Random Parameter Logit Research Articles

Quantifying Stated Preferences for Meningococcal Vaccines Among Adolescents/Young Adults and Parents of Adolescents in the United States: A Discrete Choice Experiment.

Invasive meningococcal disease (IMD) is a severe and life-threatening disease. In the United States (US), vaccine coverage with MenACWY and MenB meningococcal vaccines is suboptimal among adolescents/young adults aged 16-23years. A combined meningococcal vaccine (MenABCWY) could increase convenience (e.g., fewer injections) and improve coverage. The objective was to quantify preferences for hypothetical meningococcal vaccine profiles among adolescents/young adults and parents. An online discrete choice experiment was conducted among 16- to 23-year-olds, and parents of 16- to 18-year-olds. Attributes (3×4) and levels (1×2) were based on the literature and focus groups. Participants made ten pair-wise forced trade-off choices, systematically varied using a D-optimal design. Random parameter logit quantified the relative importance of vaccination attributes and estimated the trade-offs. Differences in preferences by subgroups were assessed. Totals of 300 adolescents and young adults (median age 20years) and 300 parents (median age 46years) completed the survey. Overall, 89.6% of 16- to 23-year-olds and 69.1% of parents preferred a simplified hypothetical meningococcal vaccination profile, e.g., with fewer injections (3 vs. 4) and fewer healthcare provider (HCP) visits (2-3 vs. 4). Having HCP advice and clear Centers for Disease Control and Prevention recommendations impacted vaccination choice, with both groups reporting high trust in HCP information (83.3% among 16- to 23-year-olds; 98.7% among parents). Barriers to vaccination included lack of HCP advice or awareness of meningococcal vaccines, and income level and out-of-pocket costs for parents. Adolescents/young adults and parents demonstrated a significant preference for a meningococcal vaccine that is more convenient (such as combined MenABCWY). Parents' vaccination preferences differed by income level and out-of-pocket costs, suggesting financial barriers to vaccination may exist which could result in IMD prevention inequalities. Findings from this study provide important information to support patient-facing informed policy discussions. A simplified vaccination schedule and strong recommendation could help improve vaccine uptake, schedule compliance, disease prevention, and reduce inequalities in IMD risk and prevention. A graphical abstract is available with this article.

The new frontier of urban mobility: a scenario-based analysis of autonomous vehicles adoption in urban transportation

ABSTRACT Urban mobility, as a critical component of urban development and sustainability, is on the brink of a major revolution with the advent and widespread adoption of autonomous vehicles (AVs). However, the ambitious vision of a transformative shared economic future and decreased car ownership conflicts with the notion that car dependency will be worsened, leading to significant uncertainties and potential challenges surrounding AVs, particularly in developing countries. In this context, this study contributes to the literature by addressing these uncertainties, through an empirical examination of preferences toward four distinct modes of transportation: “public transport”, “conventional private car”, “autonomous private car”, and “shared autonomous taxi”. To accomplish this, a scenario-based stated preference survey was administered in Istanbul, Turkey, as an example of a megacity in a developing country. The collected data were analyzed using discrete choice models (multinomial and random parameter (mixed) logit models) to reveal the preferences of the survey participants. The findings indicate that considering the social status and significance associated with private vehicle ownership, the challenges of popularizing shared and on-demand AV modes should not be underestimated.

Assessing the factors associated with pedestrian injury severity in hit-and-run crashes in Ghana

ABSTRACT Hit-and-run crashes often have severe consequences for vulnerable road users. In light of governmental efforts to promote pedestrian-friendly urban environments, the significance of these crashes cannot be overstated. In this study, we assessed the factors that influence the injury severity of pedestrians involved in hit-and-run crashes in Ghana. Historical crash data (1469 observations) spanning from 2013 to 2020 was used in this study. An injury-severity model was developed using random parameters logit approach to assess what crash factors significantly affect the injury outcome of the crashes. It was observed that hit-and-run crashes that occur on dark and unlit roadways were more likely to result in fatal injuries. Also, female pedestrians were less likely to be killed in hit-and-run crashes. These findings provide the basis for developing and implementing appropriate countermeasures, such as punitive laws for drivers who leave the crash scene and protective laws for those who help their victims.

