Construct Validity, Reliability, and Responsiveness of the 10-Item Well-being Instrument for Use in Economic Evaluation Studies.

OBJECTIVES: Economic evaluations of interventions in health and social care require outcome measures that capture their full benefits, including those beyond health. This study aimed to assess construct validity, test-retest reliability, and responsiveness of the newly developed 10-item Well-being instrument (WiX). METHODS: Data were gathered via an online survey in a representative sample of the adult general population in The Netherlands (N = 1045). Construct validity was assessed by inspecting convergent, structural, and discriminant validity, following the COnsensus-based Standards for the selection of health status Measurement INstruments methodology. Regression analyses of the WiX and its items on other validated measures of well-being were performed to assess the convergent validity of the instrument and the relevance of its items. Dimensionality of the WiX was assessed using exploratory factor analysis. To assess discriminant validity, several hypotheses in terms of well-being differences were assessed. Finally, a second survey was sent out 2 weeks after the initial survey (n = 563; 53.9% response rate) to assess the test-retest reliability and responsiveness of the WiX. RESULTS: The WiX showed to be correlated with alternative well-being measures as expected and able to sufficiently differentiate between relevant subgroups in the population. Moreover, the dimensionality analysis indicated that the WiX captures a broad array of elements relevant to well-being, including physical and mental health. The test-retest reliability was good, with an intraclass correlation coefficient of 0.82. CONCLUSIONS: The results regarding the WiX are favorable and indicate that this new instrument may be a promising alternative for existing measures of well-being for evaluating interventions in health and social care.

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).

How to Present a Decision Object in Health Preference Research: Attributes and Levels, the Decision Model, and the Descriptive Framework.

In health preference research (HPR) studies, data are generated by participants'/subjects' decisions. When developing an HPR study, it is therefore important to have a clear understanding of the components of a decision and how those components stimulate participant behavior. To obtain valid and reliable results, study designers must sufficiently describe the decision model and its components. HPR studies require a detailed examination of the decision criteria, detailed documentation of the descriptive framework, and specification of hypotheses. The objects that stimulate subjects' decisions in HPR studies are defined by attributes and attribute levels. Any limitations in the identification and presentation of attributes and levels can negatively affect preference elicitation, the quality of the HPR data, and study results. This practical guide shows how to link the HPR question to an underlying decision model. It covers how to (1) construct a descriptive framework that presents relevant characteristics of a decision object and (2) specify the research hypotheses. The paper outlines steps and available methods to achieve all this, including the methods' advantages and limitations.

Research trends in contemporary health economics: a scientometric analysis on collective content of specialty journals.

INTRODUCTION: Health economics is a thriving sub-discipline of economics. Applied health economics research is considered essential in the health care sector and is used extensively by public policy makers. For scholars, it is important to understand the history and status of health economics-when it emerged, the rate of research output, trending topics, and its temporal evolution-to ensure clarity and direction when formulating research questions. METHODS: Nearly 13,000 articles were analysed, which were found in the collective publications of the ten most specialised health economic journals. We explored this literature using patterns of term co-occurrence and document co-citation. RESULTS: The research output in this field is growing exponentially. Five main research divisions were identified: (i) macroeconomic evaluation, (ii) microeconomic evaluation, (iii) measurement and valuation of outcomes, (iv) monitoring mechanisms (evaluation), and (v) guidance and appraisal. Document co-citation analysis revealed eighteen major research streams and identified variation in the magnitude of activities in each of the streams. A recent emergence of research activities in health economics was seen in the Medicaid Expansion stream. Established research streams that continue to show high levels of activity include Child Health, Health-related Quality of Life (HRQoL) and Cost-effectiveness. Conversely, Patient Preference, Health Care Expenditure and Economic Evaluation are now past their peak of activity in specialised health economic journals. Analysis also identified several streams that emerged in the past but are no longer active. CONCLUSIONS: Health economics is a growing field, yet there is minimal evidence of creation of new research trends. Over the past 10 years, the average rate of annual increase in internationally collaborated publications is almost double that of domestic collaborations (8.4% vs 4.9%), but most of the top scholarly collaborations remain between six countries only.

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.

Preferences of patients, clinicians, and healthy controls for the management of a Bethesda III thyroid nodule.

