Using a discrete choice experiment to estimate health state utility values

Bansback, Nick; Brazier, John; Tsuchiya, Aki; Anis, Aslam H.

doi:10.1016/j.jhealeco.2011.11.004

Cited by 210 publications

(310 citation statements)

References 34 publications

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“…Under Type 0, negative values are interpolated from within the data, when the combined disutility of an EQ‐5D‐5L state cancels out the utility of full health. For further details, see Bansback et al (2012).

This is a non‐iterative TTO with no lead time (NITTO).

…”

Section: Methodsmentioning

confidence: 99%

“…Thus, each of the four types above is presented in two different modes. a tabulated presentation taken from Bansback et al (2012), Mulhern et al (2014), and Mulhern et al (2017); anda visual aid similar to TTO boards used in Gudex (1994), Dolan (1997), and Devlin et al (2013). …”

Section: Methodsmentioning

confidence: 99%

“…a tabulated presentation taken from Bansback et al (2012), Mulhern et al (2014), and Mulhern et al (2017); and…”

Section: Methodsmentioning

confidence: 99%

“…Discrete Choice Experiment (DCE) is a method that does not involve iteration because each task is independent and is therefore free of iteration bias. DCE with duration as an attribute (DCE TTO ) can generate health state values anchored at 1 for full health and 0 for being dead at the aggregate level (Bansback, Brazier, Tsuchiya, & Anis, 2012). However, a disadvantage of DCE TTO is the cognitive burden: each pairwise choice task in a DCE TTO of EQ‐5D‐5L comprises 12 pieces of information (the five dimensions of health and duration for one scenario, and the same for the other scenario), and all of these can change from one task to another.…”

Section: Introductionmentioning

confidence: 99%

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An exploration of the non‐iterative time trade‐off method to value health states

et al. 2018

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Time Trade‐Off (TTO) usually relies on “iteration,” which is susceptible to bias. Discrete Choice Experiment with duration (or DCETTO) is free of such bias, but respondents find this cognitively more challenging. This paper explores non‐iterative TTO with or without lead time: NI(LT)TTO. In NI(LT)TTO, respondents see a series of independent pairwise choices without iteration (similar to DCETTO), but one of the two scenarios always involves full health for a shorter duration (similar to TTO). We compare three different “types” of NI(LT)TTO relative to DCETTO. Each type is presented in two “modes”: (a) verbally tabulated (as in a DCE) and (b) with visual aids (as in a TTO). The study has 8 survey variants, each with 12 experimental choice tasks and a 13th task with a logically determined answer. Data on the 12 experimental choices from an online survey of 6,618 respondents are modelled, by variant, using conditional logistic regressions. The results indicate that NI(LT)TTO is feasible, but some relatively mild states appear to have implausibly low predicted values, and the range of predicted values is much narrower than in DCETTO. The presentation of NI(LT)TTO tasks needs further improvement.

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This is a non‐iterative TTO with no lead time (NITTO).

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…a tabulated presentation taken from Bansback et al (2012), Mulhern et al (2014), and Mulhern et al (2017); and…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An exploration of the non‐iterative time trade‐off method to value health states

et al. 2018

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“…This puts constraints on the possible biases that are associated with monadic measurement approaches. One recent study attempted to compare the TTO technique with a DCE that includes a time attribute [71]. The authors showed that adding a time attribute to a health state is feasible for eliciting health-state values; however, more research is needed to verify that the constant proportional TTO in such a DCE is not violated.…”

Section: Probabilistic Choice Models Versus Other Methodologiesmentioning

confidence: 99%

Probabilistic choice models in health-state valuation research: background, theories, assumptions and applications

Arons

Krabbe

2013

Expert Review of Pharmacoeconomics & Outcomes Research

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Interest is rising in measuring subjective health outcomes, such as treatment outcomes that are not directly quantifiable (functional disability, symptoms, complaints, side effects and healthrelated quality of life). Health economists in particular have applied probabilistic choice models in the area of health evaluation. They increasingly use discrete choice models based on random utility theory to derive values for healthcare goods or services. Recent attempts have been made to use discrete choice models as an alternative method to derive values for health states. In this article, various probabilistic choice models are described according to their underlying theory. A historical overview traces their development and applications in diverse fields. The discussion highlights some theoretical and technical aspects of the choice models and their similarity and dissimilarity. The objective of the article is to elucidate the position of each model and their applications for health-state valuation.

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