A study on commuters’ public transportation mode choice behavior in river valley-type cities considering terrain spatial perception: evidence from Lanzhou, China

Existing research rarely examines the subjective and objective built environment of river valley-type cities in relation to travel mode choice, particularly overlooking the heterogeneity among travelers in these cities. In this paper, based on questionnaire survey data and built environment data, terrain spatial perception (TSP) is introduced to expand the theory of planned behavior (TPB), and a Structural Equation Model (SEM) is established. Factor analysis and path analysis are conducted using SPSS and AMOS to estimate latent variables. An integrated model of SEM and random parameter Logit model (RPLM), which can not only analyze the psychological perception factors of commuters in river valley-type cities but also consider the heterogeneity of psychological perception, was constructed to analyze the impact of personal attributes, objective built environment factors, and psychological latent variables on the commuting mode choice behavior of public transport users in river valley-type cities. The results indicate that the five observation indicators corresponding to the proposed terrain spatial perception latent variables can better explain the terrain spatial perception of commuters in river valley-type cities. Different from plain cities, the subjective and objective built environment of river valley-type cities notably influence the travel behavior of commuters. Moreover, the parameters of terrain spatial perception follow a normal distribution, indicating that the sensitivity of different commuters to the terrain spatial perception of river valley-type cities is heterogeneous. The results of our study can provide a reference for alleviating traffic issues in valley cities.

Preferences among patients with triple-class exposed (TCE) relapsed/refractory multiple myeloma (RRMM) in the era of B-cell maturation antigen (BCMA)-directed T-cell–engaging therapies.

e23309 Background: BCMA-directed T-cell–engaging therapies have been introduced into clinical practice for the treatment of TCE RRMM. While efficacious, such treatments are associated with safety considerations such as cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and serious infections. These treatments also vary in reported efficacy, administration processes, and hospitalization requirements. The aim of this study is to quantify patient preferences and tradeoffs regarding these attributes. Methods: A discrete choice experiment (DCE) was designed to elicit preference weights corresponding to seven attributes related to efficacy (objective response rate [ORR], overall survival [OS]), safety (all-grade CRS, all-grade ICANS, serious infections) and treatment procedures (treatment administration, hospitalization requirements). Attribute levels were based on plausible ranges of potential values. Patients in the United States (US) with a physician-confirmed diagnosis of TCE RRMM were recruited. Pre-test interviews with patients were conducted and the final survey deployed online in 2023. A random-parameters logit model was used to estimate preference weights. Conditional relative importance (CRI) was calculated to rank the relative importance of each attribute. The minimum acceptable benefit (MAB) and maximum acceptable risk (MAR) were calculated to estimate the tradeoffs between efficacy and safety attributes. Results: The sample included 200 respondents (mean [standard deviation] age: 61 [6.4] years; 43% female; 46% White; 10% African American). A total of 197 participants reported having responded to prior treatment, of whom 195 had relapsed or become refractory. In the DCE, a 24-month increase in OS was found to be the most important attribute, with a CRI of 32.0%. Next most important were avoiding a 60% risk of serious infection (CRI 17.3%), avoiding 14-day hospitalization requirements (CRI 15.0%), and a 38% increase in ORR (CRI 13.7%). A treatment that could be started immediately and administered weekly or up to every 4 weeks was preferred over a CAR T-like procedure with a waiting period. Fewer initial hospitalization days was significantly preferred over more hospitalization days. The MABs for OS when the risk of serious infection was increased from 20% to 60%, CRS from 40% to 95%, and ICANS from 3% to 20% were 24.2, 17.1, and 18.0 months, respectively. When OS was increased from 12 to 36 months, the estimated MARs for serious infections, CRS, and ICANS were > 60%, > 95%, and > 20%, respectively. Conclusions: This study highlights US patient preferences regarding treatment attributes for TCE RRMM. Treatment benefits and risks and the mutual tradeoffs considered by patients should be evaluated when developing and selecting therapeutic approaches.

Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering

Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers.