BACKGROUND: Active surveillance is propagated as an alternative for hemithyroidectomy in the management of Bethesda III thyroid nodules. METHODS: A cross-sectional survey questioned respondents on their willingness to accept risks related to active surveillance and hemithyroidectomy. RESULTS: In case of active surveillance, respondents (129 patients, 46 clinicians, and 66 healthy controls) were willing to accept a risk of 10%-15% for thyroid cancer and 15% for needing more extensive surgery in the future. Respondents were willing to accept a risk of 22.5%-30% for hypothyroidism after hemithyroidectomy. Patients and controls were willing to accept a higher risk on permanent voice changes compared with clinicians (10% vs. 3%, p < 0.001). CONCLUSION: Real-life risks associated which active surveillance and hemithyroidectomy for Bethesda III nodules are equivalent or less than the risks people are willing to accept. Clinicians accepted less risk for permanent voice changes.

Towards machine learning for moral choice analysis in health economics: A literature review and research agenda.

BACKGROUND: Discrete choice models (DCMs) for moral choice analysis will likely lead to erroneous model outcomes and misguided policy recommendations, as only some characteristics of moral decision-making are considered. Machine learning (ML) is recently gaining interest in the field of discrete choice modelling. This paper explores the potential of combining DCMs and ML to study moral decision-making more accurately and better inform policy decisions in healthcare. METHODS: An interdisciplinary literature search across four databases - PubMed, Scopus, Web of Science, and Arxiv - was conducted to gather papers. Based on the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA) guideline, studies were screened for eligibility on inclusion criteria and extracted attributes from eligible papers. Of the 6285 articles, we included 277 studies. RESULTS: DCMs have shortcomings in studying moral decision-making. Whilst the DCMs' mathematical elegance and behavioural appeal hold clear interpretations, the models do not account for the 'moral' cost and benefit in an individual's utility calculation. The literature showed that ML obtains higher predictive power, model flexibility, and ability to handle large and unstructured datasets. Combining the strengths of ML methods with DCMs has the potential for studying moral decision-making. CONCLUSIONS: By providing a research agenda, this paper highlights that ML has clear potential to i) find and deepen the utility specification of DCMs, and ii) enrich the insights extracted from DCMs by considering the intrapersonal determinants of moral decision-making.

A Roadmap for Increasing the Usefulness and Impact of Patient-Preference Studies in Decision Making in Health: A Good Practices Report of an ISPOR Task Force.

Many qualitative and quantitative methods are readily available to study patient preferences in health. These methods are now being used to inform a wide variety of decisions, and there is a growing body of evidence showing studies of patient preferences can be used for decision making in a wide variety of contexts. This ISPOR Task Force report synthesizes current good practices for increasing the usefulness and impact of patient-preference studies in decision making. We provide the ISPOR Roadmap for Patient Preferences in Decision Making that invites patient-preference researchers to work with decision makers, patients and patient groups, and other stakeholders to ensure that studies are useful and impactful. The ISPOR Roadmap consists of 5 key elements: (1) context, (2) purpose, (3) population, (4) method, and (5) impact. In this report, we define these 5 elements and provide good practices on how patient-preference researchers and others can actively contribute to increasing the usefulness and impact of patient-preference studies in decision making. We also present a set of key questions that can support researchers and other stakeholders (eg, funders, reviewers, readers) to assess efforts that promote the ongoing impact (both intended and unintended) of a particular preference study and additional studies in the future.

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.

Dyadic Discrete Choice Experiments Enable Persons with Dementia and Informal Caregivers to Participate in Health Care Decision Making: A Mixed Methods Study.

BACKGROUND: Discrete choice experiments (DCEs) may facilitate persons with dementia and informal caregivers to state care preferences. DCEs can be cognitively challenging for persons with dementia. OBJECTIVE: This study aims to design a dementia friendly dyadic DCE that enables persons with dementia and informal caregivers to provide input individually and jointly, by testing the number of attributes and choice tasks persons with dementia can complete and providing insight in their DCE decision-making process. METHODS: This study included three DCE rounds: 1) persons with dementia, 2) informal caregivers, and 3) persons with dementia and informal caregivers together. A flexible DCE design was employed, with increasing choice task complexity to explore cognitive limitations in decision-making. Summary statistics and bivariate comparisons were calculated. A qualitative think-aloud approach was used to gain insight in the DCE decision-making processes. Transcripts were analyzed using thematic analysis. RESULTS: Fifteen person with dementia, 15 informal caregiver, and 14 dyadic DCEs were conducted. In the individual DCE, persons with dementia completed six choice tasks (median), and 80% could complete a choice task with least three attributes. In the dyadic DCE persons with dementia completed eight choice tasks (median) and could handle slightly more attributes. Qualitative results included themes of core components in DCE decision-making such as: understanding the choice task, attribute and level perception, option attractiveness evaluation, decision rule selection, and preference adaptation. CONCLUSION: Persons with dementia can use simple DCE designs. The dyadic DCE was promising for dyads to identify overlapping and discrepant care preferences while reaching consensus.

