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BACKGROUND AND OBJECTIVES: Case 2 best-worst scaling (BWS-2) is an increasingly popular method to elicit patient preferences. Because BWS-2 potentially has a lower cognitive burden compared with discrete choice experiments, the aim of this study was to compare treatment preference weights and relative importance scores.METHODS: Patients with neuromuscular diseases completed an online survey at two different moments in time, completing one method per occasion. Patients were randomly assigned to either first a discrete choice experiment or BWS-2. Attributes included: muscle strength, energy endurance, balance, cognition, chance of blurry vision, and chance of liver damage. Multinomial logit was used to calculate overall relative importance scores and latent class logit was used to estimate heterogeneous preference weights and to calculate the relative importance scores of the attributes for each latent class.RESULTS: A total of 140 patients were included for analyses. Overall relative importance scores showed differences in attribute importance rankings between a discrete choice experiment and BWS-2. Latent class analyses indicated three latent classes for both methods, with a specific class in both the discrete choice experiment and BWS-2 in which (avoiding) liver damage was the most important attribute. Ex-post analyses showed that classes differed in sex, age, level of education, and disease status. The discrete choice experiment was easier to understand compared with BWS-2.CONCLUSIONS: This study showed that using a discrete choice experiment and BWS-2 leads to different outcomes, both in preference weights as well as in relative importance scores, which might have been caused by the different framing of risks in BWS-2. However, a latent class analysis revealed similar latent classes between methods. Careful consideration about method selection is required, while keeping the specific decision context in mind and pilot testing the methods.

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.

This study undertook a head-to-head comparison of best-worst, best-best and ranking discrete choice experiments (DCEs) to help decide which method to use if moving beyond traditional single-best DCEs. Respondents were randomized to one of three preference elicitation methods. Rank-ordered (exploded) mixed logit models and respondent-reported data were used to compare methods and first and second choices. First choices differed from second choices and preferences differed between elicitation methods, even beyond scale and scale dynamics. First choices of best-worst had good choice consistency, scale dynamics and statistical efficiency, but this method's second choices performed worst. Ranking performed best on respondent-reported difficulty and preference; best-best's second choices on statistical efficiency. All three preference elicitation methods improve efficiency of data collection relative to using first choices only. However, differences in preferences between first and second choices challenge moving beyond single-best DCE. If nevertheless doing so, best-best and ranking are preferred over best-worst DCE.

Accurately predicting the next shopping basket of a customer is important for retailers, as it offers an opportunity to serve customers with personalized product recommendations or shopping lists. The goal of next-basket prediction is to predict a coherent set of products that the customer will buy next, rather than just a single product. However, if the assortment of the retailer contains thousands of products, the number of possible baskets becomes extremely large and most standard choice models can no longer be applied. Therefore, we propose the use of a gated recurrent unit (GRU) network for next-basket prediction in this study, which is easily scalable to large assortments. Our proposed model is able to capture dynamic customer taste, recurrency in purchase behavior and frequent product co-occurrences in shopping baskets. Moreover, it allows for the inclusion of additional covariates. Using two real-life datasets, we demonstrate that our model is able to outperform both naive benchmarks and a state-of-the-art next-basket prediction model on several performance measures. We also illustrate that the model learns meaningful patterns about the retailer's assortment structure.

Digital technology in financial services is helping consumers gain wider access to investment funds, acquire these funds at lower costs, and customize their own investments. However, direct digital access also creates new challenges because consumers may make suboptimal investment decisions. We address the challenge that consumers often face complex investment decisions involving multiple funds. Normative optimal asset allocation theory prescribes that investors should simultaneously optimize risk–returns over their entire portfolio. We propose two behavioral effects (mental separation and correlation neglect) that prevent consumers from doing so and a new choice architecture of virtually integrating investment funds that can help overcome these effects. Results from three experiments, using general population samples, provide support for the predicted behavioral effects and the beneficial impact of virtual integration. We find that consumers’ behavioral biases are not overcome by financial literacy, which further underlines the marketing relevance of this research.

Objective: To empirically test the impact of allowing respondents time to think (TTT) about their choice options on the outcomes of a discrete choice experiments (DCE). Methods: In total, 613 participants of the Swedish CArdioPulmonary bioImage Study (SCAPIS) completed a DCE questionnaire that measured their preferences for receiving secondary findings of a genetic test. A Bayesian D-efficient design with 60 choice tasks divided over 4 questionnaires was used. Each choice task contained 2 scenarios with 4 attributes: type of disease, disease penetrance probability, preventive opportunities, and effectiveness of prevention. Respondents were randomly allocated to the TTT or no TTT (NTTT) sample. Latent class models (LCMs) were estimated to determine attribute-level values and their relative importance. In addition, choice certainty, attribute-level interpretation, choice consistency, and potential uptake rates were compared between samples. Results: In the TTT sample, 92% of the respondents (245 of 267) indicated they used the TTT period to (1) read the information they received (72%) and (2) discuss with their family (24%). In both samples, respondents were very certain about their choices. A 3-class LCM was fitted for both samples. Preference reversals were found for 3 of the 4 attributes in one class in the NTTT sample (34% class-membership probability). Relative importance scores of the attributes differed between the 2 samples, and significant scale effects indicating higher choice consistency in TTT sample were found. Conclusions: Offering respondents TTT influences decision making and preferences. Developers of future DCEs regarding complex health-related decisions are advised to consider this approach to enhance the validity of the elicited preferences.

