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

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

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.</p

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

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

Preferences for centralised emergency medical services: discrete choice experiment

Objectives It is desirable that public preferences are established and incorporated in emergency healthcare reforms. The aim of this study was to investigate preferences for local versus centralised provision of all emergency medical services (EMS) and explore what individuals think are important considerations for EMS delivery. Design A discrete choice experiment was conducted. The attributes used in the choice scenarios were: travel time to the hospital, waiting time to be seen, length of stay in the hospital, risks of dying, readmission and opportunity for outpatient care after emergency treatment at a local hospital. Setting North East England. Participants Participants were a randomly sampled general population, aged 16 years or above recruited from Healthwatch Northumberland network database of lay members and from clinical contact with Northumbria Healthcare National Health Service Foundation Trust via Patient Experience Team. Primary and secondary outcome measures Analysis used logistic regression modelling techniques to determine the preference of each attribute. Marginal rates of substitution between attributes were estimated to understand the trade-offs individuals were willing to make. Results Responses were obtained from 148 people (62 completed a web and 86 a postal version). Respondents preferred shorter travel time to hospital, shorter waiting time, fewer number of days in hospital, low risk of death, low risk of readmission and outpatient follow-up care in their local hospital. However, individuals were willing to trade off increased travel time and waiting time for high-quality centralised care. Individuals were willing to travel 9 min more for a 1-day reduction in length of stay in the hospital, 38 min for a 1% reduction in risk of death and 112 min for having outpatient follow-up care at their local hospital. Conclusions People value centralised EMS if it provides higher quality care and are willing to travel further and wait longer

Health-state valuation using discrete choice models

The aim of this thesis was to investigate specific problems associated with preference-based measures (values) of health states and with the methodology used to derive health-state values. The first chapter covers the changes in phrasing and differences in measurement methods in the EQ-5D instrument after the five-level version was introduced alongside the conventional three-level version. Specifically, a head-to-head comparison of the values elicited from a large sample showed slight differences between the two versions. The study described in the second chapter was motivated by the fact that most instruments do not take into account the interactions of distinct health attributes (reduction in perceived health status may intensify if two different health problems interact). Our study revealed that the inclusion of interactions between various EQ-5D health aspects leads to different values for health states. The third chapter shows that people with experience of disease tend to value health states differently, or assign importance to certain health attributes differently than healthy respondents do. The fourth chapter presents a separate discrete choice study that we conducted to determine the importance of criteria regarding new medical treatments. We found four criteria to be important, based on preferences of the general population and patients: health gains, patient's age, patient's initial health condition, and cause of the disease. In the fifth chapter we present an eye-tracking study, which proved to support a basic assumption in health-state measurement: that respondents pay attention to all elements of the health state description and do not disregard information

Eye tracking to explore attendance in health-state descriptions

<div><p>Introduction</p><p>A crucial assumption in health valuation methods is that respondents pay equal attention to all information components presented in the response task. So far, there is no solid evidence that respondents are fulfilling this condition. The aim of our study is to explore the attendance to various information cues presented in the discrete choice (DC) response tasks.</p><p>Methods</p><p>Eye tracking was used to study the eye movements and fixations on specific information areas. This was done for seven DC response tasks comprising health-state descriptions. A sample of 10 respondents participated in the study. Videos of their eye movements were recorded and are presented graphically. Frequencies were computed for length of fixation and number of fixations, so differences in attendance were demonstrated for particular attributes in the tasks.</p><p>Results</p><p>All respondents completed the survey. Respondents were fixating on the left-sided health-state descriptions slightly longer than on the right-sided. Fatigue was not observed, as the time spent did not decrease in the final response tasks. The time spent on the tasks depended on the difficulty of the task and the amount of information presented.</p><p>Discussion and conclusion</p><p>Eye tracking proved to be a feasible method to study the process of paying attention and fixating on health-state descriptions in the DC response tasks. Eye tracking facilitates the investigation of whether respondents fully read the information in health descriptions or whether they ignore particular elements.</p></div

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

Lost in the crowd? Using eye-tracking to investigate the effect of complexity on attribute non-attendance in discrete choice experiments

Background: The provision of additional information is often assumed to improve consumption decisions, allowing consumers to more accurately weigh the costs and benefits of alternatives. However, increasing the complexity of decision problems may prompt changes in information processing. This is particularly relevant for experimental methods such as discrete choice experiments (DCEs) where the researcher can manipulate the complexity of the decision problem. The primary aims of this study are (i) to test whether consumers actually process additional information in an already complex decision problem, and (ii) consider the implications of any such ‘complexity-driven’ changes in information processing for design and analysis of DCEs. Methods: A discrete choice experiment (DCE) is used to simulate a complex decision problem; here, the choice between complementary and conventional medicine for different health conditions. Eye-tracking technology is used to capture the number of times and the duration that a participant looks at any part of a computer screen during completion of DCE choice sets. From this we can analyse what has become known in the DCE literature as ‘attribute non-attendance’ (ANA). Using data from 32 participants, we model the likelihood of ANA as a function of choice set complexity and respondent characteristics using fixed and random effects models to account for repeated choice set completion. We also model whether participants are consistent with regard to which characteristics (attributes) they consider across choice sets. Results: We find that complexity is the strongest predictor of ANA when other possible influences, such as time pressure, ordering effects, survey specific effects and socio-demographic variables (including proxies for prior experience with the decision problem) are considered. We also find that most participants do not apply a consistent information processing strategy across choice sets. Conclusions: Eye-tracking technology shows promise as a way of obtaining additional information from consumer research, improving DCE design, and informing the design of policy measures. With regards to DCE design, results from the present study suggest that eye-tracking data can identify the point at which adding complexity (and realism) to DCE choice scenarios becomes self-defeating due to unacceptable increases in ANA. Eye-tracking data therefore has clear application in the construction of guidelines for DCE design and during piloting of DCE choice scenarios. With regards to design of policy measures such as labelling requirements for CAM and conventional medicines, the provision of additional information has the potential to make difficult decisions even harder and may not have the desired effect on decision-making.Griffith Health, School of MedicineFull Tex

Keeping an eye on cost : what can eye tracking tell us about attention to cost information in discrete choice experiments?

The University of Aberdeen and the Chief Scientist Office of the Scottish Government Health and Social Care Directorates fund the Health Economics Research Unit (HERU). We thank all participants who took part in the study, Alison Findlay for help with data collection, HESG participants, the editor, anonymous reviewers, and Dr Frouke Hermens for helpful comments and suggestions on the paper. The information and views set out in the article are those of the authors.Peer reviewedPublisher PD