Search

Background: There is much interest from stakeholders in understanding how health technology assessment (HTA) committees make national funding decisions for health technologies. A growing literature has analysed past decisions by committees (revealed preference, RP studies) and hypothetical decisions by committee members (stated preference, SP studies) to identify factors influencing decisions and assess their importance., Objectives: A systematic review of the literature was undertaken to provide insight into committee preferences for these factors (after controlling for other factors) and the methods used to elicit them., Methods: Ovid Medline, Embase, Econlit and Web of Science were searched from inception to 11 May 2017. Included studies had to have investigated factors considered by HTA committees and to have conducted multivariate analysis to identify the effect of each factor on funding decisions. Factors were classified as being important based on statistical significance, and their impact on decisions was compared using marginal effects., Results: Twenty-three RP and four SP studies (containing 42 analyses) of 14 HTA committees met the inclusion criteria. Although factors were defined differently, the SP literature generally found clinical efficacy, cost-effectiveness and equity factors (such as disease severity) were each important to the Pharmaceutical Benefits Advisory Committee (PBAC), the National Institute for Health and Care Excellence (NICE) and the All Wales Medicines Strategy Group. These findings were supported by the RP studies of the PBAC, but not the other committees, which found funding decisions by these and other committees were mostly influenced by the acceptance of the clinical evidence and, where applicable, cost-effectiveness. Trust in the evidence was very important for decision makers, equivalent to reducing the incremental cost-effectiveness ratio (cost per quality-adjusted life-year) by A$38,000 (Australian dollars) for the PBAC and £15,000 for NICE., Conclusions: This review found trust in the clinical evidence and, where applicable, cost-effectiveness were important for decision makers. Many methodological differences likely contributed to the diversity in some of the other findings across studies of the same committee. Further work is needed to better understand how competing factors are valued by different HTA committees.

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 regularly make 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.

In deciding pharmacotherapy treatment, doctors have to balance the risks and benefits of treatment, and their preferences may not always align with patient preferences. A pilot study to explore decision-making regarding treatment with antipsychotic medications among doctors and patients. A discrete choice experiment (DCE), comprised of systematically structured choice tasks, in which doctors and patients were asked to trade off between attributes of antipsychotic medications, each described in terms of mode of administration, effectiveness (on positive and negative symptoms) and side effect profiles. Participants also ranked different factors that they consider important when choosing an antipsychotic medication. 52 doctors and 49 patients completed the survey. Doctors accepted a higher risk of side effects than patients if it achieved better efficacy. Patients perceived long-acting injectables (LAIs) to be easier than taking tablets every day. Issues of embarrassment, pain and fear of needles were not rated as highly by patients, as anticipated by doctors. Doctors and patients demonstrated differences in decision-making about treatment with antipsychotic medications. Addressing these issues could facilitate shared decision-making, with the goal of improving patient adherence to antipsychotic medications, and thereby improve patient outcomes. [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]

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: A key objective of discrete choice experiments is to obtain sufficient quantity of high quality choice data to estimate choice models to be used to explore various policy/clinically relevant issues. This paper focuses on a relatively new form of choice experiment, ‘Best Worst Discrete Choice Experiments’ (BWDCEs) and their relevance to health research as a new way to meet such an objective. We explain what BWDCEs are, how and when to apply them and we present several analytical approaches to model the resulting data. We demonstrate this preference elicitation approach in an empirical application exploring preferences of 898 members of the general public in Edmonton and Calgary, Canada for treatment of cardiac arrest occurring in a public place and show the gains achieved compared to traditional analysis of first best data. We suggest that BWDCEs are a valuable way to investigate preferences in the health sector and discuss implications for task design, analysis and areas for future research. [Copyright &y& Elsevier]

Discrete choice experiments (DCEs) are regularly used in health economics to elicit preferences for healthcare products and programmes. There is growing recognition that DCEs can provide more than information on preferences and, in particular, they have the potential to contribute more directly to outcome measurement for use in economic evaluation. Almost uniquely, DCEs could potentially contribute to outcome measurement for use in both cost-benefit and cost-utility analysis. Within this expanding remit, our intention is to provide a resource for current practitioners as well as those considering undertaking a DCE, using DCE results in a policy/commercial context, or reviewing a DCE. We present the fundamental principles and theory underlying DCEs. To aid in undertaking and assessing the quality of DCEs, we discuss the process of carrying out a choice study and have developed a checklist covering conceptualizing the choice process, selecting attributes and levels, experimental design, questionnaire design, pilot testing, sampling and sample size, data collection, coding of data, econometric analysis, validity, interpretation and welfare and policy analysis. In this fast-moving area, a number of issues remain on the research frontier. We therefore outline potentially fruitful areas for future research associated both with DCEs in general, and with health applications specifically, paying attention to how the results of DCEs can be used in economic evaluation. We also discuss emerging research trends. We conclude that if appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically. Thus, they offer viable alternatives and complements to existing methods of valuation and preference elicitation. [ABSTRACT FROM AUTHOR]

The oft-applied assumption in the use of Quality Adjusted Life Years (QALYs) in economic evaluation, that all QALYs are valued equally, has been questioned from the outset. The literature has focused on differential values of a QALY based on equity considerations such as the characteristics of the beneficiaries of the QALYs. However, a key characteristic which may affect the value of a QALY is the type of QALY itself. QALY gains can be generated purely by gains in survival, purely by improvements in quality of life, or by changes in both. Using a discrete choice experiment and a new methodological approach to the derivation of relative weights, we undertake the first direct and systematic exploration of the relative weight accorded different QALY types and do so in the presence of equity considerations; age and severity. Results provide new evidence against the normative starting point that all QALYs are valued equally. [ABSTRACT FROM AUTHOR]

Health programs/services are often bundled, allowing for both substitution and complementarity. We adapt Discrete Choice Experiments to capture bundling, with application to a case study of exercise and nutrition; complementarity arises due to the goal of improving health. Our contributions are (1) to present a menu-based choice experiment to explore bundling; (2) to analyse the menu-based data using an extension of the choice set generation model (GenL) to account for correlations between bundles and component singles. A nationally representative sample of 333 Australians chose between a nutrition program only; exercise program only; both nutrition and exercise programs; or their status quo. Overall, we show that by incorporating the menu choice task and introducing the combined alternative, we capture a significant portion of the population seeking both exercise and nutrition components. We estimate a latent class GenL model, and identify two latent classes: Class 1 preferred to choose programs on offer, and Class 2 was more price sensitive and had a stronger preference for staying with their status quo. We show in the post-estimation analysis that heterogeneity in preferences translates into heterogeneity in the way alternatives are bundled, indicating that the combined offering is appealing to specific classes of individuals who prefer bundling. By implementing the menu choice task, researchers and policymakers can effectively identify, cater to and influence the demand for combined exercise and nutrition options, leading to more targeted and impactful interventions in promoting healthier lifestyle choices. • We propose a menu-based choice experiment to explore bundling for health-related options. • Bundling accounts for a significant portion of demand in a case study exploring choice of nutrition and exercise programs. • Ignoring bundling leads to an overstatement of demand for single alternatives, leading to misprediction of demand. • Combined offering is appealing to specific classes of individuals who prefer bundling. • We provide insights as to why policy interventions to improve uptake may not be effective for everybody. [ABSTRACT FROM AUTHOR]

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]