Analyzing the factors influencing speeding behavior based on quasi-induced exposure and random parameter logit model with heterogeneity in means

Introduction: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity. Method: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior. Results: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. Conclusions: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. Practical Applications: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

Toll road crash severity using mixed logit model incorporating heterogeneous mean structures

The current study examined 1,465 crash observations (2017–2021) from Louisiana, identifying significant variables grouped into three major categories: drivers’, crash, and road characteristics. Considering crash injury severity as a dependent variable, we employed classic Multinomial Logit (MNL) model, and several other models to address unobserved heterogeneity in crash data including Random Parameter Logit (RPL), Random Parameter Logit with Heterogeneity in Means (RPLHM), and Random Parameter Logit with Heterogeneity in Means and Variance (RPLHMV). Our findings highlight the impact of factors such as driver gender, age, traffic violations, driver distractions, crash types, surface conditions, and roadway attributes on crash injury severity. These insights emphasise the complexity of toll road safety and inform targeted interventions to mitigate crash injury severity. Notably, male drivers and those under 25 years old increased property damage likelihood, while factors like driver distractions and lower posted speed limits reduced the likelihood of severe injuries or fatalities.

Temporal analysis of factors affecting injury severities of expressway rear-end crashes during weekdays and weekends

ABSTRACT High fatality rates in frequent rear-end crashes have underscored significant safety concerns in China. This study aims to explore the mechanisms and determinants of rear-end crashes, with a particular focus on the factors influencing crash severity during weekdays and weekends (W-W). Employing the Random Parameter Logit Model (RPLM) to account for variability in data, we analyzed W-W rear-end crashes on the Beijing-Shanghai Expressway in Jiangsu province from 2017 to 2019, considering three severity levels: no injury, minor injury, and severe injury. Our comprehensive analysis covered variables from temporal, roadway, vehicle, crash, and environmental categories, alongside calculating the marginal effects of each significant variable on crash severity. Findings reveal temporal instability over the three-year period and notable differences in W-W crash severity. Out of all variables, four displayed random parameter characteristics, indicating potential interactions that influence crash outcomes. Specifically, our results indicate that rear-end crashes involving three or more vehicles on bridges are more likely to result in casualties. Interchange segments typically saw no injuries in two-vehicle crashes. Speeding during winter or on sunny days significantly increases the risk of injuries and fatalities. Furthermore, rear-end crashes in interchange areas during winter are particularly prone to causing injuries. These findings offer guidance for the development of effective safety countermeasures targed at different pediods.

Assessing preferences for forest carbon credit and co-benefits: A choice experiment case study in Japan

This study investigated corporate preferences for forest carbon credit offsets. The preferences were elicited through a choice experiment by administering a questionnaire to determine the price of credit, location of forest sequestration project offset, social development on local employment, preservation of ecosystem services, and unit of sustainable development goals (SDGs). We also examined stated preferences for credit offset and the willingness to pay. The analysis employed multinomial logit and random parameter logit (RPL) models to account for heterogeneity in preferences. The results showed that small and medium-sized enterprises (SMEs) held a neutral stance regarding offsetting their emissions and were categorized as being in the early stages of engagement with carbon offsetting. As SMEs can decide whether to purchase credit, we assessed their preferences for the type of credit-based forest carbon sequestration. SMEs had a significant preference for the location of the project, SDGs, and credit price when deciding to purchase credit based on the RPL model estimates. They were willing to pay JPY 6,191 (approximately USD 41) for the location of a project to be local rather than overseas, JPY 933 for a higher number of unit SDGs, and JPY 131 for an increase in the number of jobs. These results suggest that SMEs prefer purchasing local credit generated within their prefecture as a carbon offset alternative.

Safety analysis of pedestrians distracted by mobile phones at street crossings: Field study in Nanjing

