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Background: Treatment with intravenous alteplase for eligible patients with acute ischemic stroke is underused, with variation in treatment rates across the UK. This study sought to elucidate factors influencing variation in clinicians' decision-making about this thrombolytic treatment. Methods: A discrete choice experiment using hypothetical patient vignettes framed around areas of clinical uncertainty was conducted with UK-based clinicians. Mixed logit regression analyses were conducted on the data. Results: A total of 138 clinicians completed the discrete choice experiment. Seven patient factors were individually predictive of increased likelihood of immediately offering IV alteplase (compared to reference levels in brackets): stroke onset time 2 h 30 min [50 min]; pre-stroke dependency mRS 3 [mRS 4]; systolic blood pressure 185 mm/Hg [140 mm/Hg]; stroke severity scores of NIHSS 5 without aphasia, NIHSS 14 and NIHSS 23 [NIHSS 2 without aphasia]; age 85 [68]; Afro-Caribbean [white]. Factors predictive of withholding treatment with IV alteplase were: age 95 [68]; stroke onset time of 4 h 15 min [50 min]; severe dementia [no memory problems]; SBP 200 mm/Hg [140 mm/Hg]. Three clinicianrelated factors were predictive of an increased likelihood of offering IV alteplase (perceived robustness of the evidence for IV alteplase; thrombolyzing more patients in the past 12 months; and high discomfort with uncertainty) and one with a decreased likelihood (high clinician comfort with treating patients outside the licensing criteria). Conclusions: Both patient- and clinician-related factors have a major influence on the use of alteplase to treat patients with acute ischemic stroke. Clinicians' views of the evidence, comfort with uncertainty and treating patients outside the license criteria are important factors to address in programs that seek to reduce variation in care quality regarding treatment with IV alteplase. Further research is needed to further understand the differences in clinical decision-making about treating patients with acute ischemic stroke with IV alteplase. [ABSTRACT FROM AUTHOR]

We provide a user guide on the analysis of data (including best-worst and best-best data) generated from discrete-choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post-estimation. We also provide a review of standard software. In providing this guide, we endeavour to not only provide guidance on choice modelling but to do so in a way that provides a 'way in' for researchers to the practicalities of data analysis. We argue that choice of modelling approach depends on the research questions, study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful to researchers not only within but also beyond health economics. [ABSTRACT FROM AUTHOR]

Background: Discrete choice experiments with duration (DCETTO) can be used to estimate utility values for preference-based measures, such as the EQ-5D-5L. For self-completion, the health dimensions are presented in a standard order. However, for valuation, this may result in order effects. Thus, it is important to understand whether health state dimension ordering affects values. The aim of this study was to examine the importance of dimension ordering on DCE values using EQ-5D-5L.Methods: A choice experiment presenting two health profiles and a third immediate death option was developed. A three-arm study was used, with the same 120 choice sets presented online across each arm (n = 360 per arm). Arm 1 presented the standard EQ-5D-5L dimension order, arm 2 randomised order between respondents, and arm 3 randomised within respondents. Conditional logit regression was used to assess model consistency, and scale parameter testing was used to assess model poolability.Results: There were minor inconsistencies across each arm, but the magnitudes of the coefficients produced were generally consistent. Arm 3 produced the largest range of utility values (1 to -0.980). Scale parameter testing suggested that the models did not differ, and the data could be pooled. Follow-up questions did not suggest variation in terms of difficulty.Conclusions: The results suggest that the level of randomisation used in DCE health state valuation studies does not significantly impact values, and dimension order may not be as important as other study design issues. The results support past valuation studies that use the standard order of dimensions. [ABSTRACT FROM AUTHOR]

Perinatal depression and anxiety (PNDA) are an international healthcare priority, associated with significant short- and long-term problems for women, their children and families. Effective treatment is available but uptake is suboptimal: some women go untreated whilst others choose treatments without strong evidence of efficacy. Better understanding of women’s preferences for treatment is needed to facilitate uptake of effective treatment. To address this issue, a discrete choice experiment (DCE) was administered to 217 pregnant or postnatal women in Australia, who were recruited through an online research company and had similar sociodemographic characteristics to Australian data for perinatal women. The DCE investigated preferences regarding cost, treatment type, availability of childcare, modality and efficacy. Data were analysed using logit-based models accounting for preference and scale heterogeneity. Predicted probability analysis was used to explore relative attribute importance and policy change scenarios, including how these differed by women’s sociodemographic characteristics. Cost and treatment type had the greatest impact on choice, such that a policy of subsidising effective treatments was predicted to double their uptake compared with the base case. There were differences in predicted uptake associated with certain sociodemographic characteristics: for example, women with higher educational attainment were more likely to choose effective treatment. The findings suggest policy directions for decision makers whose goal is to reduce the burden of PNDA on women, their children and families. [ABSTRACT FROM AUTHOR]

Background: Much attention in recent years has been given to the topic of public engagement in health technology assessment (HTA) decision-making. HTA organizations spend substantial resources and time on undertaking public engagement, and numerous studies have examined challenges and barriers to engagement in the decision-making process however uncertainty remains as to optimal methods to incorporate the views of the public in HTA decision-making. Little research has been done to ascertain whether current engagement processes align with public preferences and to what extent their desire for engagement is dependent on the question being asked by decision-makers or the characteristics of the decision. This study will examine public preferences for engagement in Australian HTA decision-making using an exploratory mixed methods design. Methods/Design: The aims of this study are to: 1) identify characteristics about HTA decisions that are important to the public in determining whether public engagement should be undertaken on a particular topic, 2) determine which decision characteristics influence public preferences for the extent, or type of public engagement, and 3) describe reasons underpinning these preferences. Focus group participants from the general community, aged 18-70 years, will be purposively sampled from the Australian population to ensure a wide range of demographic groups. Each focus group will include a general discussion on public engagement as well as a ranking exercise using a modified nominal group technique (NGT). The NGT will inform the design of a discrete choice study to quantitatively assess public preferences for engagement in HTA decision-making. Discussion: The proposed research seeks to investigate under what circumstances and how the public would like their views and preferences to be considered in health technology assessments. HTA organizations regularlymake decisions about when and how public engagement should occur but without consideration of the public's preferences on the method and extent of engagement. This information has the potential to assist decision-makers in tailoring engagement approaches, and may be particularly useful in decisions with potential for conflict where clarification of public values and preferences could strengthen the decision-making process. [ABSTRACT FROM AUTHOR]

External validity is a crucial but under-researched topic when considering using discrete choice experiment (DCE) results to inform decision making in clinical, commercial or policy contexts. We present the theory and tests traditionally used to explore external validity that focus on a comparison of final outcomes and review how this traditional definition has been empirically tested in health economics and other sectors (such as transport, environment and marketing) in which DCE methods are applied. While an important component, we argue that the investigation of external validity should be much broader than a comparison of final outcomes. In doing so, we introduce a new and more comprehensive conceptualisation of external validity, closely linked to process validity, that moves us from the simple characterisation of a model as being or not being externally valid on the basis of predictive performance, to the concept that external validity should be an objective pursued from the initial conceptualisation and design of any DCE. We discuss how such a broader definition of external validity can be fruitfully used and suggest innovative ways in which it can be explored in practice. [ABSTRACT FROM AUTHOR]

Since the introduction of Discrete Choice Analysis, countless efforts have been made to enhance the efficiency of data collection through choice experiments and to improve the behavioural realism of choice models. One example development in data collection are best-worst discrete choice experiments (BWDCE), which have the benefit of obtaining a larger number of observations per respondent, allowing for reliably estimating choice models even with smaller samples. In SWDCE, respondents are asked to alternatingly select the 'best'/'worst' alternatives, until the choice set is exhausted. The use of BWDCE raises the question of decision-rule consistency through the stages of the experiment. We challenge the notion that the same fully compensatory decision rule is utilised throughout the experiment. We hypothesize that respondents may utilise one decision rule for selecting the 'best' and another for selecting the 'worst' alternatives. To test our hypothesis, we developed a model that combines the SBWMNL model for modelling best-worst data and the μRRM model that can account for variations in decision rules. Our results show that decision-rule heterogeneity does seem to be present in BWDCE: it is more likely that 'best' choices are made using a fully compensatory decision rule (maximising utility), whereas 'worst' choices are more likely made using a non-compensatory decision rule (minimising regret). Such behaviour is largely similar to how image theory describes the decision-making process in complex situations. Our findings give choice modellers new insight into the behaviour of respondents in best-worst experiments and allows them to represent their behaviour more accurately. • Adapted best-worst choice model that can account for decision rule heterogeneity. • Respondents do not use the same decision rule in best-worst choice experiments. • Successive choices in discrete choice experiments have a lower reliability. • The developed models do not produce significantly different parameter estimates. [ABSTRACT FROM AUTHOR]

Abstract: Stated-preference methods are a class of evaluation techniques for studying the preferences of patients and other stakeholders. While these methods span a variety of techniques, conjoint-analysis methods—and particularly discrete-choice experiments (DCEs)—have become the most frequently applied approach in health care in recent years. Experimental design is an important stage in the development of such methods, but establishing a consensus on standards is hampered by lack of understanding of available techniques and software. This report builds on the previous ISPOR Conjoint Analysis Task Force Report: Conjoint Analysis Applications in Health—A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. This report aims to assist researchers specifically in evaluating alternative approaches to experimental design, a difficult and important element of successful DCEs. While this report does not endorse any specific approach, it does provide a guide for choosing an approach that is appropriate for a particular study. In particular, it provides an overview of the role of experimental designs for the successful implementation of the DCE approach in health care studies, and it provides researchers with an introduction to constructing experimental designs on the basis of study objectives and the statistical model researchers have selected for the study. The report outlines the theoretical requirements for designs that identify choice-model preference parameters and summarizes and compares a number of available approaches for constructing experimental designs. The task-force leadership group met via bimonthly teleconferences and in person at ISPOR meetings in the United States and Europe. An international group of experimental-design experts was consulted during this process to discuss existing approaches for experimental design and to review the task force’s draft reports. In addition, ISPOR members contributed to developing a consensus report by submitting written comments during the review process and oral comments during two forum presentations at the ISPOR 16th and 17th Annual International Meetings held in Baltimore (2011) and Washington, DC (2012). [Copyright &y& Elsevier]

