Patient preferences in genetic newborn screening for rare diseases: study protocol.

INTRODUCTION: Rare diseases (RDs) collectively impact over 30 million people in Europe. Most individual conditions have a low prevalence which has resulted in a lack of research and expertise in this field, especially regarding genetic newborn screening (gNBS). There is increasing recognition of the importance of incorporating patients' needs and general public perspectives into the shared decision-making process regarding gNBS. This study is part of the Innovative Medicine Initiative project Screen4Care which aims at shortening the diagnostic journey for RDs by accelerating diagnosis for patients living with RDs through gNBS and the use of digital technologies, such as artificial intelligence and machine learning. Our objective will be to assess expecting parent's perspectives, attitudes and preferences regarding gNBS for RDs in Italy and Germany. METHODS AND ANALYSIS: A mixed method approach will assess perspectives, attitudes and preferences of (1) expecting parents seeking genetic consultation and (2) 'healthy' expecting parents from the general population in two countries (Germany and Italy). Focus groups and interviews using the nominal group technique and ranking exercises will be performed (qualitative phase). The results will inform the treatment of attributes to be assessed via a survey and a discrete choice experiment (DCE). The total recruitment sample will be 2084 participants (approximatively 1000 participants in each country for the online survey). A combination of thematic qualitative and logit-based quantitative approaches will be used to analyse the results of the study. ETHICS AND DISSEMINATION: This study has been approved by the Erlangen University Ethics Committee (22-246_1-B), the Freiburg University Ethics Committee (23-1005 S1-AV) and clinical centres in Italy (University of FerraraCE: 357/2023/Oss/AOUFe and Hospedale Bambino Gesu: No.2997 of 2 November 2023, Prot. No. _902) and approved for data storage and handling at the Uppsala University (2022-05806-01). The dissemination of the results will be ensured via scientific journal publication (open access).

A socially interdependent choice framework for social influences in healthcare decision-making: a study protocol.

OBJECTIVES: Current choice models in healthcare (and beyond) can provide suboptimal predictions of healthcare users' decisions. One reason for such inaccuracy is that standard microeconomic theory assumes that decisions of healthcare users are made in a social vacuum. Healthcare choices, however, can in fact be (entirely) socially determined. To achieve more accurate choice predictions within healthcare and therefore better policy decisions, the social influences that affect healthcare user decision-making need to be identified and explicitly integrated into choice models. The purpose of this study is to develop a socially interdependent choice framework of healthcare user decision-making. DESIGN: A mixed-methods approach will be used. A systematic literature review will be conducted that identifies the social influences on healthcare user decision-making. Based on the outcomes of a systematic literature review, an interview guide will be developed that assesses which, and how, social influences affect healthcare user decision-making in four different medical fields. This guide will be used during two exploratory focus groups to assess the engagement of participants and clarity of questions and probes. The refined interview guide will be used to conduct the semistructured interviews with healthcare professionals and users. These interviews will explore in detail which, and how, social influences affect healthcare user decision-making. Focus group and interview transcripts will be analysed iteratively using a constant comparative approach based on a mix of inductive and deductive coding. Based on the outcomes, a social influence independent choice framework for healthcare user decision-making will be drafted. Finally, the Delphi technique will be employed to achieve consensus about the final version of this choice framework. ETHICS AND DISSEMINATION: This study was approved by the Erasmus School of Health Policy and Management Research Ethics Review Committee (ESHPM, Rotterdam, The Netherlands; reference ETH2122-0666).

Stated-Preference Survey Design and Testing in Health Applications.