The growing public concern over traffic safety hazards caused by pedestrians’ distracted behavior, particularly related to mobile phone usage at pedestrian crossings. Through video recording of pedestrians’ street-crossing behaviors on 12 sidewalks across 9 urban road intersections in Nanjing city, 1778 valid pedestrian samples were collected. The study categorizes mobile phone use during the crossing into five distinct types: no use, voice call, screen gaze, screen gaze with operation, and listening to music with headphones. Then, the effects of gender, age and companion conditions on the mobile phone during the crossing were examined by chi-square tests. Utilizing binomial logistic and Relogit regression models, the study analyzed the impact and safety risks of distraction on crossing behavior. Additionally, a random parameters (RP) logit model with heterogeneity in means was used to investigate the determinants affecting mobile phone usage. Notably, individuals aged 18 to 30 years were identified as the random parameter, while factors such as being under 18 years old, having a companion without interaction, having a companion with interaction, weekdays, sidewalk length, and the total number of other pedestrians were identified as fixed parameters. The study also presented significant variables affecting the probability of mobile phone usage through marginal effects, highlighting the potential safety risks associated with mobile phone usage during street crossing. These findings emphasize the need for heightened pedestrian safety awareness and a reduction in distracted behaviors to enhance overall traffic safety.

Analysis of Factors Affecting the Severity of Injuries in Electric Two-Wheeled Vehicle Crashes with or without Violation: A Random Parametric Logit Model considering Heterogeneity of Means and Variances

Electric two-wheeled vehicle is one of the main commuting tools in China, but they are also more likely to have violations of the road group. In order to study the effect of the presence of violation on the severity of road traffic crashes among electric two-wheeler riders, in this study, the effects of rider characteristics, road characteristics, collision characteristics, and environmental characteristics on the severity of injuries of electric two-wheeled vehicle riders were considered separately analyzed based on the data of 6403 two-wheeled electric vehicle traffic crashes in a region of Shandong Province from 2015 to 2021, and a random parametric logit model considering the heterogeneity of the mean and the variance (RP-HMV logit) was established based on the presence or absence of violation behaviors of riders, respectively, in order to explore unobserved heterogeneity. In order to test the validity of the model for modeling the injury severity of pedestrians riding electric two-wheelers, multinomial logit (MN-logit model), and random parameter logit model (RP-logit) were estimated, and the results showed that the RP-HMV logit model was significantly superior in terms of goodness of fit. The study showed that some of the factors differed somewhat between the two scenarios, such as gender, while the factors that were significant in both scenarios were >60, broken pavement, street lights at night, no street lights at night, mixed motorized and nonmotorized lanes, sidewalks, other angles, no control, severe weather, and visibility <200 m, where the severe weather and visibility <200 m were random parameters obeying normal distributions, there is a significant difference between having street lights and no control at night in both scenarios, and the difference is significant. The results of the study can provide a reference for the development of targeted countermeasures to improve the traffic safety of electric two-wheeled vehicles in China.

Preferences of healthcare providers in Switzerland for attributes of pediatric hexavalent vaccines: a discrete-choice experiment

Objective To understand the preferences of healthcare providers (HCPs) in Switzerland for pediatric hexavalent vaccine attributes. Methods A discrete-choice experiment included a series of choices between 2 hypothetical pediatric hexavalent vaccines with varying attributes: device type (including preparation time and risk of dosage errors), proportion of infants seroprotected against Haemophilus influenzae type b (Hib) at 11–12 months (pre-booster), packaging size, years on the market, and the thermostability at room temperature. Odds ratios (ORs) and conditional relative attribute importance (CRAI) were calculated using random-parameters logit. Results HCPs (150 pediatricians and 40 nursing staff) in Switzerland were unlikely to choose a vaccine conferring 50% (OR 0.00; 95% CI 0.00–0.00) or 70% (OR 0.01; 95% CI 0.00–0.01) of infants with Hib seroprotection at 11-12 months (pre-booster) compared with a vaccine conferring 90% seroprotection. The odds of choosing a vaccine available on the market for more than 3 years were nearly 5 times the odds of choosing a vaccine available on the market for less than 1 year (OR 4.76; 95% CI 1.87–7.65). The odds of choosing a vaccine in a prefilled syringe were nearly 3 times the odds of choosing a reconstituted vaccine (OR 2.77; 95% CI 1.39–4.15), and the odds of choosing a vaccine with a smaller package size were nearly 2 times the odds of choosing a vaccine with larger package size (OR 1.89; 95% CI 1.23–2.55). HCPs were equally likely to choose vaccines that can stay at room temperature for 6 versus 3 days (OR 1.07; 95% CI 0.73–1.42). According to CRAI, the most important attribute was Hib seroprotection, followed by years on the market, device type, and packaging size. Conclusion Hib seroprotection at 11–12 months was the most important hexavalent vaccine attribute to HCPs in this study.

Chinese consumer preference for beef with geographical indications and other attributes

As the demand for beef products grows in the Chinese market, understanding consumer preferences for beef, especially those related to quality labelling, is essential. The recent agreement between China and the European Union to promote Geographical Indications (GIs) provides a new insight into preferences for beef with quality labelling. This paper assesses consumer preferences for beef products with GIs and other attributes. A nationwide survey is conducted including 1210 respondents in China by a choice experiment attributing GI label, ‘green’, ‘hazard-free’, and ‘organic’ labels, feeding regimes (grain-fed, grass-fed), country of origin (China, Ireland, Australia, Brazil), and price (30, 40, 80, 100 ¥/500 g). The random parameter logit model with error component reveals that Chinese consumers have a significant preference for grain-fed beef and domestic beef, and they are willing to pay a premium price for GI-labelled beef compared with other attributes. The interaction between GIs and country of origin is included to indicate the positive price impact of GIs on imported beef products. Demographic factors such as place of residence and occupation are found to affect consumer preferences for GIs.

Voluntary blood donation preferences in China: A discrete choice experiment among experienced and inexperienced donors.

Due partly to an ageing population, China faces an increasingly dire blood shortage crisis requiring greater voluntary blood donations. A better understanding of blood donation preferences can inform blood donation policies and potentially increase donations. We used an online survey and discrete choice experiment to achieve our study objective: identify the most influential structural facilitators and barriers to voluntary blood donation in China. First, we identified six structural attributes (travel time, venue, donation volume, paid leave, scheduling and gifts) that were hypothesized to influence voluntary blood donation; attribute selection was based on a literature review and qualitative interviews. Second, a d-efficient design with 36 choice sets and 9 blocks was developed. Participants were asked to complete four choice sets, and in each choice set, they were asked to choose from three options: two voluntary blood donation scenarios and a 'Do not donate blood' option. Study participants were recruited through an online survey platform company in China. Voluntary blood donation preferences and preferences by blood donation history were estimated with random-parameter logit models and interaction terms. In 2022, 1185 individuals enrolled in the study. Most participants had college education (92%). Generally, participants preferred longer paid leave, lower blood donation volumes and gifts after donation. Based on interaction analyses, experienced and inexperienced donors exhibited similar preferences. Campaigns to increase voluntary blood donation rates in China should consider implementing paid leave after voluntary blood donation, lower blood donation volumes and small gifts conferred after donation.

Assessing the Impact of Different Population Density Scenarios on Two-Wheeler Accident Characteristics at Intersections

Examining 1192 intersection car and two-wheeled vehicle collision accidents from the China In-Depth Accident Study (CIDAS) database, this study employs population density heat maps for precise assessment of surrounding population densities at accident sites. The K-Medoid clustering algorithm and silhouette coefficient were used to classify accidents into two distinct groups based on population density. Subsequent application of the random parameter logit model revealed key contributing factors to these accidents in varying population densities. The results show notable differences in factors such as collision direction of two-wheeled vehicles, types of accident conflict, road conditions, and traffic flow, depending on the population density. Based on these conclusions, the research can inform differentiated risk prediction for two-wheeled vehicle accidents at intersections and provide insights for intersection design in various population density scenarios.

Consumers’ valuation of blockchain-based food traceability: role of consumer ethnocentrism and communication via QR codes

