suitable during clinical development and regulatory launch, and SW and PTT seem most suitable throughout industry decision points. Sensitivity analysis showed substantial impact of slight changes in the performance matrix.ConclusionsWith rapid changes in preference research, performance matrices of preference methods should continue to be re-evaluated as more and more evidence accumulates. While DCE is the most applied preference elicitation method, other methods should also be considered to address the needs of MPLC stakeholders. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use.

Exploring perceptions of using preference elicitation methods to inform clinical trial design in rheumatology: A qualitative study and OMERACT collaboration

BackgroundClinical trial design requires value judgements and understanding patient preferences may help inform these judgements, for example when prioritizing treatment candidates, designing complex interventions, selecting appropriate outcomes, determining clinically important thresholds, or weighting composite outcomes. Preference elicitation methods are quantitative approaches that can estimate patients' preferences to quantify the absolute or relative importance of outcomes or other attributes relevant to the decision context. We aimed to explore stakeholder perceptions of using preference elicitation methods to inform judgements when designing clinical trials in rheumatology. MethodsWe conducted 1-on-1 semi-structured interviews with patients with rheumatic diseases and rheumatology clinicians/researchers, recruited using purposive and snowball sampling. Participants were provided pre-interview materials, including a video and a document, to introduce the topic of preference elicitation methods and case examples of potential applications to clinical trials. Interviews were conducted via Zoom and were audio-recorded and transcribed. We used thematic analysis to analyze our data. ResultsWe interviewed 17 patients and 9 clinicians/researchers, until data and inductive thematic saturation were achieved within each group. Themes were grouped into overall perceptions, barriers, and facilitators. Patients and clinicians/researchers generally agreed that preference elicitation studies can improve clinical trial design, but that many considerations are required around preference heterogeneity and feasibility. A key barrier identified was the additional resources and expertise required to measure and incorporate preferences effectively in trial design. Key facilitators included developing guidance on how to use preference elicitation to inform trial design, as well as the role of external decision-makers in developing such guidance, and the need to leverage the movement towards patient engagement in research to encourage including patient preferences when designing trials. ConclusionOur findings allowed us to consider the potential applications of patient preferences in trial design according to stakeholders within rheumatology who are involved in the trial process. Future research should be conducted to develop comprehensive guidance on how to meaningfully include patient preferences when designing clinical trials in rheumatology. Doing so may have important downstream effects for shared decision-making, especially given the chronic nature of rheumatic diseases.