Preference Paths and Their Kaizen Tasks for Small Samples.

BACKGROUND: Stated preference research currently lacks a form of evidence that is well suited for small samples. A preference path is a sequence of two or more choices showing the evolution of an object following an adaptive process. OBJECTIVES: The aims were to introduce preference paths and their kaizen tasks and to demonstrate how to analyze their evidence using a small sample. METHODS: Twenty respondents were assigned the same 16 profiles generated from an orthogonal array based on the five attributes of the EQ-5D-5L descriptive system. Each kaizen task began with an opt-out paired comparison (i.e., choosing between the initial 10-year profile and the opt-out "dying immediately"), followed by choosing three changes, and ended with a second paired comparison (final profile versus opt-out) if the respondent chose opt-out initially. By maximum likelihood with respondent clusters, we estimated the 20 main effects using conditional logit and Zermelo-Bradley-Terry (ZBT) specifications. RESULTS: Apart from demonstrating heterogeneity and profile effects, all main effect estimates were non-negative, and most were significant (15 for logit and all 20 for ZBT; p value < 0.05). Under the logit and ZBT specifications, the value of the worst EQ-5D-5L profile (55555) is - 0.920 quality-adjusted life years (QALYs) or - 1.478 QALYs, respectively. Furthermore, the findings illustrate a log-linear relationship between the logit and ZBT main effects. CONCLUSION: This paper demonstrates the feasibility of a stated-preference study that estimates 20 main effects using path evidence from 20 respondents (16 kaizen tasks, 15-min interviews). This approach shows promise for future application in stated-preference research, particularly in small samples.

Does Device or Connection Type Affect Health Preferences in Online Surveys?

BACKGROUND AND OBJECTIVE: Recent evidence has shown that online surveys can reliably collect preference data, which markedly decrease the cost of health preference studies and expand their representativeness. As the use of mobile technology continues to grow, we wanted to examine its potential impact on health preferences. METHODS: Two recently completed discrete choice experiments using members of the US general population (n = 15,292) included information on respondent device (cell phone, tablet, Mac, PC) and internet connection (business, cellular, college, government, residential). In this analysis, we tested for differences in respondent characteristics, participation, response quality, and utility values for the 5-level EQ-5D (EQ-5D-5L) by device and connection. RESULTS: Compared to Mac and PC users, respondents using a cell phone or tablet had longer completion times and were significantly more likely to drop out during the surveys (p < 0.001). Tablet users also demonstrated more logical inconsistencies (p = 0.05). Likewise, respondents using a cellular internet connection exhibit significantly less consistency in their health preferences. However, matched samples for tablets and cell phones produced similar EQ-5D-5L utility values (mean differences < 0.06 on a quality-adjusted life-year [QALY] scale for all potential health states). CONCLUSION: Allowing respondents to complete online surveys using a cell phone or tablet or over a cellular connection substantially increases the diversity of respondents and the likelihood of obtaining a representative sample, as many individuals have cell phones but not a computer. While the results showed systematic variability in participation and response quality by device and connection type, this study did not show any meaningful changes in utility values.

Examining the Association Between Maternal Smoking During Pregnancy and Child Behavior Problems Using Quality-Adjusted Life Years.

Objectives Examining the association between maternal smoking and losses in childhood health-related quality of life due to behavior problems provides parents and policymakers another tool for the valuation of smoking cessation during pregnancy. Methods Using the National Longitudinal Survey of Youth 1979 Child and Young Adult data, this study retrospectively examined a cohort of 4114 women and 8668 children. In addition to questions focusing on maternal smoking and general demographics, each survey included the Behavior Problems Index (BPI), a 28-item questionnaire with six subscales measuring childhood behavior problems (antisocial behavior, anxiousness/depression, headstrongness, hyperactivity, immature dependency, and peer conflict/social withdrawal). Responses to the BPI, completed by mothers with children ages 4-14, were summarized on a QALY scale using published preference weights. Results Children whose mothers smoked during pregnancy experience additional QALY losses of 0.181, on average, per year due to increased behavior problems. Boys suffered larger QALY losses associated with maternal smoking (0.242) compared to girls (0.119; p value = .021), regardless of age. Moreover, heavier smoking during pregnancy (i.e., 1 or more packs/day) was associated with larger QALY losses (0.282; p-value < .001). Conclusions for Practice These findings illustrate the burden of maternal smoking during pregnancy on child health, namely behavioral problems. The losses in QALYs may be incorporated into economic evaluations for smoking cessation interventions during pregnancy. Future research will investigate how maternal smoking following childbirth is associated with child QALYs.