Following the conceptualization of a well-formulated and relevant research question, selection of an appropriate stated-preference method, and related methodological issues, researchers are tasked with developing a survey instrument. A major goal of designing a stated-preference survey for health applications is to elicit high-quality data that reflect thoughtful responses from well-informed respondents. Achieving this goal requires researchers to design engaging surveys that maximize response rates, minimize hypothetical bias, and collect all the necessary information needed to answer the research question. Designing such a survey requires researchers to make numerous interrelated decisions that build upon the decision context, selection of attributes, and experimental design. Such decisions include considering the setting(s) and study population in which the survey will be administered, the format and mode of administration, and types of contextual information to collect. Development of a survey is an interactive process in which feedback from respondents should be collected and documented through qualitative pre-test interviews and pilot testing. This paper describes important issues to consider across all major steps required to design and test a stated-choice survey to elicit patient preferences for health preference research.

Treatment Preferences of Adolescents and Young Adults with Depressive Symptoms: A Discrete Choice Experiment.

BACKGROUND: Depression in adolescents and young adults is common and causes considerable disease burden while hampering their development, leading to adverse consequences in later life. Although treatment is available, young people are a vulnerable group regarding uptake and completion of treatment. To improve this, insight into youth's preferences for treatment is essential. OBJECTIVE: The aim of this study was to investigate patient preferences for depression treatment in a Dutch sample aged 16-24 years using a discrete choice experiment (DCE). METHODS: The study was conducted in The Netherlands between October 2018 and June 2019, and included 236 adolescents and young adults with current depressive symptoms or previous treatment. The DCE included five attributes (treatment type, frequency of appointment, waiting time, effectiveness, evaluation of therapeutic alliance) with corresponding levels. Results were analysed using latent class analysis. RESULTS: Results show a general preference for individual psychotherapy, treatment with high frequency, high effectiveness, short waiting time and a standard evaluation of the therapeutic alliance ('click' with the therapist) early in treatment. Latent class analysis revealed three different patterns of preferences regarding treatment type and willingness to engage in therapy. The first class showed a strong preference for individual therapy. The second class, including relatively older, higher educated and treatment-experienced participants, preferred high frequency treatment and was more open to different forms of therapy. The third class, including lower educated, younger and treatment-naïve adolescents showed reluctance to engage in therapy overall and in group therapy specifically. CONCLUSION: In this DCE, three classes could be identified that share similar preferences regarding treatment effectiveness, waiting time and evaluation of the therapeutic alliance, but varied considerably in their preference for treatment type (individual, group, or combined psychotherapy) and their willingness to engage. The results from this study may inform mental health care providers and institutions and help optimize professional care for adolescents and young adults with depressive symptoms, improving engagement in this vulnerable group.

Research Priorities to Increase Confidence in and Acceptance of Health Preference Research: What Questions Should be Prioritized Now?

BACKGROUND AND OBJECTIVE: There has been an increase in the study and use of stated-preference methods to inform medicine development decisions. The objective of this study was to identify prioritized topics and questions relating to health preferences based on the perspective of members of the preference research community. METHODS: Preference research stakeholders from industry, academia, consultancy, health technology assessment/regulatory, and patient organizations were recruited using professional networks and preference-targeted e-mail listservs and surveyed about their perspectives on 19 topics and questions for future studies that would increase acceptance of preference methods and their results by decision makers. The online survey consisted of an initial importance prioritization task, a best-worst scaling case 1 instrument, and open-ended questions. Rating counts were used for analysis. The best-worst scaling used a balanced incomplete block design. RESULTS: One hundred and one participants responded to the survey invitation with 66 completing the best-worst scaling. The most important research topics related to the synthesis of preferences across studies, transferability across populations or related diseases, and method topics including comparison of methods and non-discrete choice experiment methods. Prioritization differences were found between respondents whose primary affiliation was academia versus other stakeholders. Academic researchers prioritized methodological/less studied topics; other stakeholders prioritized applied research topics relating to consistency of practice. CONCLUSIONS: As the field of health preference research grows, there is a need to revisit and communicate previous work on preference selection and study design to ensure that new stakeholders are aware of this work and to update these works where necessary. These findings might encourage discussion and alignment among different stakeholders who might hold different research priorities. Research on the application of previous preference research to new contexts will also help increase the acceptance of health preference information by decision makers.