Financial advisors seek to accurately measure individuals’ risk preferences and provide sound personalized investment advice. Both advice tasks are increasingly offered through automated online technologies. Little is known, however, about what drives individuals’ acceptance of such automated financial advice and, from a consumer point of view, which firms may be best positioned to provide such advice. We generate novel insights on these questions by conducting a real-world empirical study using an interactive automated online tool that employs an innovative computer algorithm to build pension investment profiles, the “Pension Builder,” and a large, representative sample. We focus on the role that two key firm characteristics have on consumer acceptance of pension investment advice generated by computer algorithms running on automated interactive online tools: profit orientation and role in the sales channel. We find that consumers’ perceptions of trust and expertise of the firm providing the automated advice are important drivers of advice acceptance (besides a strong impact of the satisfaction with the consumer–online tool interaction), and that these constructs themselves are clearly influenced by the for-profit vs. not-for-profit orientation and the product provider vs. advisor only role in the sales channel of the firm providing the advice. We discuss the implications of our findings for marketers and policy makers and provide suggestions for future research.

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 R2estimates, 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.

© 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research 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.

Many consumer decisions are assisted by product recommendations. When retailers provide such recommendations, there is an inherent tension between (1) presenting a set of products that are close in attractiveness (fine product set granularity) and (2) presenting a wider range of products that are more different in attractiveness (coarse product set granularity). While the former can maximize the attractiveness of the recommended set of products, the latter makes it easier for consumers to determine which of the recommended products is most attractive, thus boosting consumer response. Evidence from a large-scale field study (with naturally occurring variation in the granularity of online recommendation sets) provides strong support for this tension and shows that less fine-grained product recommendation sets promote consumer response. We also find that, in line with our theorizing, coarser set granularity increases the time consumers spend processing detailed information about individual products relative to time they spend comparing products at the set level. These effects are less pronounced when consumer engagement in the decision process is low. The key insights from the field study are replicated in a tightly controlled experiment (using a different product domain). The findings of this research have important implications for how best to integrate large online assortments and product recommendations to stimulate consumer response.

This multi-method study aims to shed light on digital platforms' decisions regarding their openness. Platform openness results from a series of decisions on how open a platform is regarding: (a) suppliers, (b) customers, (c) complementary service providers, as well as to (d) product categories and (e) channels. By conducting a scoping literature review, we analyze the current body of knowledge about the drivers, dimensions and outcomes of platform openness. Using an expert panel discussion and analysis of real-world digital platforms, we confront this existing knowledge with current business challenges to identify research challenges. We address how future research can advance platform research by tackling these challenges.

In modern retail contexts, retailers sell products from vast product assortments to a large and heterogeneous customer base. Understanding purchase behavior in such a context is very important. Standard models cannot be used because of the high dimensionality of the data. We propose a new model that creates an efficient dimension reduction through the idea of purchase motivations. We only require customer-level purchase history data, which is ubiquitous in modern retailing. The model handles large-scale data and even works in settings with shopping trips consisting of few purchases. Essential features of our model are that it accounts for the product, customer, and time dimensions present in purchase history data; relates the relevance of motivations to customer- and shopping-trip characteristics; captures interdependencies between motivations; and achieves superior predictive performance. Estimation results from this comprehensive model provide deep insights into purchase behavior. Such insights can be used by managers to create more intuitive, better informed, and more effective marketing actions. As scalability of the model is essential for practical applicability, we develop a fast, custom-made inference algorithm based on variational inference. We illustrate the model using purchase history data from a Fortune 500 retailer involving more than 4,000 unique products.

Background and ObjectiveNon-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.MethodsAn 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.ResultsScreening 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.ConclusionsThis 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.

Consumers are widely adopting Artificially Intelligent Voice Assistants (AIVAs). AIVAs now handle many different everyday tasks and are also increasingly assisting consumers with purchasing decisions, making AIVAs a rich topic for marketing researchers. We develop a series of propositions regarding how consumer decision-making processes may change when moved from traditional online purchase environments to AI-powered voice-based dialogs, in the hopes of encouraging further academic thinking and research in this rapidly developing, high impact area of consumer-firm interaction. We also provide suggestions for marketing managers and policymakers on points to pay attention to when they respond to the proposed effects of AIVAs on consumer decisions.

A randomized controlled discrete choice experiment (DCE) with 3,320 participating respondents was used to investigate the individual and combined impact of level overlap and color coding on task complexity, choice consistency, survey satisfaction scores, and dropout rates. The systematic differences between the study arms allowed for a direct comparison of dropout rates and cognitive debriefing scores and accommodated the quantitative comparison of respondents' choice consistency using a heteroskedastic mixed logit model. Our results indicate that the introduction of level overlap made it significantly easier for respondents to identify the differences and choose between the choice options. As a stand‐alone design strategy, attribute level overlap reduced the dropout rate by 30%, increased the level of choice consistency by 30%, and avoided learning effects in the initial choice tasks of the DCE. The combination of level overlap and color coding was even more effective: It reduced the dropout rate by 40% to 50% and increased the level of choice consistency by more than 60%. Hence, we can recommend attribute level overlap, with color coding to amplify its impact, as a standard design strategy in DCEs.

Background Discrete choice experiments (DCEs) are increasingly used for health state valuations. However, the values derived from initial DCE studies vary widely. We hypothesize that these findings indicate the presence of unknown sources of bias that must be recognized and minimized. Against this background, we studied whether values derived from a DCE are sensitive to how well the DCE design spans the severity range. Methods We constructed an experiment involving three variants of DCE tasks for health state valuation: standard DCE, DCE-death, and DCE-duration. For each type of DCE, an experimental design was generated under two different conditions, enabling a comparison of health state values derived from current best practice Bayesian efficient DCE designs with values derived from ‘severity-stratified’ designs that control for coverage of the severity range in health state selection. About 3000 respondents participated in the study and were randomly assigned to one of the six study arms. Results Imposing the severity-stratified restriction had a large effect on health states sampled for the DCE-duration approach. The unstratified efficient design returned a skewed distribution of selected health states, and this introduced bias. The choice probability of bad health states was underestimated, and time trade-offs to avoid bad states were overestimated, resulting in too low values. Imposing the same restriction had limited effect in the DCE-death approach and standard DCE. Conclusion Variation in DCE-derived values can be partially explained by differences in how well selected health states spanned the severity range. Imposing a ‘severity stratification’ on DCE-duration designs is a validity requirement. Electronic supplementary material The online version of this article (10.1007/s40273-018-0694-6) contains supplementary material, which is available to authorized users.

Background Despite evidence of nonproportional trade-offs in time trade-off exercises and the explicit incorporation of exponential discounting in health technology assessment calculations, quality-adjusted life-year (QALY) tariffs are currently still established under the assumption of linear time preferences. Objectives The aim of this study was to introduce a general method of accommodating for nonlinear time preferences in discrete choice experiment (DCE) duration studies and to evaluate its impact on estimated QALY tariffs. Methods A parsimonious utility function is proposed that accommodates any discounting function and preserves linear time preferences as a special case. Based on an efficient DCE design and 1775 respondents from a nationally representative scientific household panel, preferences and QALY tariffs for the Dutch SF-6D were estimated while accommodating for nonlinear time preferences via exponential and hyperbolic discounting functions. Results When the discount rate was estimated directly, we found strong evidence of nonlinear time preferences (with an exponential and hyperbolic discount rate of 5.7% and 16.5%, respectively). When the discount rate was estimated as a function of health state severity, we found that years lived in better health states are discounted minus years lived in impaired health states. Finally, the best statistical fit was obtained when using a hyperbolic discount function, which resulted in smaller QALY decrements and fewer health states classified as worse than immediate death. Conclusions Our results highlight the relevance and even necessity of a paradigm shift in health valuation studies in favor of time-preference corrected QALY tariffs, with potentially important implications for health technology assessment calculations and regulatory decisions.

This paper studies the performance of case 2 best-worst scaling (BWS) when it is applied to a mix of positive and negative attributes, for example in studying treatments characterized by both benefits and harms. Intuitively, such a mix of positive and negative attributes leads to dominance. We analytically show that dominance leads to infinitely large differences between the parameter estimates for the positive versus negative attributes. The results from a simulation study confirm our analytical results: parameter values of the attributes could not be accurately recovered. When only a single positive attribute was used, even the relative ordering of the attribute level preferences was not identified. As a result, case 2 BWS can be used to elicit preferences if only good (positive) or only bad (negative) attributes are included in the choice tasks, but not for both since dominance will impact parameter estimation and therefore decision-making.

In modern retail contexts, retailers sell products from vast product assortments to a large and heterogeneous customer base. Understanding purchase behavior in such a context is very important. Standard models cannot be used due to the high dimensionality of the data. We propose a new model that creates an efficient dimension reduction through the idea of purchase motivations. We only require customer-level purchase history data, which is ubiquitous in modern retailing. The model handles large-scale data and even works in settings with shopping trips consisting of few purchases. As scalability of the model is essential for practical applicability, we develop a fast, custom-made inference algorithm based on variational inference. Essential features of our model are that it accounts for the product, customer and time dimensions present in purchase history data; relates the relevance of motivations to customer- and shopping-trip characteristics; captures interdependencies between motivations; and achieves superior predictive performance. Estimation results from this comprehensive model provide deep insights into purchase behavior. Such insights can be used by managers to create more intuitive, better informed, and more effective marketing actions. We illustrate the model using purchase history data from a Fortune 500 retailer involving more than 4,000 unique products.

Lack of evidence about the external validity of Discrete Choice Experiments (DCEs)-sourced preferences inhibits greater use of DCEs in healthcare decision-making. This study examines the external validity of such preferences, unravels its determinants, and provides evidence of whether healthcare choice is predictable. We focused on influenza vaccination and used a six-step approach: i) literature study, ii) expert interviews, iii) focus groups, iv) survey including a DCE, v) field data, and vi) in-depth interviews with respondents who showed discordance between stated choices and actual healthcare utilization. Respondents without missing values in the survey and the actual healthcare utilization (377/499 = 76%) were included in the analyses. Random-utility-maximization and random-regret-minimization models were used to analyze the DCE data, whereas the in-depth interviews combined five scientific theories to explain discordance. When models took into account both scale and preference heterogeneity, real-world choices to opt for influenza vaccination were correctly predicted by DCE at an aggregate level, and 91% of choices were correctly predicted at an individual level. There was 13% (49/377) discordance between stated choices and actual healthcare utilization. In-depth interviews showed that several dimensions played a role in clarifying this discordance: attitude, social support, action of planning, barriers, and intention. Evidence was found that our DCE yields accurate actual healthcare choice predictions if at least scale and preference heterogeneity are taken into account. Analysis of discordant subjects showed that we can even do better. The DCE measures an important part of preferences by focusing on attribute tradeoffs that people make in their decision to participate in a healthcare intervention. Inhibitors may be among these attributes, but it is more likely that inhibitors have to do with exogenous factors like goals, religion, and social norms. Con-ducting upfront work on constraints/inhibitors of the focal behavior, not just what promotes the behavior, might further improve predictive ability.

Charitable organizations send out large volumes of direct mailings, soliciting for money in support of many good causes. Without any request, donations are rarely made, and it is well known that each request for money by a charity likely generates at least some revenues. Whether a single request from a charity increases the total amount donated by an individual is however unknown. Indeed, a response to one request can hurt responses to others. The net effect is therefore not easily observable, certainly not when multiple charities address the same individuals. In this paper we alleviate these observational difficulties by carrying out a field experiment in which five large charities cooperate. With the unique data that we collect, we study the impact of sending more requests on total donations. The results indicate that there is a negative competitive effect on requests from other charities, but this effect dies out rapidly. Soon after the mailing has been sent, it is only a strong cannibalization of the charity's own revenues that prevails. This empirical finding suggests the important conclusion that not much coordination across charities is needed to increase revenues. We also demonstrate that charities need sophisticated evaluation tools that do not ignore the effects of cannibalization.

Preference studies are becoming increasingly important within the medical product decision-making context. Currently, there is limited understanding of the range of methods to gain insights into patient preferences. We developed a compendium and taxonomy of preference exploration (qualitative) and elicitation (quantitative) methods by conducting a systematic literature review to identify these methods. This review was followed by analyzing prior preference method reviews, to cross-validate our results, and consulting intercontinental experts, to confirm our outcomes. This resulted in the identification of 32 unique preference methods. The developed compendium and taxonomy can serve as an important resource for assessing these methods and helping to determine which are most appropriate for different research questions at varying points in the medical product lifecycle. ispartof: DRUG DISCOVERY TODAY vol:24 issue:7 pages:1324-1331 ispartof: location:England status: published

Contains fulltext : 188598.pdf (Publisher’s version ) (Open Access) 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. 10 p.

This paper develops new directions on how individuals’ use of multiple goals can be incorporated in econometric models of individual decision making. We start by outlining key components of multiple, simultaneous goal pursuit and multi-stage choice. Since different goals are often only partially compatible, such a multiple goal-based approach implies balancing goals, leading to a deliberate goal-level choice strategy on the part of the decision maker. Accordingly, we introduce a conceptual framework to classify different aspects of individuals’ decisions in the presence of multiple goals. Based on this framework we propose a formalization of individual decision making when pursuing multiple goals. We briefly review different previous streams on goal-based decision making and how the proposed goal-driven conceptual framework relates to earlier research in discrete choice models. The framework is illustrated using examples from different domains, in particular marketing, environmental economics, transportation and sociology. Finally, we discuss identification and modeling needs for goal-based choice strategies and opportunities for further research. Refereed/Peer-reviewed

Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care. Electronic supplementary material The online version of this article (doi:10.1007/s40271-015-0118-z) contains supplementary material, which is available to authorized users.