Abstract: This paper presents the first attempt to use a discrete choice experiment to derive distributional weights for quality adjusted life years (QALYs), based on characteristics (age and severity) of the beneficiaries. A novel approach using the Hicksian compensating variation is applied. Advantages include derivation of weights for QALYs, not just for life or life years saved, and investigation of the impact of the size of the health gain by allowing the gain to be traded against other characteristics. Results suggest one would generally not weight QALYs, except in a small number of specific cases and in those cases the weights are relatively small. Methodological challenges are highlighted as is a future research agenda. [Copyright &y& Elsevier]

Background: A discrete choice experiment (DCE) is a method used to elicit participants' preferences and the relative importance of different attributes and levels within a decision-making process. DCEs have become popular in healthcare; however, approaches to identify the attributes/levels influencing a decision of interest and to selection methods for their inclusion in a DCE are under-reported. Our objectives were: to explore the development process used to select/present attributes/levels from the identified range that may be influential; to describe a systematic and rigorous development process for design of a DCE in the context of thrombolytic therapy for acute stroke; and, to discuss the advantages of our five-stage approach to enhance current guidance for developing DCEs.Methods: A five-stage DCE development process was undertaken. Methods employed included literature review, qualitative analysis of interview and ethnographic data, expert panel discussions, a quantitative structured prioritisation (ranking) exercise and pilot testing of the DCE using a 'think aloud' approach.Results: The five-stage process reported helped to reduce the list of 22 initial patient-related factors to a final set of nine variable factors and six fixed factors for inclusion in a testable DCE using a vignette model of presentation.Conclusions: In order for the data and conclusions generated by DCEs to be deemed valid, it is crucial that the methods of design and development are documented and reported. This paper has detailed a rigorous and systematic approach to DCE development which may be useful to researchers seeking to establish methods for reducing and prioritising attributes for inclusion in future DCEs. [ABSTRACT FROM AUTHOR]

Objectives To improve information for patients and to facilitate a vaccination coverage that is in line with the EU and World Health Organization goals, we aimed to quantify how vaccination and patient characteristics impact on influenza vaccination uptake of elderly people. Methods An online discrete choice experiment (DCE) was conducted among 1261 representatives of the Dutch general population aged 60 years or older. In the DCE, we used influenza vaccination scenarios based on five vaccination characteristics: effectiveness, risk of severe side effects, risk of mild side effects, protection duration, and absorption time. A heteroscedastic multinomial logit model was used, taking scale and preference heterogeneity (based on 19 patient characteristics) into account. Results Vaccination and patient characteristics both contributed to explain influenza vaccination uptake. Assuming a base case respondent and a realistic vaccination scenario, the predicted uptake was 58%. One-way changes in vaccination characteristics and patient characteristics changed this uptake from 46% up to 61% and from 37% up to 95%, respectively. The strongest impact on vaccination uptake was whether the patient had been vaccinated last year, whether s/he had experienced vaccination side effects, and the patient’s general attitude towards vaccination. Conclusions Although vaccination characteristics proved to influence influenza vaccination uptake, certain patient characteristics had an even higher impact on influenza vaccination uptake. Policy makers and general practitioners can use these insights to improve their communication plans and information regarding influenza vaccination for individuals aged 60 years or older. For instance, physicians should focus more on patients who had experienced side effects due to vaccination in the past, and policy makers should tailor the standard information folder to patients who had been vaccinated last year and to patient who had not. [ABSTRACT FROM AUTHOR]

This exploratory study sought to investigate the effect of cognitive functioning on the consistency of individual responses to a discrete choice experiment (DCE) study conducted exclusively with older people. Methods A DCE to investigate preferences for multidisciplinary rehabilitation was administered to a consenting sample of older patients (aged 65 years and older) after surgery to repair a fractured hip (N = 84). Conditional logit, mixed logit, heteroscedastic conditional logit, and generalized multinomial logit regression models were used to analyze the DCE data and to explore the relationship between the level of cognitive functioning (specifically the absence or presence of mild cognitive impairment as assessed by the Mini-Mental State Examination) and preference and scale heterogeneity. Results Both the heteroscedastic conditional logit and generalized multinomial logit models indicated that the presence of mild cognitive impairment did not have a significant effect on the consistency of responses to the DCE. Conclusions This study provides important preliminary evidence relating to the effect of mild cognitive impairment on DCE responses for older people. It is important that further research be conducted in larger samples and more diverse populations to further substantiate the findings from this exploratory study and to assess the practicality and validity of the DCE approach with populations of older people. [ABSTRACT FROM AUTHOR]