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Objectives Discrete choice experiments (DCEs) are increasingly advocated as a way to quantify preferences for health. However, increasing support does not necessarily result in increasing quality. Although specific reviews have been conducted in certain contexts, there exists no recent description of the general state of the science of health-related DCEs. The aim of this paper was to update prior reviews (1990–2012), to identify all health-related DCEs and to provide a description of trends, current practice and future challenges. Methods A systematic literature review was conducted to identify health-related empirical DCEs published between 2013 and 2017. The search strategy and data extraction replicated prior reviews to allow the reporting of trends, although additional extraction fields were incorporated. Results Of the 7877 abstracts generated, 301 studies met the inclusion criteria and underwent data extraction. In general, the total number of DCEs per year continued to increase, with broader areas of application and increased geographic scope. Studies reported using more sophisticated designs (e.g. D-efficient) with associated software (e.g. Ngene). The trend towards using more sophisticated econometric models also continued. However, many studies presented sophisticated methods with insufficient detail. Qualitative research methods continued to be a popular approach for identifying attributes and levels. Conclusions The use of empirical DCEs in health economics continues to grow. However, inadequate reporting of methodological details inhibits quality assessment. This may reduce decision-makers’ confidence in results and their ability to act on the findings. How and when to integrate health-related DCE outcomes into decision-making remains an important area for future research.

There is emerging interest in the use of discrete choice experiments as a means of quantifying the perceived balance between benefits and risks (quantitative benefit-risk assessment) of new healthcare interventions, such as medicines, under assessment by regulatory agencies. For stated preference data on benefit-risk assessment to be used in regulatory decision making, the methods to generate these data must be valid, reliable and capable of producing meaningful estimates understood by decision makers. Some reporting guidelines exist for discrete choice experiments, and for related methods such as conjoint analysis. However, existing guidelines focus on reporting standards, are general in focus and do not consider the requirements for using discrete choice experiments specifically for quantifying benefit-risk assessments in the context of regulatory decision making. This opinion piece outlines the current state of play in using discrete choice experiments for benefit-risk assessment and proposes key areas needing to be addressed to demonstrate that discrete choice experiments are an appropriate and valid stated preference elicitation method in this context. Methodological research is required to establish: how robust the results of discrete choice experiments are to formats and methods of risk communication; how information in the discrete choice experiment can be presented effectually to respondents; whose preferences should be elicited; the correct underlying utility function and analytical model; the impact of heterogeneity in preferences; and the generalisability of the results. We believe these methodological issues should be addressed, alongside developing a ‘reference case’, before agencies can safely and confidently use discrete choice experiments for quantitative benefit-risk assessment in the context of regulatory decision making for new medicines and healthcare products.

Background Formative qualitative research is foundational to the methodological development process of quantitative health preference research (HPR). Despite its ability to improve the validity of the quantitative evidence, formative qualitative research is underreported. Objective To improve the frequency and quality of reporting, we developed guidelines for reporting this type of research. The guidelines focus on formative qualitative research used to develop robust and acceptable quantitative study protocols and corresponding survey instruments in HPR. Methods In December 2018, a steering committee was formed as a means to accumulate the expertise of the HPR community on the reporting guidelines (21 members, seven countries, multiple settings and disciplines). Using existing guidelines and examples, the committee constructed, revised, and refined the guidelines. The guidelines underwent beta testing by three researchers, and further revisions to the guidelines were made based on their feedback as well as on comments from members of the International Academy of Health Preference Research (IAHPR) and the editorial board of The Patient: Patient-Centered Outcomes Research . Results The guidelines have five components: introductory material (4 domains), methods (12), results/findings (2), discussion (2), and other (2). They are concordant with existing guidelines, published examples, beta-testing results, and expert comments. Conclusions Publishing formative qualitative research is a necessary step toward strengthening the foundation of any quantitative study, enhancing the relevance of its preference evidence. The guidelines should aid researchers, reviewers, and regulatory agencies and promote transparency within HPR more broadly.

Background Literature reviews show stated-preference studies, used to understand the values individuals place on health and health care, are increasingly administered online, potentially maximising respondent access and allowing for enhanced response quality. Online respondents may often choose whether to use a desktop or laptop personal computer (PC), tablet or smartphone, all with different screen sizes and modes of data entry, to complete the survey. To avoid differences in measurement errors, frequently respondents are asked to complete the surveys on a PC despite evidence that handheld devices are increasingly used for internet browsing. As yet, it is unknown if or how the device used to access the survey affects responses and/or the subsequent valuations derived. Method This study uses data from a discrete choice experiment (DCE) administered online to elicit preferences of a general population sample of females for a national breast screening programme. The analysis explores differences in key outcomes such as completion rates, engagement with the survey materials, respondent characteristics, response time, failure of an internal validity test and health care preferences for (1) handheld devices and (2) PC users. Preferences were analysed using a fully correlated random parameter logit (RPL) model to allow for unexplained scale and preference heterogeneity. Results One thousand respondents completed the survey in its entirety. The most popular access devices were PCs ( n  = 785), including Windows ( n  = 705) and Macbooks ( n  = 69). Two-hundred and fifteen respondents accessed the survey on a handheld device. Most outcomes related to survey behaviour, including failure of a dominance check, ‘flat lining’, self-reported attribute non-attendance (ANA) or respondent-rated task difficulty, did not differ by device type ( p  > 0.100). Respondents accessing the survey using a PC were generally quicker (median time to completion 14.5 min compared with 16 min for those using handheld devices) and were significantly less likely to speed through a webpage. Although there was evidence of preference intensity (taste) or variability (scale) heterogeneity across respondents in the sample, it was not driven by the access device. Conclusion Overall, we find that neither preferences nor choice behaviour is associated with the type of access device, as long as respondents are presented with question formats that are easy to use on small touchscreens. Health preference researchers should optimise preference instruments for a range of devices and encourage respondents to complete the surveys using their preferred device. However, we suggest that access device characteristics should be gathered and included when reporting results.

