A new and improved experimental design for the discrete choice experiment module of the EQ-VT valuation protocol.

OBJECTIVES: The EuroQol Valuation Technology (EQ-VT) protocol, which is used to value the EQ-5D-5L instrument, comprises a composite time trade-off (cTTO) and a discrete choice experiment (DCE) module. Despite significant limitations, the DCE module has not been updated since its inception in 2012. This study aimed to update the EQ-VT DCE design using state-of-the-art methods. METHODS: DCE data from 19 EQ-5D-5L valuation studies were summarized using a Bayesian hierarchical meta-analysis model, which created the priors for our Bayesian efficient DCE design. This design comprised 20 sub-designs, each with 12 choice tasks, and included 2 levels that overlapped to reduce the complexity of the choice tasks. The relative efficiency and robustness of the new design were established by comparing the D-errors and minimal sample size requirements for the 19 within-sample and 7 out-of-sample countries with the previous DCE design. RESULTS: The updated DCE design shows large reductions in the D-error: by 20% and 22% for the 19 within-sample and 7 out-of-sample countries, respectively. Sample size requirements were also reduced, resulting in an average reduction of 45% for both the within and out-of-sample countries. CONCLUSIONS: The updated DCE design outperforms the current EQ-VT design. Given its enhanced performance and reduced complexity, it is set to replace the existing DCE design in future EQ-5D-5L valuation studies using the EQ-VT protocol.

The EQ-5D-5L valuation study for Trinidad and Tobago.

PURPOSE: The 2016 EQ-5D-3L value set for Trinidad and Tobago (T&T) allows for the calculation of EQ-5D-5L values via the crosswalk algorithm. The 2016 value set was based on methods predating the EQ-VT protocol, now considered the gold standard for developing EQ-5D value sets. Furthermore, direct elicitation of EQ-5D-5L is preferred over crosswalked values. This study aimed to produce an EQ-5D-5L value set for T&T. METHODS: A representative sample (age, sex, geography) of adults each completed 10 composite Time Trade-Off (cTTO) tasks and 12 Discrete Choice Experiment (DCE) tasks in face-to-face interviews. The cTTO data were analyzed using a Tobit model that corrects for heteroskedasticity. DCE data were analyzed using a mixed logit model. The cTTO and DCE data were combined in hybrid models. RESULTS: One thousand and seventy-nine adults completed the valuation interviews. Among the modelling approaches that were explored, the hybrid heteroskedastic Tobit model produced all internally consistent, statistically significant coefficients, and performed best in terms of out-of-sample predictivity for single states. Compared to the existing EQ-5D-5L crosswalk set, the new value set had a higher number of negative values (236 or 7.6% versus 21 or 0.7%). The mean absolute difference was 0.157 and the correlation coefficient between the two sets was 0.879. CONCLUSION: This study provides a value set for the EQ-5D-5L for T&T using the EQ-VT protocol. We recommend this value set for QALY computations relating to T&T.

A Direct Comparison Between Discrete Choice With Duration and Composite Time Trade-Off Methods: Do They Produce Similar Results?

OBJECTIVES: Discrete choice experiments including a duration attribute (DCEd) represent a promising candidate method for valuing health-related quality-of-life instruments. However, it has not been established that DCEd can produce similar results as composite time trade-off (cTTO) or EuroQol Valuation Technology (EQ-VT) valuations of the EQ-5D-5L instrument. This study provides a direct comparison between cTTO and EQ-VT, and DCEd valuation methods. METHODS: An EQ-VT study was conducted in Trinidad and Tobago to value the EQ-5D-5L. 1079 respondents each completed 10 cTTO tasks and 12 discrete choice experiments tasks without a duration attribute. A separate sample of 970 respondents each completed 18 split-triplet DCEd tasks. Several regression models were applied to the EQ-VT data, and the DCEd data were analyzed using mixed logit models with an exponential discount rate. The estimated values were compared using scatterplots and Bland-Altman plots. RESULTS: The ordering of dimensions was identical in level 5 for cTTO/EQ-VT and DCEd models, with pain/discomfort being the most important dimension and usual activities being least important. cTTO/EQ-VT models produced a value for state 55555 ranging between -0.52 and -0.69, whereas this was -0.543 for the nonlinear mixed logit model for the DCEd data. Scatterplots and Bland-Altman plots suggested excellent agreement between cTTO/EQ-VT and DCEd-based estimates. CONCLUSIONS: CTTO/EQ-VT and DCEd valuations produce similar results when correcting DCEd for nonlinear time preferences. The ordering of importance of the dimensions and scale are identical, suggesting that the 2 methods measure the same construct and produce similar results.