ObjectiveThe Assessment of Burden of COPD (ABC) tool supports shared decision making between patient and caregiver. It includes a coloured balloon diagram to visualise patients’ scores on burden indicators. We aim to determine the importance of each indicator from a patient perspective, in order to calculate a weighted index score and investigate whether that score is predictive of costs.DesignDiscrete choice experiment.Setting and participantsPrimary care and secondary care in the Netherlands. 282 patients with chronic obstructive pulmonary disease (COPD) and 252 members of the general public participated.MethodsRespondents received 14 choice questions and indicated which of two health states was more severe. Health states were described in terms of specific symptoms, limitations in physical, daily and social activities, mental problems, fatigue and exacerbations, most of which had three levels of severity. Weights for each item-level combination were derived from a Bayesian mixed logit model. Weights were rescaled to construct an index score from 0 (best) to 100 (worst). Regression models were used to find a classification of this index score in mild, moderate and severe that was discriminative in terms of healthcare costs.ResultsFatigue, limitations in moderate physical activities, number of exacerbations, dyspnoea at rest and fear of breathing getting worse contributed most to the burden of disease. Patients assigned less weight to dyspnoea during exercise, listlessness and limitations with regard to strenuous activities. Respondents from the general public mostly agreed. Mild, moderate and severe burden of disease were defined as scores ConclusionsThe ABCIndex is a new index score for the burden of COPD, which is based on patients’ preferences. The classification of the index score into mild, moderate and severe is predictive of future healthcare costs.Trial registration numberNTR3788; Post-results.

Background: The Assessment of Burden of COPD (ABC) tool is an instrument that supports shared decision making between patients and physicians. It includes a coloured balloon diagram to visualize a patient’s scores on a questionnaire about the experienced burden of COPD and several objective severity indicators. An algorithm translates these scores into a treatment advice to be discussed with the patient. The resulting individual care plan includes personalized treatment goals framed in the patient’s own words. In an 18-months cluster randomized controlled trial this tool was found to be effective in improving disease-specific quality of life.Aim: The aim of this study was twofold. Firstly, to determine the importance of each item of the experienced burden of disease, from a patient-perspective, in order to calculate a weighted index score on a 0-100 scale. Secondly, to investigate whether the ABC Index score can be used to develop a grouping of COPD patients into mild, moderate, and severe burden of disease that is predictive of future healthcare utilization and costs.Methods: The importance weights were determined in a discrete choice experiment (DCE) among 282 COPD patients and 252 members of the general public. Respondents received 14 choice questions; in each question they were asked which of two health states was more severe. Health states were described in terms of specific symptoms, limitations, mental problems, fatigue and exacerbations. Weights for each item-level combination were derived statistically from the likelihood of each health state to be considered worse than the other. Weights were re-scaled to a range from 0 (best) to 100 (worst). The capability of the ABC Index to predict healthcare utilization and costs was explored by dividing the ABC Index into three classes (mild, moderate, severe burden of disease) and comparing the average resource utilization and costs per class. Multi-level regression models were used to find the most discriminative cut-off points to define the classes.Results: For patients, fatigue, limitations in moderate physical activities, number of exacerbations, dyspnea at rest, and concern about breathing getting worse were the most important drivers of the burden of COPD. Little weight was attached to dyspnoea during exercise, listlessness and limitations with regard to strenuous activities. Respondents from the general public mostly agreed with this. Mild, moderate and severe burden of disease were defined as scores 40, respectively. This categorisation was most predictive of healthcare utilisation and annual costs: €1200, €2500 and €9500 per year, respectively.Conclusion: The ABC tool is an instrument for shared decision making, which scores can be aggregated into an overall score of the experienced burden of disease that is based on the importance that patients themselves assign to the various items. Patients can be grouped into mild, moderate and severe burden of disease and this grouping is predictive of their future healthcare utilization and costs. Hence, at a group level, the ABC Index can be used for monitoring and it may guide contracting between health insurers and healthcare providers.

Background Several epidemiological studies have investigated the effect of the quantity of green space on health outcomes such as self-rated health, morbidity and mortality ratios. These studies have consistently found positive associations between the quantity of green and health. However, the impact of other aspects, such as the perceived quality and average distance to public green, and the effect of urban green on population health are still largely unknown. Methods Linear regression models were used to investigate the impact of three different measures of urban green on small-area life expectancy (LE) and healthy life expectancy (HLE) in The Netherlands. All regressions corrected for average neighbourhood household income, accommodated spatial autocorrelation, and took measurement uncertainty of LE, HLE as well as the quality of urban green into account. Results Both the quantity and the perceived quality of urban green are modestly related to small-area LE and HLE: an increase of 1 SD in the percentage of urban green space is associated with a 0.1-year higher LE, and, in the case of quality of green, with an approximately 0.3-year higher LE and HLE. The average distance to the nearest public green is unrelated to population health. Conclusions The quantity and particularly quality of urban green are positively associated with small-area LE and HLE. This concurs with a growing body of evidence that urban green reduces stress, stimulates physical activity, improves the microclimate and reduces ambient air pollution. Accordingly, urban green development deserves a more prominent place in urban regeneration and neighbourhood renewal programmes.