Prosocial Behaviour and Antibiotic Resistance: Evidence from a Discrete Choice Experiment.

INTRODUCTION: The health of a community depends on the health of its individuals; therefore, individual health behaviour can implicitly affect the health of the entire community. This is particularly evident in the case of infectious diseases. Because the level of prosociality in a community might determine the effectiveness of health programmes, prosocial behaviour may be a crucial disease-control resource. This study aimed to extend the literature on prosociality and investigate the role of altruism in antibiotic decision making. METHODS: A discrete choice experiment was conducted to assess the influence of altruism on the general public's preferences regarding antibiotic treatment options. The survey was completed by 378 Swedes. Latent class analysis models were used to estimate antibiotic treatment characteristics and preference heterogeneity. A three-class model resulted in the best model fit, and altruism significantly impacted preference heterogeneity. RESULTS: Our findings suggest that people with higher altruism levels had more pronounced preferences for treatment options with lower contributions to antibiotic resistance and a lower likelihood of treatment failure. Furthermore, altruism was statistically significantly associated with sex, education, and health literacy. CONCLUSIONS: Antibiotic awareness, trust in healthcare systems, and non-discriminatory priority setting appear to be structural elements conducive to judicious and prosocial antibiotic behaviour. This study suggests that prosocial messages could help to decrease the demand for antibiotic treatments.

Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries.

BACKGROUND: With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public's views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. OBJECTIVE: This study investigated public preferences for digital health data sharing. METHODS: A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes ("data collector," "data user," "reason for data use," "information on data sharing and consent," and "availability of review process"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class's pooled and regional data sets. RESULTS: A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. CONCLUSIONS: This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for "data collector," "data user," "reason," "type of consent," and "review" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

Patients accept therapy using embryonic stem cells for Parkinson's disease: a discrete choice experiment.

BACKGROUND: New disease-modifying ways to treat Parkinson's disease (PD) may soon become a reality with intracerebral transplantation of cell products produced from human embryonic stem cells (hESCs). The aim of this study was to assess what factors influence preferences of patients with PD regarding stem-cell based therapies to treat PD in the future. METHODS: Patients with PD were invited to complete a web-based discrete choice experiment to assess the importance of the following attributes: (i) type of treatment, (ii) aim of treatment, (iii) available knowledge of the different types of treatments, (iv) effect on symptoms, and (v) risk for severe side effects. Latent class conditional logistic regression models were used to determine preference estimates and heterogeneity in respondents' preferences. RESULTS: A substantial difference in respondents' preferences was observed in three latent preference patterns (classes). "Effect on symptoms" was the most important attribute in class 1, closely followed by "type of treatment," with medications as preferred to other treatment alternatives. Effect on symptoms was also the most important attribute in class 2, with treatment with hESCs preferred over other treatment alternatives. Likewise for class 3, that mainly focused on "type of treatment" in the decision-making. Respondents' class membership was influenced by their experience in treatment, side effects, and advanced treatment therapy as well as religious beliefs. CONCLUSIONS: Most of the respondents would accept a treatment with products emanating from hESCs, regardless of views on the moral status of embryos. Preferences of patients with PD may provide guidance in clinical decision-making regarding treatments deriving from stem cells.

Maximum Acceptable Risk Estimation Based on a Discrete Choice Experiment and a Probabilistic Threshold Technique.