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

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.

An investigation of age dependency in Dutch and Chinese values for EQ-5D-Y.

Aims: The primary aim was to explore the age dependency of health state values derived via trade-offs between health-related quality of life (HRQoL) and life years in a discrete choice experiment (DCE). The secondary aim was to explore if people weigh life years and HRQoL differently for children, adolescents, adults, and older adults. Methods: Participants from the general population of the Netherlands and China first completed a series of choice tasks offering choices between two EQ-5D-Y states with a given lifespan. The choice model captured the value of a year in full health, disutility determined by EQ-5D-Y, and a discount rate. Next, they received a slightly different choice task, offering choices between two lives that differed in HRQoL and life expectancy but produced the same number of quality-adjusted life years (QALYs). Participants were randomly assigned to fill out the survey for three or four age frames: a hypothetical person of 10, 15, 40, and 70 years (the last one only applicable to China) to allow the age dependency of the responses to be explored. Results: A total of 1,234 Dutch and 1,818 Chinese people administered the survey. Controlling for time preferences, we found that the agreement of health state values for different age frames was generally stronger in the Netherlands than in China. We found no clear pattern of differences in the QALY composition in both samples. The probability distribution over response options varied most when levels for lifespan or severity were at the extremes of the spectrum. Conclusion/discussion: The magnitude and direction of age effects on values seemed dimension- and country specific. In the Netherlands, we found a few differences in dimension-specific weights elicited for 10- and 15-year-olds compared to 40-year-olds, but the overall age dependency of values was limited. A stronger age dependency of values was observed in China, where values for 70-year-olds differed strongly from the values for other ages. The appropriateness of using existing values beyond the age range for which they were measured needs to be evaluated in the local context.

Interaction Effects in Health State Valuation Studies: An Optimal Scaling Approach.

OBJECTIVES: This study aimed to introduce a parsimonious modeling approach that enables the estimation of interaction effects in health state valuation studies. METHODS: Instead of supplementing a main-effects model with interactions between each and every level, a more parsimonious optimal scaling approach is proposed. This approach is based on the mapping of health state levels onto domain-specific continuous scales. The attractiveness of health states is then determined by the importance-weighted optimal scales (ie, main effects) and the interactions between these domain-specific scales (ie, interaction effects). The number of interaction terms only depends on the number of health domains. Therefore, interactions between dimensions can be included with only a few additional parameters. The proposed models with and without interactions are fitted on 3 valuation data sets from 2 different countries, that is, a Dutch latent-scale discrete choice experiment (DCE) data set with 3699 respondents, an Australian time trade-off data set with 400 respondents, and a Dutch DCE with duration data set with 788 respondents. RESULTS: Important interactions between health domains were found in all 3 applications. The results confirm that the accumulation of health problems within health states has a decreasing marginal effect on health state values. A similar effect is obtained when so-called N3 or N5 terms are included in the model specification, but the inclusion of 2-way interactions provides superior model fits. CONCLUSIONS: The proposed interaction model is parsimonious, produces estimates that are straightforward to interpret, and accommodates the estimation of interaction effects in health state valuation studies with realistic sample size requirements. Not accounting for interactions is shown to result in biased value sets, particularly in stand-alone DCE with duration studies.

The Garbage Class Mixed Logit Model: Accounting for Low-Quality Response Patterns in Discrete Choice Experiments.

OBJECTIVES: To introduce the garbage class mixed logit (MIXL) model as a convenient alternative to manually screening and accounting for respondents with low data quality in discrete choice experiments. METHODS: Garbage classes are typically used in latent class logit analyses to designate or identify group(s) of respondents with low data quality. Yet, the same concept can be applied to MIXL models as well. RESULTS: Based on a reanalysis of 4 discrete choice experiments that were originally analyzed using a standard MIXL model, it is shown that garbage class MIXL models can achieve the same effect as manually screening for (and excluding) respondents with low data quality based on the more commonly used root likelihood test, but with less effort and ambiguity. CONCLUSIONS: Including a garbage class in MIXL models removes the influence of respondents with a random choice pattern from the MIXL model estimates, provides an estimate of the number of low-quality respondents in the dataset, and avoids having to manually screen for respondents with low data quality based on internal or statistical validity tests. Although less versatile than the combination of standard MIXL estimates with separate assessments of data quality and sensitivity analyses, the proposed garbage class MIXL model provides an attractive alternative.

Heterogeneity in preferences for outcomes of integrated care for persons with multiple chronic diseases: a latent class analysis of a discrete choice experiment.

PURPOSE: For an integrated care programme to be successful, preferences of the stakeholders involved should be aligned. The aim of this study is to investigate to which extent outcomes beyond health are valued and to study the heterogeneity of preferences of those involved in integrated care. METHODS: A discrete choice experiment (DCE) was conducted to elicit preferences for eight Triple Aim outcomes, i.e., physical functioning, psychological well-being, social relationships & participation, enjoyment of life, resilience, person-centeredness, continuity of care and total health and social care costs. Stakeholders were recruited among Dutch persons with multi-morbidity, informal caregivers, professionals, payers, and policymakers. A Bayesian mixed-logit model was used to analyse the data. Subsequently, a latent class analysis was performed to identify stakeholders with similar preferences. RESULTS: 739 stakeholders completed the DCE. Enjoyment of life was perceived as the most important outcome (relative importance: 0.221) across stakeholders, while total health and social care costs were perceived as least important (0.063). The latent class analysis identified four classes. The first class (19.9%) put most weight on experience with care outcomes. The second class (39%) favoured enjoyment of life. The third class (18%) focused relatively more on physical health. The fourth class (24%) had the least consistent preferences. CONCLUSION: This study has highlighted the heterogeneity in views of stakeholders in integrated care on what is important in health(care) for persons with multi-morbidity. To accurately value integrated care a variety of outcomes beyond health-e.g., enjoyment of life and experience with care-should be taken into account.

The Sensitivity and Specificity of Repeated and Dominant Choice Tasks in Discrete Choice Experiments.

OBJECTIVES: This study aimed to identify the most commonly used internal validity tests in the discrete choice experiment (DCE) literature and establish their sensitivity and specificity. METHODS: A structured literature review of recent DCE articles (2018-2020Q1) published in the health, marketing, transport economics, and environmental science literature was used to identify commonly used internal validity tests. The 2 most frequently used internal validity tests were incorporated in 4 new data collections. Respondent preferences in each application were summarized using a mixed logit model, which served as the benchmark for the subsequent sensitivity and specificity calculations. The performance of the internal validity tests was also compared with that of the root likelihood (RLH) test, which is a likelihood-based statistical validity test that is commonly used in marketing applications. RESULTS: Dominant and repeated choice tasks were most commonly included in health-related DCE designs. Based on 4 applications, their specificity and sensitivity depend on the type of incorrect response pattern to be detected and on design characteristics such as the number of choice options per choice task and the number of internal validity tests as included in the experimental design. In all but one scenario, the performance of the dominant and repeated choice tasks was considerably worse than that of the RLH test. CONCLUSIONS: Dominant and repeated choice tasks are unreliable screening tests and costly in terms of statistical power. The RLH test, which is a statistical test that does not require additional choice tasks to be included in the DCE design, provides a more reliable alternative.