Discrete choice experiments (DCEs) are used to quantify the preferences of specified sample populations for different aspects of a good or service and are increasingly used to value interventions and services related to healthcare. Systematic reviews of healthcare DCEs have focussed on the trends over time of specific design issues and changes in the approach to analysis, with a more recent move towards consideration of a specific type of variation in preferences within the sample population, called taste heterogeneity, noting rises in the popularity of mixed logit and latent class models. Another type of variation, called scale heterogeneity, which relates to differences in the randomness of choice behaviour, may also account for some of the observed ‘differences’ in preference weights. The issue of scale heterogeneity becomes particularly important when comparing preferences across subgroups of the sample population as apparent differences in preferences could be due to taste and/or choice consistency. This primer aims to define and describe the relevance of scale heterogeneity in a healthcare context, and illustrate key points, with a simulated data set provided to readers in the Online appendix.

Introduction There have been promising developments in technologies and associated algorithm-based prescribing (‘stratified approach’) to target biologics to sub-groups of people with rheumatoid arthritis (RA). The acceptability of using an algorithm-guided approach in practice is likely to depend on various factors. Objective This study quantified preferences for an algorithm-guided approach to prescribing biologics (termed ‘biologic calculator’). Methods An online discrete choice experiment (DCE) was designed to elicit preferences from patients and the public for using a ‘biologic calculator’ compared with conventional prescribing. Treatment approaches were described by five attributes: delay to starting treatment; positive and negative predictive value (PPV/NPV); risk of infection; and cost saving to the UK national health service. Each survey contained six choice sets asking respondents to select their preferred option from two hypothetical biologic calculators or conventional prescribing. Background questions included sociodemographics, health status and healthcare experiences. DCE data were analysed using mixed logit models. Results Completed choice data were collected from 292 respondents (151 patients with RA and 142 members of the public). PPV, NPV and risk of infection were the most highly valued attributes to respondents deciding between prescribing strategies. Conclusion Respondents were generally receptive to personalised medicine in RA, but researchers developing personalised approaches should pay close attention to generating evidence on both the PPV and the NPV of their technologies.

Background Many countries offer screening programmes to unborn and newborn babies (antenatal and newborn screening) to identify those at risk of certain conditions to aid earlier diagnosis and treatment. Technological advances have stimulated the development of screening programmes to include more conditions, subsequently changing the information required and potential benefit-risk trade-offs driving participation. Quantifying preferences for screening programmes can provide programme commissioners with data to understand potential demand, the drivers of this demand, information provision required to support the programmes and the extent to which preferences differ in a population. This study aimed to identify published studies eliciting preferences for antenatal and newborn screening programmes and provide an overview of key methods and findings. Methods A systematic search of electronic databases for key terms identified eligible studies (discrete choice experiments (DCEs) or best-worst scaling (BWS) studies related to antenatal/newborn testing/screening published between 1990 and October 2018). Data were systematically extracted, tabulated and summarised in a narrative review. Results A total of 19 studies using a DCE or BWS to elicit preferences for antenatal ( n  = 15; 79%) and newborn screening ( n  = 4; 21%) programmes were identified. Most of the studies were conducted in Europe ( n  = 12; 63%) but there were some examples from North America ( n  = 2; 11%) and Australia ( n  = 2; 11%). Attributes most commonly included were accuracy of screening ( n  = 15; 79%) and when screening occurred ( n  = 13; 68%). Other commonly occurring attributes included information content ( n  = 11; 58%) and risk of miscarriage ( n  = 10; 53%). Pregnant women ( n  = 11; 58%) and healthcare professionals ( n  = 11; 58%) were the most common study samples. Ten studies (53%) compared preferences across different respondents. Two studies (11%) made comparisons between countries. The most popular analytical model was a standard conditional logit model ( n  = 11; 58%) and one study investigated preference heterogeneity with latent class analysis. Conclusion There is an existing literature identifying stated preferences for antenatal and newborn screening but the incorporation of more sophisticated design and analytical methods to investigate preference heterogeneity could extend the relevance of the findings to inform commissioning of new screening programmes.