Choice Defines Value: A Predictive Modeling Competition in Health Preference Research.

OBJECTIVE: To identify which specifications and approaches to model selection better predict health preferences, the International Academy of Health Preference Research (IAHPR) hosted a predictive modeling competition including 18 teams from around the world. METHODS: In April 2016, an exploratory survey was fielded: 4074 US respondents completed 20 out of 1560 paired comparisons by choosing between two health descriptions (e.g., longer life span vs. better health). The exploratory data were distributed to all teams. By July, eight teams had submitted their predictions for 1600 additional pairs and described their analytical approach. After these predictions had been posted online, a confirmatory survey was fielded (4148 additional respondents). RESULTS: The victorious team, "Discreetly Charming Econometricians," led by Michal Jakubczyk, achieved the smallest χ2, 4391.54 (a predefined criterion). Its primary scientific findings were that different models performed better with different pairs, that the value of life span is not constant proportional, and that logit models have poor predictive validity in health valuation. CONCLUSIONS: The results demonstrated the diversity and potential of new analytical approaches in health preference research and highlighted the importance of predictive validity in health valuation.

Comparing and transforming PROMIS utility values to the EQ-5D.

PURPOSE: Summarizing patient-reported outcomes (PROs) on a quality-adjusted life year (QALY) scale is an essential component to any economic evaluation comparing alternative medical treatments. While multiple studies have compared PRO items and instruments based on their psychometric properties, no study has compared the preference-based summary of the EQ-5D-3L and Patient Reported Outcomes Measurement Information System (PROMIS-29) instruments. As part of this comparison, a major aim of this manuscript is to transform PROMIS-29 utility values to an EQ-5D-3L scale. METHODS: A nationally representative survey of 2623 US adults completed the 29-item PROMIS health profile instrument (PROMIS-29) and the 3-level version of the EQ-5D instrument (EQ-5D-3L). Their responses were summarized on a health utility scale using published estimates. Using regression analysis, PROMIS-29 and EQ-5D-3L utility weights were compared with each other as well as with self-reported general health. RESULTS: PROMIS-29 utility weights were much lower than the EQ-5D-3L weights. However, a correlation coefficient of 0.769 between the utility values of the two instruments suggests that the main discordance is simply a difference in scale between the measures. It is also possible to map PROMIS-29 utility weights onto an EQ-5D-3L scale. EQ-5D-3L losses equal .1784 × (PROMIS-29 Losses).7286. CONCLUSIONS: The published estimates of the PROMIS-29 produce lower utility values than many other health instruments. Mapping the PROMIS-29 estimates to an EQ-5D-3L scale alleviates this issue and allows for a more straightforward comparison between the PROMIS-29 and other common health instruments.

Prevalence and Losses in Quality-Adjusted Life Years of Child Health Conditions: A Burden of Disease Analysis.

OBJECTIVES: To estimate the prevalence and losses in quality-adjusted life years (QALYs) associated with 20 child health conditions. METHODS: Using data from the 2009-2010 National Survey of Children with Special Health Care Needs, preference weights were applied to 14 functional difficulties to summarize the quality of life burden of 20 health conditions. RESULTS: Among the 14 functional difficulties, "a little trouble with breathing" had the highest prevalence (37.1 %), but amounted to a loss of just 0.16 QALYs from the perspective of US adults. Though less prevalent, "a lot of behavioral problems" and "chronic pain" were associated with the greatest losses (1.86 and 3.43 QALYs). Among the 20 conditions, allergies and asthma were the most prevalent but were associated with the least burden. Muscular dystrophy and cerebral palsy were among the least prevalent and most burdensome. Furthermore, a scatterplot shows the association between condition prevalence and burden. CONCLUSIONS: In child health, condition prevalence is negatively associated with quality of life burden from the perspective of US adults. Both should be considered carefully when evaluating the appropriate role for public health prevention and interventions.