8,694 research outputs found
A comparison of methods for converting DCE values onto the full health-dead QALY scale
Cardinal preference elicitation techniques such as time trade-off (TTO) and Standard Gamble (SG) receive criticism for their complexity and difficulties in using them in more vulnerable populations. Ordinal techniques such as discrete choice experiment (DCE) and Best Worst Scaling (BWS) are easier, but values generated by them are not anchored onto the full health-dead 1-0 QALY scale required for use in economic evaluation. This paper explores new methods for converting modelled DCE latent values onto the full health-dead QALY scale: (1) anchoring assuming worst state is equal to being dead; (2) anchoring DCE values using dead as valued in the DCE; (3) anchoring DCE values using TTO value for worst state; (4) mapping DCE values onto TTO; (5) combining DCE and TTO data in a hybrid model. We use postal DCE data (n=263) and TTO data (n=307) collected by interview in a general population valuation study of an asthma condition-specific measure (AQL-5D). Methods (4) and (5) using mapping and hybrid models perform best; the anchor-based methods perform relatively poorly. These new methods have a useful role for producing values on the QALY scale from ordinal techniques such as DCE and BWS for use in cost utility analyses
Relational discrepancies and emotion : The moderating roles of relationship type and relational discrepancy valence
Peer reviewedPostprin
The total assessment profile, volume 2
Appendices are presented which include discussions of interest formulas, factors in regionalization, parametric modeling of discounted benefit-sacrifice streams, engineering economic calculations, and product innovation. For Volume 1, see
Drivers of consumer’s adoption of innovative food
Over the last years, food safety, health and environmental issues are a few among many other reasons that force consumers to adopt new innovative food products – organic, private label, genetically modified and functional – as part of their consumption. This spectacular shift of the consumption forwards “innovative” food products attracts the interest of the analyst as it can shed new light on consumer’s behaviour and on modeling and understanding better his long-term behaviour. Thus, this study attempts to investigate the factors that influence consumer’s decision in purchasing either traditional or new innovative products and to what extend this shift between those two groups of products is related to pre-defined elements. This is achieved by employing both descriptive statistics and multivariate analysis. Two-step cluster analysis was used to explore the different levels of innovative products adoption and a categorical regression model was estimated to determine the relation between consumer’s characteristics and willingness to adopt innovative products.adoption, consumption, food, innovative products, multivariate analysis., Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety,
Selection of Ordinally Scaled Independent Variables
Ordinal categorial variables are a common case in regression
modeling. Although the case of ordinal response variables has been well investigated, less work has been done concerning ordinal predictors. This article deals with the selection of ordinally scaled independent variables in the classical linear model, where the ordinal structure is taken into account by use of a difference penalty on adjacent dummy coefficients. It is shown how the Group Lasso can be used for the selection of ordinal predictors, and an alternative blockwise Boosting procedure is proposed. Emphasis is placed on the application of the presented methods to the (Comprehensive) ICF Core Set for chronic widespread pain.
The paper is a preprint of an article accepted for publication in the Journal of the Royal Statistical Society Series C (Applied Statistics). Please use the journal version for citation
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