Background Online survey-based methods are increasingly used to elicit preferences for healthcare. This digitization creates an opportunity for interactive survey elements, potentially improving respondents’ understanding and/or engagement. Objective Our objective was to understand whether, and how, training materials in a survey influenced stated preferences. Methods An online discrete-choice experiment (DCE) was designed to elicit public preferences for a new targeted approach to prescribing biologics (“biologic calculator”) for rheumatoid arthritis (RA) compared with conventional prescribing. The DCE presented three alternatives, two biologic calculators and a conventional approach (opt out), described by five attributes: delay to treatment, positive predictive value, negative predictive value, infection risk, and cost saving to the national health service. Respondents were randomized to receive training materials as plain text or an animated storyline. Training materials contained information about RA and approaches to treatment and described the biologic calculator. Background questions included sociodemographics and self-reported measures of task difficulty and attribute non-attendance. DCE data were analyzed using conditional and heteroskedastic conditional logit (HCL) models. Results In total, 300 respondents completed the DCE, receiving either plain text ( n  = 158) or the animated storyline ( n  = 142). The HCL showed the estimated coefficients for all attributes aligned with a priori expectations and were statistically significant. The scale term was statistically significant, indicating that respondents who received plain-text materials had more random choices. Further tests suggested preference homogeneity after accounting for differences in scale. Conclusions Using animated training materials did not change the preferences of respondents, but they appeared to improve choice consistency, potentially allowing researchers to include more complex designs with increased numbers of attributes, levels, alternatives or choice sets.

ObjectiveCardiac rehabilitation (CR) is offered to people who recently experienced a cardiac event, and often comprises of exercise, education and psychological care. This stated preference study aimed to investigate preferences for attributes of a psychological therapy intervention in CR.MethodsA discrete choice experiment (DCE) was conducted and recruited a general population sample and a trial sample. DCE attributes included the modality (group or individual), healthcare professional providing care, information provided prior to therapy, location and the cost to the National Health Service (NHS). Participants were asked to choose between two hypothetical designs of therapy, with a separate opt-out included. A mixed logit model was used to analyse preferences. Cost to the NHS was used to estimate willingness to pay (WTP) for aspects of the intervention design.ResultsThree hundred and four participants completed the DCE (general public sample (n=262, mean age 47, 48% female) and trial sample (n=42, mean age 66, 45% female)). A preference for receiving psychological therapy was demonstrated by both samples (general population WTP £1081; 95% CI £957 to £1206). The general population appeared to favour individual therapy (WTP £213; 95% CI £160 to £266), delivered by a CR professional (WTP £48; 9% % CI £4 to £93) and with a lower cost (β=−0.002; p<0.001). Participants preferred to avoid options where no information was received prior to starting therapy (WTP −£106; 95% CI −£153 to −£59). Results for the location attribute were variable and challenging to interpret.ConclusionsThe study demonstrates a preference for psychological therapy as part of a programme of CR, as participants were more likely to opt-in to therapy. Results indicate that some aspects of the delivery which may be important to participants can be tailored to design a psychological therapy. Preference heterogeneity is an issue which may prevent a ‘one-size-fits-all’ approach to psychological therapy in CR.

Background Antipsychotic medications are effective treatments for schizophrenia (SZ) and bipolar I disorder (BD-I), but when presented with different treatment options, there are tradeoffs that individuals make between clinical improvement and adverse effects. As new options become available, understanding the attributes of antipsychotic medications that are valued and the tradeoffs that individuals consider when choosing among them is important. Methods A discrete-choice experiment (DCE) was administered online to elicit preferences across 5 attributes of oral antipsychotics: treatment efficacy (i.e., improvement in symptom severity), weight gain over 6 months, sexual dysfunction, sedation, and akathisia. Eligible respondents were aged 18–64 years with a self-reported clinician diagnosis of SZ or BD-I. Results In total, 144 respondents with SZ and 152 with BD-I completed the DCE. Of those with SZ, 50% identified themselves as female and 69.4% as White, with a mean (SD) age of 41.0 (10.1) years. Of those with BD-I, most identified themselves as female (69.7%) and as White (77.6%), with a mean (SD) age of 40.0 (10.7) years. In both cohorts, respondents preferred oral antipsychotics with better efficacy, less weight gain, no sexual dysfunction or akathisia, and lower risk of sedation. Treatment efficacy was the most important attribute, with a conditional relative importance (CRI) of 31.4% for respondents with SZ and 31.0% for those with BD-I. Weight gain (CRI = 21.3% and 23.1%, respectively) and sexual dysfunction (CRI = 23.4% and 19.2%, respectively) were adverse effects in this study that respondents most wanted to avoid. Respondents with SZ were willing to accept 9.8 lb of weight gain or > 25% risk of sedation for symptom improvement; those with BD-I were willing to accept 8.5 lb of weight gain or a > 25% risk of sedation. Conclusions In this DCE, treatment efficacy was the most important attribute of oral antipsychotic medications among respondents with SZ and BD-I. Weight gain and sexual dysfunction were the adverse effects respondents most wanted to avoid; however, both cohorts were willing to accept some weight gain or sedation to obtain better efficacy. These results highlight features that patients value in antipsychotic medications and how they balance benefits and risks when choosing among treatments.