| 1. |
- Coast, Joanna, et al.
(författare)
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Valuing the ICECAP capability index for older people.
- 2008
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Ingår i: Social Science and Medicine. - : Elsevier BV. - 0277-9536 .- 1873-5347. ; 67:5, s. 874-82
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Tidskriftsartikel (refereegranskat)abstract
- This paper reports the first application of the capabilities approach to the development and valuation of an instrument for use in the economic evaluation of health and social care interventions. The ICECAP index of capability for older people focuses on quality of life rather than health or other influences on quality of life, and is intended to be used in decision making across health and social care in the UK. The measure draws on previous qualitative work in which five conceptual attributes were developed: attachment, security, role, enjoyment and control. This paper details the innovative use within health economics of further iterative qualitative work in the UK among 19 informants to refine lay terminology for each of the attributes and levels of attributes used in the eventual index. For the first time within quality of life measurement for economic evaluation, a best-worst scaling exercise has been used to estimate general population values (albeit for the population of those aged 65+ years) for the levels of attributes, with values anchored at one for full capability and zero for no capability. Death was assumed to be a state in which there is no capability. The values obtained indicate that attachment is the attribute with greatest impact but all attributes contribute to the total estimation of capability. Values that were estimated are feasible for use in practical applications of the index to measure the impact of health and social care interventions.
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| 2. |
- Flynn, Terry N, et al.
(författare)
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Best--worst scaling : What it can do for health care research and how to do it.
- 2007
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Ingår i: Journal of Health Economics. - : Elsevier BV. - 0167-6296 .- 1879-1646. ; 26:1, s. 171-89
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Tidskriftsartikel (refereegranskat)abstract
- Statements like "quality of care is more highly valued than waiting time" can neither be supported nor refuted by comparisons of utility parameters from a traditional discrete choice experiment (DCE). Best--worst scaling can overcome this problem because it asks respondents to perform a different choice task. However, whilst the nature of the best--worst task is generally understood, there are a number of issues relating to the design and analysis of a best--worst choice experiment that require further exposition. This paper illustrates how to aggregate and analyse such data and using a quality of life pilot study demonstrates how richer insights can be drawn by the use of best--worst tasks.
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| 3. |
- Flynn, Terry N, et al.
(författare)
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Estimating preferences for a dermatology consultation using Best-Worst Scaling : comparison of various methods of analysis.
- 2008
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 8, s. 76-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Additional insights into patient preferences can be gained by supplementing discrete choice experiments with best-worst choice tasks. However, there are no empirical studies illustrating the relative advantages of the various methods of analysis within a random utility framework.METHODS: Multinomial and weighted least squares regression models were estimated for a discrete choice experiment. The discrete choice experiment incorporated a best-worst study and was conducted in a UK NHS dermatology context. Waiting time, expertise of doctor, convenience of attending and perceived thoroughness of care were varied across 16 hypothetical appointments. Sample level preferences were estimated for all models and differences between patient subgroups were investigated using covariate-adjusted multinomial logistic regression.RESULTS: A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it). Adjusting for covariates showed that patients who felt particularly affected by their skin condition during the previous week displayed extreme preference for short/no waiting time and were less concerned about other aspects of the appointment. Higher levels of educational attainment were associated with larger differences in utility between the levels of all attributes, although the attributes per se had the same impact upon choices as those with lower levels of attainment. The study also demonstrated the high levels of agreement between summary analyses using weighted least squares and estimates from multinomial models.CONCLUSION: Robust policy-relevant information on preferences can be obtained from discrete choice experiments incorporating best-worst questions with relatively small sample sizes. The separation of the effects due to attribute impact from the position of levels on the latent utility scale is not possible using traditional discrete choice experiments. This separation is important because health policies to change the levels of attributes in health care may be very different from those aiming to change the attribute impact per se. The good approximation of summary analyses to the multinomial model is a useful finding, because weighted least squares choice totals give better insights into the choice model and promote greater familiarity with the preference data.
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| 4. |
- Flynn, Terry Nicholas, et al.
(författare)
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Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters.
- 2010
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Ingår i: Social Science and Medicine. - : