Evaluating credit applications: A validation of multiattribute utility weight elicitation techniques

Abstract Background Snack eating occasions contribute approximately a third of children’s energy intake, with approximately half of all unhealthy foods consumed during snack times. Therefore, it is critical to understand the drivers of primary food providers’ snack provision. The study aims were to determine the relative importance of physical resources and social supports when primary food providers are choosing snacks to provide to their child, and to investigate how these attributes differ in social versus non-social occasions, and between subgroups of primary food providers based on socio-economic position. Methods Primary food providers of three to seven-year olds completed an online discrete choice experiment, by making trade-offs when completing repeated, hypothetical choice tasks on the choice of snacks to provide to their child in: 1) non-social and 2) social condition. Choice tasks included two alternatives consisting of varying attribute (i.e. factor) levels, and an opt-out option. The order of conditions shown were randomized across participants. Multinomial logit model analyses were used to determine utility weights for each attribute. Results Two-hundred and twenty-five primary food providers completed the study, providing 1125 choice decisions per condition. In the non-social condition, the top three ranked attributes were type of food (utility weight 1.94, p < 0.001), child resistance (− 1.62, p < 0.001) and co-parent support (0.99, p < 0.001). In the social condition, top ranking attributes were child resistance (utility weight − 1.50, p < 0.001), type of food (1.38, p < 0.001) and co-parent support (1.07, p < 0.001). In both conditions, time was not a significant influence and cost was of lowest relative importance. Subgroup analyses revealed cost was not a significant influence for families from higher socio-economic backgrounds. Conclusions Type of food, child resistance and co-parent support were of greatest relative importance in primary food providers’ snack provision decision-making, regardless of social condition or socio-economic position. In designing future interventions to reduce unhealthy snacks, researchers should prioritize these influences, to better support primary food providers in changing their physical and social opportunity. Trial registration Australian New Zealand Clinical Trials Registry no. ACTR N12618001173280

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United States Utility Algorithm for the EORTC QLU-C10D, a Multiattribute Utility Instrument Based on a Cancer-Specific Quality-of-Life Instrument

Medical Decision Making ◽

10.1177/0272989x211003569 ◽

2021 ◽

pp. 0272989X2110035

Author(s):

Dennis A. Revicki ◽

Madeleine T. King ◽

Rosalie Viney ◽

A. Simon Pickard ◽

Rebecca Mercieca-Bebber ◽

...

Keyword(s):

Quality Of Life ◽

United States ◽

Emotional Functioning ◽

Health State ◽

Life Questionnaire ◽

Multiattribute Utility ◽

Value Set ◽

Eortc Qlq C30 ◽

Eortc Qlq

Background The EORTC QLU-C10D is a multiattribute utility measure derived from the cancer-specific quality-of-life questionnaire, the EORTC QLQ-C30. The QLU-C10D contains 10 dimensions (physical, role, social and emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems). The objective of this study was to develop a United States value set for the QLU-C10D. Methods A US online panel was quota recruited to achieve a representative sample for sex, age (≥18 y), race, and ethnicity. Respondents undertook a discrete choice experiment, each completing 16 choice-pairs, randomly assigned from a total of 960 choice-pairs. Each pair included 2 QLU-C10D health states and duration. Data were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life-year framework. Utility weights were calculated as the ratio of each dimension-level coefficient to the coefficient for life expectancy. Results A total of 2480 panel members opted in, 2333 (94%) completed at least 1 choice-pair, and 2273 (92%) completed all choice-pairs. Within dimensions, weights were generally monotonic. Physical functioning, role functioning, and pain were associated with the largest utility weights. Cancer-specific dimensions, such as nausea and bowel problems, were associated with moderate utility decrements, as were general issues such as problems with emotional functioning and social functioning. Sleep problems and fatigue were associated with smaller utility decrements. The value of the worst health state was 0.032, which was slightly greater than 0 (equivalent to being dead). Conclusions This study provides the US-specific value set for the QLU-C10D. These estimated health state scores, based on responses to the EORTC QLQ-C30 questionnaire, can be used to evaluate the cost-utility of oncology treatments.

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A Multicriteria Decision Model for Assessment of Failure Consequences in the RCM Approach

Mathematical Problems in Engineering ◽

10.1155/2015/729865 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Marcelo H. Alencar ◽

Adiel T. de Almeida

Keyword(s):

Failure Mode ◽

Decision Model ◽

Probability Distributions ◽

Maintenance Planning ◽

Multiattribute Utility ◽

Five Dimensions ◽

Multicriteria Decision ◽

Reliability Centered Maintenance ◽

Trade Offs ◽

And Behavior

This paper proposes a multicriteria decision model based on MAUT (Multiattribute Utility Theory) incorporated into an RCM (Reliability Centered Maintenance) approach in order to provide a better assessment of the consequences of failure, allowing a more effective maintenance planning. MAUT provides an evaluation of probability distributions on each attribute as well as trade-offs involving lotteries. The model proposed takes advantage of such evaluations and it also restructures consequence groups established in an RCM approach into new five dimensions. As a result, overall indices of utility are computed for each failure mode analyzed. With these values, the ranking of the alternatives is established. The decision-maker’s preferences are taken into account so that the final result for each failure mode incorporates subjective aspects based on the decision-maker’s perceptions and behavior.

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