Comparing Outcomes of a Discrete Choice Experiment and Case 2 Best-Worst Scaling: An Application to Neuromuscular Disease Treatment.

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.

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.

Two for the price of one: If moving beyond traditional single-best discrete choice experiments, should we use best-worst, best-best or ranking for preference elicitation?

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.

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.

Mimicking Real-Life Decision Making in Health: Allowing Respondents Time to Think in a Discrete Choice Experiment.

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.

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

Quantitatively summarize patient preferences for European licensed relapsing-remitting multiple sclerosis (RRMS) disease-modifying treatment (DMT) options. 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.

Can healthcare choice be predicted using stated preference data?

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.

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.

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

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.

Methods for exploring and eliciting patient preferences in the medical product lifecycle: a literature review.

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.

Attribute level overlap (and color coding) can reduce task complexity, improve choice consistency, and decrease the dropout rate in discrete choice experiments.

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.

Advocating a Paradigm Shift in Health-State Valuations: The Estimation of Time-Preference Corrected QALY Tariffs.

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.

Severity-Stratified Discrete Choice Experiment Designs for Health State Evaluations.

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.

Effect of Level Overlap and Color Coding on Attribute Non-Attendance in Discrete Choice Experiments.

Objective: The aim of this study was to test the hypothesis that level overlap and color coding can mitigate or even preclude the occurrence of attribute nonattendance in discrete choice experiments.

Methods: A randomized controlled experiment with five experimental study arms was designed to investigate the independent and combined impact of level overlap and color coding on respondents' attribute nonattendance. The systematic differences between the study arms allowed for a direct comparison of observed dropout rates and estimates of the average number of attributes attended to by respondents, which were obtained by using augmented mixed logit models that explicitly incorporated attribute non-attendance.

The impact of vaccination and patient characteristics on influenza vaccination uptake of elderly people: A discrete choice experiment.

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.

ABC Index: quantifying experienced burden of COPD in a discrete choice experiment and predicting costs.

Objective: The 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.

Are Health State Valuations from the General Public Biased? A Test of Health State Reference Dependency Using Self-assessed Health and an Efficient Discrete Choice Experiment.

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.

Estimating the Impact of Health-related Behaviors on Geographic Variation in Cardiovascular Mortality: A New Approach Based on the Synthesis of Ecological and Individual-level Data.

Background: Incidence of and mortality from cardiovascular disease (CVD) exhibit a strong geographical pattern, with inhabitants of more affluent neighborhoods showing a substantially lower risk of CVD mortality than inhabitants of deprived neighborhoods. Thus far, there is insufficient evidence as to what extent these differences can be attributed to differences in health-related behaviors.

Methods: Using a Hierarchical Related Regression approach, we combined individual and aggregate (ecological) data to investigate the extent to which small-area variation in CVD mortality in Dutch neighborhoods can be explained by several behavioral risk factors (i.

Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide.

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.

Should I stay or should I go home? A latent class analysis of a discrete choice experiment on hospital-at-home.

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 effect of urban green on small-area (healthy) life expectancy.

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.

Patients' and urologists' preferences for prostate cancer treatment: a discrete choice experiment.

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.

Small-area health comparisons using health-adjusted life expectancies: a Bayesian random-effects approach.

Health-adjusted life expectancy (HALE) is one of the most attractive summary measures of population health. It provides balanced attention to fatal as well as non-fatal health outcomes, is sensitive to the severity of morbidity within the population, and can be readily compared between areas with very different population age structures. HALE, however, cannot be calculated at the small-area level using traditional life table methodology.