OBJECTIVE: We aimed to empirically compare maximum acceptable risk results estimated using both a discrete choice experiment (DCE) and a probabilistic threshold technique (PTT). METHODS: Members of the UK general public (n = 982) completed an online survey including a DCE and a PTT (in random order) measuring their preferences for preventative treatment for rheumatoid arthritis. For the DCE, a Bayesian D-efficient design consisting of four blocks of 15 choice tasks was constructed including six attributes with varying levels. The PTT used identical risk and benefit attributes. For the DCE, a panel mixed-logit model was conducted, both mean and individual estimates were used to calculate maximum acceptable risk. For the PTT, interval regression was used to calculate maximum acceptable risk. Perceived complexity of the choice tasks and preference heterogeneity were investigated for both methods. RESULTS: Maximum acceptable risk confidence intervals of both methods overlapped for serious infection and serious side effects but not for mild side effects (maximum acceptable risk was 32.7 percent-points lower in the PTT). Although, both DCE and PTT tasks overall were considered easy or very easy to understand and answer, significantly more respondents rated the DCE choice tasks as easier to understand compared with those who rated the PTT as easier (7-percentage point difference; p < 0.05). CONCLUSIONS: Maximum acceptable risk estimate confidence intervals based on a DCE and a PTT overlapped for two out of the three included risk attributes. More respondents rated the DCE as easier to understand. This may suggest that the DCE is better suited in studies estimating maximum acceptable risk for multiple risk attributes of differing severity, while the PTT may be better suited when measuring heterogeneity in maximum acceptable risk estimates or when investigating one or more serious adverse events.

Balancing benefits and risks in lung cancer therapies: patient preferences for lung cancer treatment alternatives.

Background: In the treatment of Non-Small Cell Lung Cancer (NSCLC) the combination of Immuno- Oncotherapy (IO) and chemotherapy (CT) has been found to be superior to IO or CT alone for patients' survival. Patients and clinicians are confronted with a preference sensitive choice between a more aggressive treatment with a greater negative effect on quality of life versus alternatives that are less effective but have fewer side effects. Objectives: The aims of this study were to: (a) quantify patients' preferences for relevant attributes related to Immuno-Oncotherapy treatment alternatives, and (b) evaluate the maximum acceptable risk (MAR)/Minimum acceptable benefit (MAB) that patients would accept for treatment alternatives. Methods: An online preference survey using discrete-choice experiment (DCE) was completed by NSCLC patients from two hospitals in Italy and Belgium. The survey asked patients' preferences for five patient- relevant treatment attributes. The DCE was developed using a Bayesian D-efficient design. DCE analyses were performed using mixed logit models. Information regarding patient demographics, health literacy, locus of control, and quality of life was also collected. Results: 307 patients (158 Italian, 149 Belgian), stage I to IV, completed the survey. Patients preferred treatments with a higher 5-year survival chance as the most important attribute over all the other attributes. Preference heterogeneity for the attribute weights depended on health literacy, patients' age and locus of control. Patients were willing to accept a substantially increased risks of developing side effects in exchange for the slightest increase (1%) in the chance of surviving at least 5 years from the diagnosis of cancer. Similarly, patients were willing to accept a switch in the mode of administration or complete loss of hair to obtain an increase in survival. Conclusion: In this study, the proportion of respondents who systematically preferred survival over all other treatment attributes was particularly high. Age, objective health literacy and locus of control accounted for heterogeneity in patients' preferences. Evidence on how NSCLC patients trade between survival and other NSCLC attributes can support regulators and other stakeholders on assessing clinical trial evidence and protocols, based on patients' conditions and socio-demographic parameters.

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

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Taking the Shortcut: Simplifying Heuristics in Discrete Choice Experiments.

Health-related discrete choice experiments (DCEs) information can be used to inform decision-making on the development, authorisation, reimbursement and marketing of drugs and devices as well as treatments in clinical practice. Discrete choice experiment is a stated preference method based on random utility theory (RUT), which imposes strong assumptions on respondent choice behaviour. However, respondents may use choice processes that do not adhere to the normative rationality assumptions implied by RUT, applying simplifying decision rules that are more selective in the amount and type of processed information (i.e., simplifying heuristics). An overview of commonly detected simplifying heuristics in health-related DCEs is lacking, making it unclear how to identify and deal with these heuristics; more specifically, how researchers might alter DCE design and modelling strategies to accommodate for the effects of heuristics. Therefore, the aim of this paper is three-fold: (1) provide an overview of common simplifying heuristics in health-related DCEs, (2) describe how choice task design and context as well as target population selection might impact the use of heuristics, (3) outline DCE design strategies that recognise the use of simplifying heuristics and develop modelling strategies to demonstrate the detection and impact of simplifying heuristics in DCE study outcomes.

