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

© 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

Antecedents of True Brand Loyalty

We examine a model of six latent constructs and propose that true brand loyalty can be explained as a result of five distinct antecedents: brand credibility, affective brand conviction, cognitive brand conviction, attitude strength, and brand commitment. Data from experimental conditions with manipulations of eight product classes and two involvement levels lend support for the proposed model, demonstrating that brand loyalty can be considered as truly loyal only when mediated by a high degree of affective and cognitive brand conviction, and attitude strength. Advertising and marketing implications for the relationships among the six constructs under different manipulation conditions are discusse

Distortions in willingness-to-pay for public goods induced by endemic distrust in institutions

In this paper, we analyze the implications for the economic valuation of the provision of public goods, considering respondents’ perceptions of the institution(s) that provide the service. The specific behavioral mechanism whereby institutional distrust (ID) shows itself is through the activation of screening of choice options (choice set formation). However, ID-induced choice set formation might be confounded with the consumer budget constraint, especially in a developing country context, leading to biased welfare estimates for service improvement. We formulate a semi-compensatory hybrid choice set formation (SC-HCSF) model that enables us to 1) discriminate the effect of a budget constraint from that of ID-induced choice set formation and 2) characterize their separate impacts on welfare estimates using a spatial framework. We compare our model results to those from a standard Random Parameters Logit (RPL) Model. The RPL underestimates (overestimates) welfare when individuals have a low (high) ID. Based on our empirical model results, we demonstrate that the impacts of ignoring institutional trust issues can be highly deleterious to project appraisals, particularly in settings where legislative and regulatory institutions are perceived to be endemically corrupt

Empirical Identification in the Mixed Logit Model: Analysing the Effect of Data Richness

Mixed Logit model, Empirical identification, Data richness, Repeated choice tasks,