Can healthcare choice be predicted using stated preference data? The role of model complexity in a discrete choice experiment about colorectal cancer screening.

INTRODUCTION: The validity of discrete choice experiments (DCEs) is crucial to its usage in healthcare decision-making, but there is only a limited number of health contexts in which external validity is demonstrated. This study aims to assess the internal and external validity of the DCE in the context of colorectal cancer (CRC) screening, and gather insights into the discrepancy between stated and revealed preferences. METHODS: Stated and revealed preferences were elicited on an individual level from Dutch residents eligible for CRC screening in a DCE and a field experiment, respectively (N = 568). To identify the determinants of CRC screening participation and their relative importance, five random utility maximisation models that varied in complexity were used. We assessed the accuracy with which the models based on stated preferences predict individual-level screening choice in a holdout task (internal validity) and in the actual screening choice (external validity). Insights into the discrepancy between stated and revealed preferences were gathered by comparing groups of respondents. RESULTS: Our findings show high internal and external validity. Choices could be accurately predicted for 95% of the respondents in the holdout task, and 90% in the actual screening choice. When scale and preference heterogeneity were taken into account model fit improved; individual-level prediction accuracy slightly increased for the holdout task but not for the actual screening choice. Respondents for whom stated preferences matched revealed preferences were generally in better health and found the GP's support for their screening decision more important. DISCUSSION: Evidence was found that revealed preferences can be predicted accurately on an individual level. Incorporating heterogeneity improved internal validity but not external validity. Differences between stated and revealed preferences can be attributed to respondents' health and the support of their GP. We suggest researchers to continue investigating the internal and external validity of discrete choice experiments, and the role of model complexity.

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.

Identifying the impact of social influences in health-related discrete choice experiments.

Several disciplines, among them health, sociology, and economics, provide strong evidence that social context is important to individual choices. It is therefore surprising that relatively little research has been focused on integrating the effect of social influence into choice models, especially given the importance of such choices in healthcare. This study developed and empirically tested a choice model that accounts for social network influences in a discrete choice experiment (DCE). We focused on maternal choices for childhood vaccination in Australia, and used an econometric choice model that explicitly 1) incorporated vaccine schedule characteristics, benefits and costs, and 2) represented up to ten different identifiable key influencer types (e.g., partner, parents, friends, healthcare professionals, inter alia), allowing for the attribution of directional importance of each influencer on the gravid woman's decision to adhere to or reject childhood vaccination. Pregnant women (N = 604) aged 18 years and older recruited from an online panel completed a survey, including a DCE and questions about key influencers. A two-class ordered latent class model was conducted to analyse the DCE data, which assumes that the underlying latent driver (in our case the WHO vaccine hesitancy scale) is ordered, to give a practical interpretation of the meaning of the classes. When the choice model considered both childhood vaccination attributes and key influencers, a very high model fit was reached. The impact of key influencers on maternal choice for childhood vaccination was massive compared to the impact of childhood vaccination attributes. The marginal impact differed between key influencers. Our DCE study showed that the maternal decision for childhood vaccination was essentially almost completely socially driven, suggesting that the potential impact of social network influences can and should be considered in health-related DCEs, particular those where there are likely to be strong underlying social norms dictating decision maker behaviour.

Applications of discrete choice experiments in COVID-19 research: Disparity in survey qualities between health and transport fields.

Published choice experiments linked to various aspects of the COVID-19 pandemic are analysed in a rapid review. The aim is to (i) document the diversity of topics as well as their temporal and geographical patterns of emergence, (ii) compare various elements of design quality across different sectors of applied economics, and (iii) identify potential signs of convergent validity across findings of comparable experiments. Of the N = 43 published choice experiments during the first two years of the pandemic, the majority identifies with health applications (n = 30), followed by transport-related applications (n = 10). Nearly 100,000 people across the world responded to pandemic-related discrete choice surveys. Within health applications, while the dominant theme, up until June 2020, was lockdown relaxation and tracing measures, the focus shifted abruptly to vaccine preference since then. Geographical origins of the health surveys were not diverse. Nearly 50% of all health surveys were conducted in only three countries, namely US, China and The Netherlands. Health applications exhibited stronger pre-testing and larger sample sizes compared to transport applications. Limited signs of convergent validity were identifiable. Within some applications, issues of temporal instability as well as hypothetical bias attributable to social desirability, protest response or policy consequentiality seemed likely to have affected the findings. Nevertheless, very few of the experiments implemented measures of hypothetical bias mitigation and those were limited to health studies. Our main conclusion is that swift administration of pandemic-related choice experiments has overall resulted in certain degrees of compromise in study quality, but this has been more so the case in relation to transport topics than health topics.

