Person-centred care and dementia-care mapping both seem to reduce agitation in people with dementia in residential care.

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This new algorithm will enable the Australian community values to be reflected in future economic evaluations.

DCEs are a feasible approach to the estimation of utility weights for more complex multi-attribute utility instruments such as the EQ-5D-5L.

Cost-utility models are increasingly used in many countries to establish whether the cost of a new intervention can be justified in terms of health benefits. Health-state utility (HSU) estimates (the preference for a given state of health on a cardinal scale where 0 represents dead and 1 represents full health) are typically among the most important and uncertain data inputs in cost-utility models. Clinical trials represent an important opportunity for the collection of health-utility data. However, trials designed primarily to evaluate efficacy and safety often present challenges to the optimal collection of HSU estimates for economic models. Careful planning is needed to determine which of the HSU estimates may be measured in planned trials; to establish the optimal methodology; and to plan any additional studies needed. This report aimed to provide a framework for researchers to plan the collection of health-utility data in clinical studies to provide high-quality HSU estimates for economic modeling. Recommendations are made for early planning of health-utility data collection within a research and development program; design of health-utility data collection during protocol development for a planned clinical trial; design of prospective and cross-sectional observational studies and alternative study types; and statistical analyses and reporting.

BackgroundThe EORTC QLU-C10D is a new multi-attribute utility instrument derived from the widely used cancer-specific quality-of-life (QOL) questionnaire, EORTC QLQ-C30. The QLU-C10D contains ten dimensions (Physical, Role, Social and Emotional Functioning; Pain, Fatigue, Sleep, Appetite, Nausea, Bowel Problems), each with four levels. To be used in cost-utility analysis, country-specific valuation sets are required.ObjectiveThe aim of this study was to provide Australian utility weights for the QLU-C10D.MethodsAn Australian online panel was quota-sampled to ensure population representativeness by sex and age (≥ 18 years). Participants completed a discrete choice experiment (DCE) consisting of 16 choice-pairs. Each pair comprised two QLU-C10D health states plus life expectancy. Data were analysed using conditional logistic regression, parameterised to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each QOL dimension-level coefficient to the coefficient on life expectancy.ResultsA total of 1979 panel members opted in, 1904 (96%) completed at least one choice-pair, and 1846 (93%) completed all 16 choice-pairs. Dimension weights were generally monotonic: poorer levels within each dimension were generally associated with greater utility decrements. The dimensions that impacted most on choice were, in order, Physical Functioning, Pain, Role Functioning and Emotional Functioning. Oncology-relevant dimensions with moderate impact were Nausea and Bowel Problems. Fatigue, Trouble Sleeping and Appetite had relatively small impact. The value of the worst health state was -0.096, somewhat worse than death.ConclusionsThis study provides the first country-specific value set for the QLU-C10D, which can facilitate cost-utility analyses when applied to data collected with the EORTC QLQ-C30, prospectively and retrospectively.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-017-0582-5) contains supplementary material, which is available to authorized users.

Conventionally, generic quality-of-life health states, defined within multi-attribute utility instruments, have been valued using a Standard Gamble or a Time Trade-Off. Both are grounded in expected utility theory but impose strong assumptions about the form of the utility function. Preference elicitation tasks for both are complicated, limiting the number of health states that each respondent can value and, therefore, that can be valued overall. The usual approach has been to value a set of the possible health states and impute values for the remainder. Discrete Choice Experiments (DCEs) offer an attractive alternative, allowing investigation of more flexible specifications of the utility function and greater coverage of the response surface. We designed a DCE to obtain values for EQ-5D health states and implemented it in an Australia-representative online panel (n = 1,031). A range of specifications investigating non-linear preferences with respect to time and interactions between EQ-5D levels were estimated using a random-effects probit model. The results provide empirical support for a flexible utility function, including at least some two-factor interactions. We then constructed a preference index such that full health and death were valued at 1 and 0, respectively, to provide a DCE-based algorithm for Australian cost-utility analyses.

s. DCEs can be used to investigate preferences for health profiles and to estimate utility weights for multi-attribute utility instruments. Australian cost-utility analyses can now use domestic SF-6D weights. The comparability of DCE results to those using other elicitation methods for estimating utility weights for quality-adjusted life-year calculations should be further investigated.