Estimating an anchored utility tariff for the well-being of older people measure (WOOP) for the Netherlands.

OBJECTIVE: Health economic evaluations using common health-related quality of life measures may fall short in adequately incorporating all relevant benefits of health and social care interventions targeted at older people. The Well-being of Older People measure (WOOP) is a broader well-being measure that comprises nine well-being domains. The objective of this study was to estimate a utility tariff for the WOOP, to facilitate its application in cost-utility analyses. METHODS: A discrete choice experiment (DCE) with duration approach was set up and fielded among 2,012 individuals from the Netherlands aged 65 years and above. Matched pairwise choice tasks, colour-coding and level overlap were used to reduce the cognitive burden of the DCE. The choice tasks were created using a Bayesian heterogeneous D-efficient design. The estimation procedure accommodated for nonlinear time preferences via an exponential discounting function. RESULTS: The estimation results showed that 'physical health', 'mental health', and 'making ends meet' were the most important well-being domains for older people, followed by 'independence' and 'living situation'. Of somewhat lesser importance were domains like 'social life', 'receiving support' and 'feeling useful'. The generated utility tariffs can be used to translate well-being states described with the WOOP to a utility score between -0.616 and 1. CONCLUSIONS: This study established a tariff for the WOOP, which will facilitate its use in economic evaluations of health and social care interventions targeted at older people, first of all in the Netherlands.

Not all respondents use a multiplicative utility function in choice experiments for health state valuations, which should be reflected in the elicitation format (or statistical analysis).

Discrete choice experiments (DCEs) that include health states and duration are becoming a common method for estimating quality-adjusted life year (QALY) tariffs. These DCEs need to be analyzed under the assumption that respondents treat health and duration multiplicatively. However, in the most commonly used DCE duration format there is no guarantee that respondents actually do so; in fact, respondents can easily simplify the choice tasks by considering health and duration separately. This would result in valid DCE responses but preclude subsequent QALY tariff calculations. Using a Bayesian latent class model and data from two existing valuation studies, our analyses confirm that in both datasets the majority of respondents do not appear to have used a multiplicative utility function. Moreover, a statistical correction for respondents who used an incorrect function changes the range of the QALY weights. Hence our results imply that one can neither assume that respondents use the theoretically required multiplicative utility function nor assume that the type of utility function that respondents use does not affect the estimated QALY weights. As a solution, we advise researchers to use an alternative, more constrained DCE elicitation format that avoids these behavioral problems.

Reduction of GTV to high-risk CTV radiation margin in head and neck squamous cell carcinoma significantly reduced acute and late radiation-related toxicity with comparable outcomes.

BACKGROUND AND PURPOSE: We aim to retrospectively investigate whether reducing GTV to high-risk CTV margin will significantly reduce acute and late toxicity without jeopardizing outcome in head-and-neck squamous cell carcinoma (HNSCC) treated with definitive (chemo)radiation. MATERIALS AND METHODS: Between April 2015 and April 2019, 155 consecutive patients were treated with GTV to high-risk CTV margin of 10 mm and subsequently another 155 patients with 6 mm margin. The CTV-PTV margin was 3 mm for both groups. All patients were treated with volumetric-modulated arc therapy with daily image-guidance using cone-beam CT. End points of the study were acute and late toxicity and oncologic outcomes. RESULTS: Overall acute grade 3 toxicity was significantly lower in 6 mm, compared to 10 mm group (48% vs. 67%, respectively, p < 0.01). The same was true for acute grade 3 mucositis (18% vs. 34%, p < 0.01) and grade ≥ 2 dysphagia (67% vs. 85%, p < 0.01). Also feeding tube-dependency at the end of treatment (25% vs. 37%, p = 0.02), at 3 months (12% and 25%, p < 0.01), and at 6 months (6% and 15%, p = 0.01) was significantly less in 6 mm group. The incidence of late grade 2 xerostomia was also significantly lower in the 6 mm group (32% vs. 50%, p < 0.01). The 2-year rates of loco-regional control, disease-free and overall survival were 78.7% vs. 73.1%, 70.6% vs. 61.4%, and 83.2% vs. 74.4% (p > 0.05, all). CONCLUSION: The first study reporting on reduction of GTV to high-risk CTV margin from 10 to 6 mm showed significant reduction of the incidence and severity of radiation-related toxicity without reducing local-regional control and survival.