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.

Efficacy, cost-minimization, and budget impact of a personalized discharge letter for basal cell carcinoma patients to reduce low-value follow-up care.

BACKGROUND: The incidence of keratinocyte carcinomas is high and rapidly growing. Approximately 80% of keratinocyte carcinomas consist of basal cell carcinomas (BCC) with 50% of these being considered as low-risk tumors. Nevertheless, 83% of the low-risk BCC patients were found to receive more follow-up care than recommended according to the Dutch BCC guideline, which is one visit post-treatment for this group. More efficient management could reduce unnecessary follow-up care and related costs. OBJECTIVES: To study the efficacy, cost-utility, and budget impact of a personalized discharge letter for low-risk BCC patients compared with usual care (no personalized letter). METHODS: In a multi-center intervention study, a personalized discharge letter in addition to usual care was compared to usual care in first-time BCC patients. Model-based cost-utility and budget impact analyses were conducted, using individual patient data gathered via surveys. The outcome measures were number of follow-up visits, costs and quality adjusted life years (QALY) per patient. RESULTS: A total of 473 first-time BCC patients were recruited. The personalized discharge letter decreased the number of follow-up visits by 14.8% in the first year. The incremental costs after five years were -€24.45 per patient. The QALYs were 4.12 after five years and very similar in both groups. The national budget impact was -€2,7 million after five years. CONCLUSIONS: The distribution of a personalized discharge letter decreases the number of unnecessary follow-up visits and implementing the intervention in a large eligible population would results in substantial cost savings, contributing to restraining the growing BCC costs.

Surgeons preference for lumbar disk surgery: a discrete choice experiment.

PURPOSE: Multiple surgical techniques are practiced to treat sciatica caused by lumbar disk herniation. It is unknown which factors surgeons find important when offering certain surgical techniques. The objective of this study is threefold: 1) determine the relative weight surgeons place on various characteristics of sciatica treatment, 2) determine the trade-offs surgeons make between these characteristics and 3) identify preference heterogeneity for sciatica treatment. METHODS: A discrete choice experiment was conducted among members of two international neurosurgical organizations. Surgeons were asked on their preferences for surgical techniques using specific scenarios based on five characteristics: effectiveness on leg pain, risk of recurrent disk herniation, duration of postoperative back pain, risk of complications and recovery period. RESULTS: Six-hundred and forty-one questionnaires were filled in, the majority by neurosurgeons. All characteristics significantly influenced the preferences of the respondents. Overall, the risk of complications was the most important characteristic in the decision to opt-in or opt-out for surgery (35.7%). Risk of recurrent disk herniation (19.6%), effectiveness on leg pain (18.8%), postoperative back pain duration (13.5%) and length of recovery period (12.4%) followed. Four latent classes were identified, which was partly explained by the tenure of the surgeon. Surgeons were willing to trade-off 57.8% of effectiveness on leg pain to offer a treatment that has a 1% complication risk instead of 10%. CONCLUSION: In the context of this discrete choice experiment, it is shown that neurosurgeons consider the risk of complications as most important when a surgical technique is offered to treat sciatica, while the risk of recurrent disk herniation and effectiveness are also important factors. Neurosurgeons were prepared to trade off substantial amounts of effectiveness to achieve lower complication rates.

A Guide to Observable Differences in Stated Preference Evidence.

BACKGROUND AND OBJECTIVE: In health preference research, studies commonly hypothesize differences in parameters (i.e., differential or joint effects on attribute importance) and/or in choice predictions (marginal effects) by observable factors. Discrete choice experiments may be designed and conducted to test and estimate these observable differences. This guide covers how to explore and corroborate various observable differences in health preference evidence. METHODS: The analytical process has three steps: analyze the exploratory data, analyze the confirmatory data, and interpret and disseminate the evidence. In this guide, we demonstrate the process using dual samples (where exploratory and confirmatory samples were collected from different sources) on 2020 US COVID-19 vaccination preferences; however, investigators may apply the same approach using split samples (i.e., single source). RESULTS: The confirmatory analysis failed to reject ten of the 17 null hypotheses generated by the exploratory analysis (p < 0.05). Apart from demographic, socioeconomic, and geographic differences, political independents and persons who have never been vaccinated against influenza are among those least likely to be vaccinated (0.838 and 0.872, respectively). CONCLUSIONS: For all researchers in health preference research, it is essential to know how to identify and corroborate observable differences. Once mastered, this skill may lead to more complex analyses of latent differences (e.g., latent classes, random parameters). This guide concludes with six questions that researchers may ask themselves when conducting such analyses or reviewing published findings of observable differences.