Objectives: This study aimed 1) to quantify the strength of patient preferences for different aspects of early assisted discharge in The Netherlands for patients who were admitted with a chronic obstructive pulmonary disease exacerbation and 2) to illustrate the benefits of latent class modeling of discrete choice data. This technique is rarely used in health economics. Methods: Respondents made multiple choices between hospital treatment as usual (7 days) and two combinations of hospital admission (3 days) followed by treatment at home. The latter was described by a set of attributes. Hospital treatment was constant across choice sets. Respondents were patients with chronic obstructive pulmonary disease in a randomized controlled trial investigating the cost-effectiveness of early assisted discharge and their informal caregivers. The data were analyzed using mixed logit, generalized multinomial logit, and latent-class conditional logit regression. These methods allow for heterogeneous preferences across groups, but in different ways. Results: Twenty-five percent of the respondents opted for hospital treatment regardless of the description of the early assisted discharge program, and 46% never opted for the hospital. The best model contained four latent classes of respondents, defined by different preferences for the hospital and caregiver burden. Preferences for other attributes were constant across classes. Attributes with the strongest effect on choices were the burden on informal caregivers and co-payments. Except for the number of visits, all attributes had a significant effect on choices in the expected direction. Conclusions: Considerable segments of respondents had fixed preferences for either treatment option. Applying latent class analysis was essential in quantifying preferences for attributes of early assisted discharge.

This paper develops new directions on how individuals’ use of multiple goals can be incorporated in econometric models of individual decision making. We start by outlining key components of multiple, simultaneous goal pursuit and multi-stage choice. Since different goals are often only partially compatible, such a multiple goal-based approach implies balancing goals, leading to a deliberate goal-level choice strategy on the part of the decision maker. Accordingly, we introduce a conceptual framework to classify different aspects of individuals’ decisions in the presence of multiple goals. Based on this framework we propose a formalization of individual decision making when pursuing multiple goals. We briefly review different previous streams on goal-based decision making and how the proposed goal-driven conceptual framework relates to earlier research in discrete choice models. The framework is illustrated using examples from different domains, in particular marketing, environmental economics, transportation and sociology. Finally, we discuss identification and modeling needs for goal-based choice strategies and opportunities for further research.

Background: Patients' preferences are important for shared decision making. Therefore, we investigated patients' and urologists' preferences for treatment alternatives for early prostate cancer (PC). Methods: A discrete choice experiment was conducted among 150 patients who were waiting for their biopsy results, and 150 urologists. Regression analysis was used to determine patients' and urologists' stated preferences using scenarios based on PC treatment modality (radiotherapy, surgery, and active surveillance (AS)), and risks of urinary incontinence and erectile dysfunction. Results: The response rate was 110 out of 150 (73%) for patients and 50 out of 150 (33%) for urologists. Risk of urinary incontinence was an important determinant of both patients' and urologists' stated preferences for PC treatment (P

Health state valuations of patients and non-patients are not the same, whereas health state values obtained from general population samples are a weighted average of both. The latter constitutes an often-overlooked source of bias. This study investigates the resulting bias and tests for the impact of reference dependency on health state valuations using an efficient discrete choice experiment administered to a Dutch nationally representative sample of 788 respondents. A Bayesian discrete choice experiment design consisting of eight sets of 24 (matched pairwise) choice tasks was developed, with each set providing full identification of the included parameters. Mixed logit models were used to estimate health state preferences with respondents' own health included as an additional predictor. Our results indicate that respondents with impaired health worse than or equal to the health state levels under evaluation have approximately 30% smaller health state decrements. This confirms that reference dependency can be observed in general population samples and affirms the relevance of prospect theory in health state valuations. At the same time, the limited number of respondents with severe health impairments does not appear to bias social tariffs as obtained from general population samples. Copyright © 2016 John Wiley & Sons, Ltd.