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.

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.

Diabetes patient preferences for glucose-monitoring technologies: results from a discrete choice experiment in Poland and the Netherlands.

INTRODUCTION: New glucose-monitoring technologies have different cost-benefit profiles compared with traditional finger-prick tests, resulting in a preference-sensitive situation for patients. This study aimed to assess the relative value adults with diabetes assign to device attributes in two countries. RESEARCH DESIGN AND METHODS: Adults with type 1 or 2 diabetes from the Netherlands (n=226) and Poland (n=261) completed an online discrete choice experiment. Respondents choose between hypothetical glucose monitors described using seven attributes: precision, effort to check, number of finger pricks required, risk of skin irritation, information provided, alarm function and out-of-pocket costs. Panel mixed logit models were used to determine attribute relative importance and to calculate expected uptake rates and willingness to pay (WTP). RESULTS: The most important attribute for both countries was monthly out-of-pocket costs. Polish respondents were more likely than Dutch respondents to choose a glucose-monitoring device over a standard finger prick and had higher WTP for a device. Dutch respondents had higher WTP for device improvements in an effort to check and reduce the number of finger pricks a device requires. CONCLUSION: Costs are the primary concern of patients in both countries when choosing a glucose monitor and would likely hamper real-world uptake. The costs-benefit profiles of such devices should be critically reviewed.

The Impact of Video-Based Educational Materials with Voiceovers on Preferences for Glucose Monitoring Technology in Patients with Diabetes: A Randomised Study.

INTRODUCTION: Ensuring patients have enough information about healthcare choices prior to completing a preference study is necessary to support the validity of the findings. Patients are commonly informed using text-based information with supporting graphics. Video-based information may be more engaging for the general patient population. This study aimed to assess (1) the impact that educating patients using video-based educational materials with a voiceover has on patient preferences compared to traditional text, and (2) whether this impact is consistent between two countries. MATERIALS AND METHODS: A video-based educational tool was developed to inform patients prior to completing a discrete choice experiment assessing preferences for glucose monitors. Patients with diabetes from the Netherlands and Poland were recruited through an online research panel. Respondents were randomised to receive information in either a text or a video with animations and a voiceover. Data were analysed using a mixed-logit model. RESULTS: N = 981 completed surveys were analysed from the Netherlands (n = 459) and Poland (n = 522). Differences were found between the countries, but no interpretable pattern of differences was found between the two types of educational materials. Patients spent less time in the educational material than would be necessary to fully review all of the content. CONCLUSIONS: Simply providing educational material in a video with animations and voiceovers does not necessarily lead to better engagement from respondents or different preference outcomes in a sample of diabetes patients when compared to text. Increasing engagement with educational materials should be a topic of future research for those conducting patient preference research as no amount of educational material will be helpful if respondents do not access it.

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

OBJECTIVES: This 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. METHODS: Applying 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. RESULTS: Fifty-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. CONCLUSION: Although 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.

Preferences for preventive treatments for rheumatoid arthritis: discrete choice survey in the UK, Germany and Romania.