What Factors Influence Non-Participation Most in Colorectal Cancer Screening? A Discrete Choice Experiment.

BACKGROUND AND OBJECTIVE: Non-participation in colorectal cancer (CRC) screening needs to be decreased to achieve its full potential as a public health strategy. To facilitate successful implementation of CRC screening towards unscreened individuals, this study aimed to quantify the impact of screening and individual characteristics on non-participation in CRC screening. METHODS: An online discrete choice experiment partly based on qualitative research was used among 406 representatives of the Dutch general population aged 55-75 years. In the discrete choice experiment, respondents were offered a series of choices between CRC screening scenarios that differed on five characteristics: effectiveness of the faecal immunochemical screening test, risk of a false-negative outcome, test frequency, waiting time for faecal immunochemical screening test results and waiting time for a colonoscopy follow-up test. The discrete choice experiment data were analysed in a systematic manner using random-utility-maximisation choice processes with scale and/or preference heterogeneity (based on 15 individual characteristics) and/or random intercepts. RESULTS: Screening characteristics proved to influence non-participation in CRC screening (21.7-28.0% non-participation rate), but an individual's characteristics had an even higher impact on CRC screening non-participation (8.4-75.5% non-participation rate); particularly the individual's attitude towards CRC screening followed by whether the individual had participated in a cancer screening programme before, the decision style of the individual and the educational level of the individual. Our findings provided a high degree of confidence in the internal-external validity. CONCLUSIONS: This study showed that although screening characteristics proved to influence non-participation in CRC screening, a respondent's characteristics had a much higher impact on CRC screening non-participation. Policy makers and physicians can use our study insights to improve and tailor their communication plans regarding (CRC) screening for unscreened individuals.

Comparing patients' and other stakeholders' preferences for outcomes of integrated care for multimorbidity: a discrete choice experiment in eight European countries.

OBJECTIVES: To measure relative preferences for outcomes of integrated care of patients with multimorbidity from eight European countries and compare them to the preferences of other stakeholders within these countries. DESIGN: A discrete choice experiment (DCE) was conducted in each country, asking respondents to choose between two integrated care programmes for persons with multimorbidity. SETTING: Preference data collected in Austria (AT), Croatia (HR), Germany (DE), Hungary (HU), the Netherlands (NL), Norway (NO), Spain (ES), and UK. PARTICIPANTS: Patients with multimorbidity, partners and other informal caregivers, professionals, payers and policymakers. MAIN OUTCOME MEASURES: Preferences of participants regarding outcomes of integrated care described as health/well-being, experience with care and cost outcomes, that is, physical functioning, psychological well-being, social relationships and participation, enjoyment of life, resilience, person-centredness, continuity of care and total costs. Each outcome had three levels of performance. RESULTS: 5122 respondents completed the DCE. In all countries, patients with multimorbidity, as well as most other stakeholder groups, assigned the (second) highest preference to enjoyment of life. The patients top-three most frequently included physical functioning, psychological well-being and continuity of care. Continuity of care also entered the top-three of professionals, payers and policymakers in four countries (AT, DE, HR and HU). Of the five stakeholder groups, preferences of professionals differed most often from preferences of patients. Professionals assigned lower weights to physical functioning in AT, DE, ES, NL and NO and higher weights to person-centredness in AT, DE, ES and HU. Payers and policymakers assigned higher weights than patients to costs, but these weights were relatively low. CONCLUSION: The well-being outcome enjoyment of life is the most important outcome of integrated care in multimorbidity. This calls for a greater involvement of social and mental care providers. The difference in opinion between patients and professionals calls for shared decision-making, whereby efforts to improve well-being and person-centredness should not divert attention from improving physical functioning.