The ABC tool is an instrument to support shared decision making between patient and caregiver. It includes a colored balloon diagram to visualize patients9 scores on the subjective burden of COPD questionnaire and objective severity indicators. We determined the importance of each item of the burden of disease from a patient-perspective, in order to calculate a weighted index score that can be related to costs. We conducted a Discrete Choice Experiment (DCE) among COPD patients in a cluster-RCT of the ABC tool. Each COPD patient received 14 choice questions, in which he was asked which of two health states was more severe. States were described in terms of the 15 items of the ABC questionnaire: dyspnoea (at rest; during physical activity), coughing, sputum production, limitations (in strenuous physical; moderate physical; daily and social activities), feeling depressed, concern about breathing getting worse, worrying, listlessness, tension, fatigue and exacerbations. Each item had 3 levels. Weights for each item-level combination were derived statistically from the likelihood of each health state to be considered worse than the other. Weights were re-scaled to generate the ABC index score, ranging from 0 (best) to 100 (worst). 282 patients completed the DCE. The highest weights were assigned to dyspnoea at rest, limitations in moderate physical activities, daily and social activities, concern about breathing getting worse, fatigue, and exacerbations. Mild, moderate and severe burden of disease were defined as ABC index scores 40, respectively. This categorisation was most predictive of mean annual healthcare costs: €1200, €2500 and €9500, respectively.

Many firms rely on choice experiment–based models to evaluate future marketing actions under various market conditions. This research investigates choice complexity (i.e., number of alternatives, number of attributes, and utility similarity between the most attractive alternatives) and individual differences in decision time as key factors that affect the predictive performance of models based on choice experiments, both within and between complexity conditions. The results show that complexity and individual decision time not only affect the error in consumer choice models but also consumers' decision strategy and systematic utilities. The authors introduce a complexity-adjusted mixed logit (CAM logit) model to capture the various influences of complexity in choice experiment–based models. They illustrate the consequences of complexity on choice behavior with market share predictions of the CAM logit model for different complexity conditions.

There are several measures that summarize the mortality experience of a population. Of these measures, life expectancies are generally preferred based on their simpler interpretation and direct age standardization, which makes them directly comparable between different populations. However, traditional life expectancy estimations are highly inaccurate for smaller populations and consequently are seldom used in small-area applications. In this paper, the authors compare the relative performance of traditional life expectancy estimation with a Bayesian random-effects approach that uses correlations (i.e., borrows strength) between different age groups, geographic areas, and sexes to improve the small-area life expectancy estimations. In the presented Monte Carlo simulations, the Bayesian random-effects approach outperforms the traditional approach in terms of bias, root mean square error, and coverage of the 95% confidence intervals. Moreover, the Bayesian random-effects approach is found to be usable for populations as small as 2,000 person-years at risk, which is considerably smaller than the minimum of 5,000 person-years at risk recommended for the traditional approach. As such, the proposed Bayesian random-effects approach is well-suited for estimation of life expectancies in small areas.

Experimental and survey-based research suggests that consumers often rely on their intuition and cognitive shortcuts to make decisions. Intuition and cognitive shortcuts can lead to suboptimal decisions and, especially in high-stakes decisions, to legitimate welfare concerns. In this paper, we propose an extension of a Bayesian learning model that allows us to quantify the impact of salience—the fact that some pieces of information are easier to retrieve from memory than others—on physician learning. We show, using data on actual prescriptions for real patients, that physicians' belief formation is strongly influenced by salience effects. Feedback from switching patients—the ones the physician decided to switch to a clinically equivalent treatment—receives considerably more weight than feedback from other patients. In the category we study, salience effects slowed down physicians' speed of learning and the adoption of a new treatment, which raises welfare concerns. For managers, our findings suggest that firms that are able to eliminate, or at least reduce, salience effects to a greater extent than their competitors can speed up the adoption of new treatments. We explore the implications of these results and suggest alternative applications of our model that are relevant for policy makers and managers.

Objectives Increased use of discrete choice experiments (DCEs) in healthcare requires establishing whether stated preferences are predictive of observed healthcare utilization. This study aimed 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 predict real-world choices correctly at an aggregate and individual level in healthcare. Methods Two randomized controlled trials (RCTs) involving choices for influenza vaccination and colorectal cancer screening were used. Each RCT had three study conditions: DCE choice tasks with (i) two alternatives, (ii) three alternatives, or (iii) both. Two samples of 1,200 respondents each were randomly assigned to one of the conditions. Each respondent answered 16 DCE choice tasks (for the derivation of the decision model) plus a choice task mimicking the real-world choice (to keep the decision context the same). The data was analysed in a systematic way using random-utility-maximization (RUM) and random-regret-minimization (RRM) choice processes with scale and/or preference heterogeneity. Results Irrespective of the number of alternatives per choice task, the choice to opt 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, three alternatives per choice task and using heteroscedastic model plus preference heterogeneity seemed to be most promising, correctly predicting the real-world choice in 81.7% and 87.9% of the cases for influenza vaccination and colorectal cancer screening respectively. No evidence was found that RRM outperformed RUM. Conclusions Our study shows that DCEs hold the potential of being externally valid at an aggregate level if at least scale and preference heterogeneity are taken into account. Further research is needed to determine if this result remains in other contexts, and to optimise choice prediction at an individual level.

