On the Optimization of Bayesian D-Efficient Discrete Choice Experiment Designs for the Estimation of QALY Tariffs That Are Corrected for Nonlinear Time Preferences

Jonker, Marcel F.; Bliemer, Michiel C.J.

doi:10.1016/j.jval.2019.05.014

Cited by 11 publications

(8 citation statements)

References 23 publications

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“…More specifically, a Bayesian heterogeneous D-efficient DCE design with 10 subdesigns and 15 pairwise choice tasks per subdesign was optimized using the TPC-QALY software package. 22 This implied a simultaneous optimization of the efficiency of the 10 separate designs as well as the efficiency of the overall design. The D-efficiency criterion was calculated with 100 Bayesian draws, assuming an exponential discount function and based on the weighted average of the overall (i.e., combined) D-error (25% weight) and D-errors of the 10 subdesigns (75% weight).…”

Section: Methodsmentioning

confidence: 99%

Estimating a Preference-Based Value Set for the Mental Health Quality of Life Questionnaire (MHQoL)

van Krugten,

Jonker,

Himmler

et al. 2023

Med Decis Making

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Background Health economic evaluations using common health-related quality of life measures may fall short in adequately measuring and valuing the benefits of mental health care interventions. The Mental Health Quality of Life questionnaire (MHQoL) is a standardized, self-administered mental health–related quality of life instrument covering 7 dimensions known to be relevant across and valued highly by people with mental health problems. The aim of this study was to derive a Dutch value set for the MHQoL to facilitate its use in cost-utility analyses. Methods The value set was estimated using a discrete choice experiment (DCE) with duration that accommodated nonlinear time preferences. The DCE was embedded in a web-based self-complete survey and administered to a representative sample ( N = 1,308) of the Dutch adult population. The matched pairwise choice tasks were created using a Bayesian heterogeneous D-efficient design. The overall DCE design comprised 10 different subdesigns, with each subdesign containing 15 matched pairwise choice tasks. Each participant was asked to complete 1 of the subdesigns to which they were randomly assigned. Results The obtained coefficients indicated that “physical health,”“mood,” and “relationships” were the most important dimensions. All coefficients were in the expected direction and reflected the monotonic structure of the MHQoL, except for level 2 of the dimension “future.” The predicted values for the MHQoL ranged from −0.741 for the worst state to 1 for the best state. Conclusions This study derived a Dutch value set for the recently introduced MHQoL. This value set allows for the generation of an index value for all MHQoL states on a QALY scale and may hence be used in Dutch cost-utility analyses of mental healthcare interventions. Highlights A discrete choice experiment was used to derive a Dutch value set for the MHQoL. This allows the use of the MHQoL in Dutch cost-utility analyses. The dimensions physical health, mood, and relationships were the most important. The utility values range from −0.741 for the worst state to 1 for the best state.

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Section: Methodsmentioning

confidence: 99%

Estimating a Preference-Based Value Set for the Mental Health Quality of Life Questionnaire (MHQoL)

van Krugten,

Jonker,

Himmler

et al. 2023

Med Decis Making

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“…Indeed, heterogeneous designs allow for a greater variation in the attribute levels, that is, a higher number of choice sets, without increasing the cognitive burden for the respondents. Following Jonker and Bliemer (2019) and de Bekker‐Grob et al (2020), we generated a heterogeneous design consisting of 4 sub‐designs with 16 choice sets each (one sub‐design per each group defined by the four reference prices). Every sub‐design was divided into two blocks of eight choice tasks to further reduce the cognitive burden on respondents and to mitigate fatigue effects.…”

Section: Methodsmentioning

confidence: 99%

Wine consumers' demand for social sustainability labeling: Evidence for the fair labor claim

Piracci

Boncinelli

Casini

2022

Applied Eco Perspectives Pol

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This study aims to investigate consumer preferences for social sustainability labeling for wine. We explored the potential demand for a fair labor label that certifies wine produced through the fair treatment of workers in Italy, since the exploitation of migrant labor has become a preeminent issue in the country. We conducted a choice experiment on a sample of 500 consumers. Results indicated that they were willing to pay a considerable premium for wine produced by wineries that respects workers' rights and that there is a wide heterogeneity in consumer preferences for sustainability labeling according to the different dimensions underlying the label.

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“…9 Second, DCEs are generally considered to be cognitively burdensome to respondents, making them less than ideal for participants who have cognitive impairments. 17,18 In addition, they require relatively large sample sizes, 19 making them inappropriate for administration in, for example, rare disease populations. The combination of expert input and large sample size renders relatively high study costs and long study duration.…”

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confidence: 99%

“…The combination of expert input and large sample size renders relatively high study costs and long study duration. 18,19 Researchers as well as stakeholders who use preference information (i.e., representatives from the pharmaceutical industry and regulatory and reimbursement bodies) have expressed the need to compare DCEs to other, simpler methods. 22 This will help guide method selection for use in patient preference studies that are budget and or time sensitive, conducted in rare disease areas, and for which Marginal Rate of Substitution (MRS) or predicted uptake are not among the required outcome measures (e.g., prioritization of unmet medical needs or endpoint selection for clinical trials).…”

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Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance: A Case Study in Lung Cancer Patient Preferences

Veldwijk,

Smith,

Oliveri

et al. 2024

Med Decis Making

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Introduction Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR. Methods A total of 307 patients with non–small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen’s weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected. Results Most respondents (>65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC ( P < 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR ( P < 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53–0.55). Conclusion Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions. Highlights Both DCEs and SW tasks can be used to determine attribute relative importance rankings and weights; however, little evidence exists empirically comparing these methods in terms of outcomes or respondent usability. Most respondents found the DCE and SW tasks very easy or easy to understand and answer. A direct comparison of DCE and SW found significant differences in attribute importance rankings and weights as well as a greater spread in the DCE-derived attribute relative importance weights.

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On the Optimization of Bayesian D-Efficient Discrete Choice Experiment Designs for the Estimation of QALY Tariffs That Are Corrected for Nonlinear Time Preferences

Cited by 11 publications

References 23 publications

Estimating a Preference-Based Value Set for the Mental Health Quality of Life Questionnaire (MHQoL)

Estimating a Preference-Based Value Set for the Mental Health Quality of Life Questionnaire (MHQoL)

Wine consumers' demand for social sustainability labeling: Evidence for the fair labor claim

Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance: A Case Study in Lung Cancer Patient Preferences

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