COVID-19 Contact Tracing Apps: Predicted Uptake in the Netherlands Based on a Discrete Choice Experiment.

BACKGROUND: Smartphone-based contact tracing apps can contribute to reducing COVID-19 transmission rates and thereby support countries emerging from lockdowns as restrictions are gradually eased. OBJECTIVE: The primary objective of our study is to determine the potential uptake of a contact tracing app in the Dutch population, depending on the characteristics of the app. METHODS: A discrete choice experiment was conducted in a nationally representative sample of 900 Dutch respondents. Simulated maximum likelihood methods were used to estimate population average and individual-level preferences using a mixed logit model specification. Individual-level uptake probabilities were calculated based on the individual-level preference estimates and subsequently aggregated into the sample as well as subgroup-specific contact tracing app adoption rates. RESULTS: The predicted app adoption rates ranged from 59.3% to 65.7% for the worst and best possible contact tracing app, respectively. The most realistic contact tracing app had a predicted adoption of 64.1%. The predicted adoption rates strongly varied by age group. For example, the adoption rates of the most realistic app ranged from 45.6% to 79.4% for people in the oldest and youngest age groups (ie, ≥75 years vs 15-34 years), respectively. Educational attainment, the presence of serious underlying health conditions, and the respondents' stance on COVID-19 infection risks were also correlated with the predicted adoption rates but to a lesser extent. CONCLUSIONS: A secure and privacy-respecting contact tracing app with the most realistic characteristics can obtain an adoption rate as high as 64% in the Netherlands. This exceeds the target uptake of 60% that has been formulated by the Dutch government. The main challenge will be to increase the uptake among older adults, who are least inclined to install and use a COVID-19 contact tracing app.

Summarizing Patient Preferences for the Competitive Landscape of Multiple Sclerosis Treatment Options.

Objective. Quantitatively summarize patient preferences for European licensed relapsing-remitting multiple sclerosis (RRMS) disease-modifying treatment (DMT) options. Methods. To identify and summarize the most important RRMS DMT characteristics, a literature review, exploratory physician interviews, patient focus groups, and confirmatory physician interviews were conducted in Germany, the United Kingdom, and the Netherlands. A discrete choice experiment (DCE) was developed and executed to measure patient preferences for the most important DMT characteristics. The resulting DCE data (n=799 and n=363 respondents in the United Kingdom and Germany, respectively) were analyzed using Bayesian mixed logit models. The estimated individual-level patient preferences were subsequently summarized using 3 additional analyses: the quality of the choice data was assessed using individual-level R2 estimates, individual-level preferences for the available DMTs were aggregated into DMT-specific preference shares, and a principal component analysis was performed to explain the patients' choice process. Results. DMT usage differed between RRMS patients in Germany and the United Kingdom but aggregate patient preferences were similar. Across countries, 42% of all patients preferred oral medications, 38% infusions, 16% injections, and 4% no DMT. The most often preferred DMT was natalizumab (26%) and oral DMT cladribine tablets (22%). The least often preferred were mitoxantrone and the beta-interferon injections (1%-3%). Patient preferences were strongly correlated with patients' MS disease duration and DMT experience, and differences in patient preferences could be summarized using 8 principle components that together explain 99% of the variation in patients' DMT preferences. Conclusion. This study summarizes patient preferences for the included DMTs, facilitates shared decision making along the dimensions that are relevant to RRMS patients, and introduces methods in the medical DCE literature that are ideally suited to summarize the impact of DMT introductions in preexisting treatment landscapes.

Population preferences for breast cancer screening policies: Discrete choice experiment in Belarus.