OBJECTIVE: To quantify preferences for preventive therapies for rheumatoid arthritis (RA) across three countries. METHODS: A web-based survey including a discrete choice experiment was administered to adults recruited via survey panels in the UK, Germany and Romania. Participants were asked to assume they were experiencing arthralgia and had a 60% chance of developing RA in the next 2 years and completed 15 choices between no treatment and two hypothetical preventive treatments. Treatments were defined by six attributes (effectiveness, risks and frequency/route of administration) with varying levels. Participants also completed a choice task with fixed profiles reflecting subjective estimates of candidate preventive treatments. Latent class models (LCMs) were conducted and the relative importance of attributes, benefit-risk trade-offs and predicted treatment uptake was subsequently calculated. RESULTS: Completed surveys from 2959 participants were included in the analysis. Most participants preferred treatment over no treatment and valued treatment effectiveness to reduce risk more than other attributes. A five-class LCM best fitted the data. Country, perceived risk of RA, health literacy and numeracy predicted class membership probability. Overall, the maximum acceptable risk for a 40% reduction in the chance of getting RA (60% to 20%) was 21.7%, 19.1% and 2.2% for mild side effects, serious infection and serious side effects, respectively. Predicted uptake of profiles reflecting candidate prevention therapies differed across classes. CONCLUSION: Effective preventive pharmacological treatments for RA were acceptable to most participants. The relative importance of treatment attributes and likely uptake of fixed treatment profiles were predicted by participant characteristics.

Does It Matter How You Ask? Assessing the Impact of Failure or Effectiveness Framing on Preferences for Antibiotic Treatments in a Discrete Choice Experiment.

Purpose: Studies assessing framing effects in discrete choice experiments (DCE) primarily focused on attributes related to mortality/survival information. Little is known about framing effects for other attributes in health-related DCEs. This study aimed to investigate how framing treatment outcome as effective, failure, or a combined frame impacts respondent choices and DCE outcomes. Patients and Methods: Three Bayesian D-efficient designed DCE surveys measuring preferences for antibiotic treatments were randomly distributed to a representative sample of the Swedish population aged 18-65 years (n=1119). Antibiotic treatments were described using five attributes. Four attributes were static: Contribution to Antibiotic Resistance, Treatment Duration, Likelihood of Side-Effects, and Costs. A fifth treatment attribute was framed in three ways: Effectiveness, Failure Rate, or both. Mixed logit models were used to analyze attribute level estimates, importance value, and choice predictions. Results: Significant differences between the frames were found for the parameter estimates of the attributes of Treatment Duration and Likelihood of Side-Effects, but not Treatment Outcome which was the alternatively framed attribute. Contribution to Antibiotic Resistance and Costs were the most important attributes for all participants regardless of framing. Choice predictions for the "best option" antibiotic only slightly differed between the groups based on the frame seen (95.2-92.4%). Conclusion: Our study showed that attribute framing can impact preferences regardless of the attribute's importance value in alternative valuation. However, the practical implication of this effect may be limited. A theoretical discussion is needed to identify how researchers should accommodate and report any potential framing effect in their studies.

Preference Variation: Where Does Health Risk Attitude Come Into the Equation?

OBJECTIVES: Decisions about health often involve risk, and different decision makers interpret and value risk information differently. Furthermore, an individual's attitude toward health-specific risks can contribute to variation in health preferences and behavior. This study aimed to determine whether and how health-risk attitude and heterogeneity of health preferences are related. METHODS: To study the association between health-risk attitude and preference heterogeneity, we selected 3 discrete choice experiment case studies in the health domain that included risk attributes and accounted for preference heterogeneity. Health-risk attitude was measured using the 13-item Health-Risk Attitude Scale (HRAS-13). We analyzed 2 types of heterogeneity via panel latent class analyses, namely, how health-risk attitude relates to (1) stochastic class allocation and (2) systematic preference heterogeneity. RESULTS: Our study did not find evidence that health-risk attitude as measured by the HRAS-13 distinguishes people between classes. Nevertheless, we did find evidence that the HRAS-13 can distinguish people's preferences for risk attributes within classes. This phenomenon was more pronounced in the patient samples than in the general population sample. Moreover, we found that numeracy and health literacy did distinguish people between classes. CONCLUSIONS: Modeling health-risk attitude as an individual characteristic underlying preference heterogeneity has the potential to improve model fit and model interpretations. Nevertheless, the results of this study highlight the need for further research into the association between health-risk attitude and preference heterogeneity beyond class membership, a different measure of health-risk attitude, and the communication of risks.