PurposeThis study aims to investigate the impact of consumer ethnocentrism on consumers' evaluation of blockchain-based traceability information. It also examined how the use of quick response (QR) codes for traceability affects consumers' evaluation of traceable food products.Design/methodology/approachAn online choice experiment was conducted to determine consumers' evaluation of the blockchain-based traceability of Feta cheese with a quota sample of 715 Greek consumers. Pearson bivariate correlation and mean comparison were used to examine the relationship between consumer ethnocentrism and QR use behaviour. Random parameter logit models were employed to examine consumers’ valuation of the examined attributes and interaction terms.FindingsThe results show that ethnocentric consumers are willing to pay more for blockchain-based traceability information. Ethnocentric consumers tend to scan QR codes with traceability information. Spending more time reading traceability information embedded in QR codes does not lead to a higher willingness-to-pay (WTP) for traceable food products.Practical implicationsThe findings suggest that patriotic marketing messages can draw consumers' attention to blockchain-based traceability information. The modest WTP for and low familiarity with blockchain-based traceability systems raise the need for educating consumers regarding the benefits of blockchain in traceability systems.Originality/valueThis is the first study to provide timely empirical evidence of a positive WTP for blockchain-based traceability information for a processed dairy product. This study is the first to attempt to distinguish the effects of the intention to scan QR codes and reading information embedded in QR codes on consumers’ valuation of food attributes.

Can the General Public Be a Proxy for an "At-Risk" Group in a Patient Preference Study? A Disease Prevention Example in Rheumatoid Arthritis.

When selecting samples for patient preference studies, it may be difficult or impractical to recruit participants who are eligible for a particular treatment decision. However, a general public sample may not be an appropriate proxy. This study compares preferences for rheumatoid arthritis (RA) preventive treatments between members of the general public and first-degree relatives (FDRs) of confirmed RA patients to assess whether a sample of the general public can be used as a proxy for FDRs. Participants were asked to imagine they were experiencing arthralgia and had screening tests indicating a 60% chance of developing RA within 2 yrs. Using a discrete choice experiment, participants were offered a series of choices between no treatment and 2 unlabeled hypothetical treatments to reduce the risk of RA. To assess data quality, time to complete survey sections and comprehension questions were assessed. A random parameter logit model was used to obtain attribute-level estimates, which were used to calculate relative importance, maximum acceptable risk (MAR), and market shares of hypothetical preventive treatments. The FDR sample (n = 298) spent more time completing the survey and performed better on comprehension questions compared with the general public sample (n = 982). The relative importance ranking was similar between the general public and FDR participant samples; however, other relative preference measures involving weights including MARs and market share differed between groups, with FDRs having numerically higher MARs. In the context of RA prevention, the general public (average risk) may be a reasonable proxy for a more at-risk sample (FDRs) for overall relative importance ranking but not weights. The rationale for a proxy sample should be clearly justified. Participants from the general public were compared to first-degree relatives on their preferences for rheumatoid arthritis (RA) preventive treatments using a discrete choice experiment.Preferences were similar between groups in terms of the most important and least important attributes of preventive treatments, with effectiveness being the most important attribute. However, relative weights differed.Attention to the survey and predicted market shares of hypothetical RA preventive treatments differed between the general public and first-degree relatives.The general public may be a reasonable proxy for an at-risk group for patient preferences ranks but not weights in the disease prevention context; however, care should be taken in sample selection for patient preference studies when choosing nonpatients.

Adult, adolescent, and caregiver preferences for attributes of topical treatments for mild-to-moderate atopic dermatitis: a discrete-choice experiment

Purpose: Topical treatments for mild-to-moderate (MM) atopic dermatitis (AD) include emollients, corticosteroids, calcineurin inhibitors, a Janus kinase inhibitor, and a phosphodiesterase 4 inhibitor, which differ in multiple ways. This study aimed to quantify the conditional relative importance (CRI) of attributes of topical treatments for MM AD among adult and adolescent patients and caregivers of children with MM AD. Materials and methods: A discrete-choice experiment (DCE) survey was administered to US adults and adolescents with MM AD and caregivers of children with MM AD. Each choice task comprised 2 hypothetical topical treatments characterized by efficacy, adverse events, vehicle, and application frequency. Data were analyzed using a random-parameters logit model to calculate the CRI of each attribute. Results and conclusions: 300 adults, 331 adolescents, and 330 caregivers completed the DCE. Avoiding changes in skin color (CRI 29.0) and time until itch improves (26.6) were most important to adults, followed by time until clear/almost clear skin (17.8). Application frequency (3.0) did not have a statistically significant impact on adults’ choices. Adolescents were less concerned about changes in skin color than adults or caregivers; caregivers were less concerned about time until clear/almost clear skin than patients. Physicians should consider age-relevant aspects of preferences in treatment discussions with patients and caregivers.

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

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