BACKGROUND: Reaching an acceptable participation rate in screening programs is challenging. With the objective of supporting the Belarus government to implement mammography screening as a single intervention, we analyse the main determinants of breast cancer screening participation. METHODS: We developed a discrete choice experiment using a mixed research approach, comprising a literature review, in-depth interviews with key informants (n = 23), "think aloud" pilots (n = 10) and quantitative measurement of stated preferences for a representative sample of Belarus women (n = 428, 89% response rate). The choice data were analysed using a latent class logit model with four classes selected based on statistical (consistent Akaike information criterion) and interpretational considerations. RESULTS: Women in the sample were representative of all six geographic regions, mainly urban (81%), and high-education (31%) characteristics. Preferences of women in all four classes were primarily influenced by the perceived reliability of the test (sensitivity and screening method) and costs. Travel and waiting time were important components in the decision for 34% of women. Most women in Belarus preferred mammography screening to the existing clinical breast examination (90%). However, if the national screening program is restricted in capacity, this proportion of women will drop to 55%. Women in all four classes preferred combined screening (mammography with clinical breast examination) to single mammography. While this preference was stronger if lower test sensitivity was assumed, 28% of women consistently gave more importance to combined screening than to test sensitivity. CONCLUSION: Women in Belarus were favourable to mammography screening. Population should be informed that there are no benefits of combined screening compared to single mammography. The results of this study are directly relevant to policy makers and help them targeting the screening population.

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models.

BACKGROUND: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. OBJECTIVES: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. METHODS: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. RESULTS: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). CONCLUSIONS: Our study shows that DCEs are able to predict choices-mimicking real-world decisions-if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts.

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

OBJECTIVES: This article explains how to optimize Bayesian D-efficient discrete choice experiment (DCE) designs for the estimation of quality-adjusted life year (QALY) tariffs that are unconfounded by respondents' time preferences. METHODS: The calculation of Bayesian D-errors is explained for DCE designs that allow for the disentanglement of respondents' time and health-state preferences. Time preferences are modelled via an exponential, hyperbolic, or power discount function and the performance of the proposed DCE designs is compared with that of several conventional DCE designs that do not take nonlinear time preferences into account. RESULTS: Based on the achieved D-error, asymptotic standard error, and estimated sample size to obtain statistically significant estimates of the discount rate parameters, the proposed designs outperform the conventional DCE designs. CONCLUSIONS: We recommend that applied researchers use appropriately optimized DCE designs for the estimation of QALY tariffs that are corrected for time preferences. The TPC-QALY software package that accompanies this article makes the recommended designs easily accessible for health-state valuation researchers.

The Fold-in, Fold-out Design for DCE Choice Tasks: Application to Burden of Disease.

Background In discrete-choice experiments (DCEs), choice alternatives are described by attributes. The importance of each attribute can be quantified by analyzing respondents' choices. Estimates are valid only if alternatives are defined comprehensively, but choice tasks can become too difficult for respondents if too many attributes are included. Several solutions for this dilemma have been proposed, but these have practical or theoretical drawbacks and cannot be applied in all settings. The objective of the current article is to demonstrate an alternative solution, the fold-in, fold-out approach (FiFo). We use a motivating example, the ABC Index for burden of disease in chronic obstructive pulmonary disease (COPD). Methods Under FiFo, all attributes are part of all choice sets, but they are grouped into domains. These are either folded in (all attributes have the same level) or folded out (levels may differ). FiFo was applied to the valuation of the ABC Index, which included 15 attributes. The data were analyzed in Bayesian mixed logit regression, with additional parameters to account for increased complexity in folded-out questionnaires and potential differences in weight due to the folding status of domains. As a comparison, a model without the additional parameters was estimated. Results Folding out domains led to increased choice complexity for respondents. It also gave domains more weight than when it was folded in. The more complex regression model had a better fit to the data than the simpler model. Not accounting for choice complexity in the models resulted in a substantially different ABC Index. Conclusion Using a combination of folded-in and folded-out attributes is a feasible approach for conducting DCEs with many attributes.