In managed care markets, health insurers bargain over the price and quality of health care services. Selective contracting is an important tool for health insurers in negotiations with health care providers. Empirical research in the United States showed that health insurers using exclusionary provider networks obtain higher discounts than insurers without restrictions on provider choice (Staten, Dunkelberg, and Umbeck 1987; Melnick et al. 1992; Sorensen 2003;). Research on health plan choice in the United States, however, showed that consumers are reluctant to choose a health plan with stringent restrictions on provider choice. Consumers dislike or even distrust some of these very restrictive forms of managed care (Feldman et al. 1989; Gawande et al. 1998; Chu-Weininger and Balkrishnan 2006; Miller 2006;). As an alternative to exclusionary networks, health insurers can also use preferred provider networks in which enrollees still have the option to seek out-of-network care. In that case, the bargaining position of health insurers largely depends on their ability to channel enrollees toward preferred providers (Pauly 1987). In turn, this ability depends on the attractiveness of the network and consumers' propensity to switch to another provider. The latter is limited by the status quo bias. Status quo bias arises once consumers are reluctant to leave their current provider even if better alternatives are readily available (Neipp and Zeckhauser 1985; Samuelson and Zeckhauser 1988; Strombom, Buchmueller, and Feldstein 2002;). Switching costs, uncertainty about other alternatives, and the relationship consumers have with their current provider determine the importance of the status quo bias. Despite the widespread use of preferred provider networks in the United States, surprisingly little is known about the insurer's ability to channel enrollees toward preferred providers. Most research focused on the impact of price on health care utilization or on health plan choice (Glied 2000; Zweifel and Manning 2000;). In a recent paper, Wu (2008) finds that health plans that successfully channel patients can extract greater discounts but that it still remains unclear which strategies are effective in directing patients to preferred providers. Furthermore, it is unclear whether the effectiveness of channeling incentives differs for different provider types. In many countries with social health insurance systems (e.g., Germany, the Netherlands, Switzerland), health insurers are nowadays provided with incentives and tools, such as selective contracting, to act as prudent buyers of care on behalf of their enrollees. We examine the potential effectiveness of patient channeling by third parties, as this is required before one can reap the benefits from exclusionary and preferred provider networks. We particularly focus on the impact of status quo bias on provider choice. We confine our investigation to GPs and pharmacies because these are most frequently consulted and because consumer sensitivity to channeling incentives may differ most strongly between these providers. These questions are investigated in the context of the Dutch health care system, in which GPs perform an important role as gatekeeper. In 2006, the Dutch health care system was profoundly reformed to provide competing health insurers with incentives to selectively contract providers (Van de Ven and Schut 2008). The results are relevant for all countries in which health insurers or other third parties aim to influence provider choice.

Customers can interact with and create value for firms in a variety of ways. This article proposes that assessing the value of customers based solely upon their transactions with a firm may not be sufficient, and valuing this engagement correctly is crucial in avoiding undervaluation and overvaluation of customers. We propose four components of a customer’s engagement value (CEV) with a firm. The first component is customer lifetime value (the customer’s purchase behavior), the second is customer referral value (as it relates to incentivized referral of new customers), the third is customer influencer value (which includes the customer’s behavior to influence other customers, that is increasing acquisition, retention, and share of wallet through word of mouth of existing customers as well as prospects), and the fourth is customer knowledge value (the value added to the firm by feedback from the customer). CEV provides a comprehensive framework that can ultimately lead to more efficient marketing strategies that enable higher long-term contribution from the customer. Metrics to measure CEV, future research propositions regarding relationships between the four components of CEV are proposed and marketing strategies that can leverage these relationships suggested.

Charities rely mainly on direct mailings to attract the attention of potential donors. Individuals may feel irritated by these mailings, in particular when they receive many mailings. This might harm the revenues charities receive from their mailing activities. Moreover, target selection by charities likely results in many mailings being sent to the best donors, and hence they might become most irritated. As such, irritation with direct mailings could well be endogenously determined. To ensure exogenous variation in irritation, we performed a unique controlled field experiment in cooperation with five of the largest charities in the Netherlands. Our analysis reveals that direct mailings do result in irritation, but surprisingly, this irritation affects neither stated nor actual donating behavior.

Efficient contracting of health care requires effective consumer channeling. Little is known about the effectiveness of channeling strategies. We study channeling incentives on pharmacy choice using a large scale discrete choice experiment. Financial incentives prove to be effective. Positive financial incentives are less effective than negative financial incentives. Channeling through qualitative incentives also leads to a significant impact on provider choice. While incentives help to channel, a strong status quo bias needs to be overcome before consumers change pharmacies. Focusing on consumers who are forced to choose a new pharmacy seems to be the most effective strategy.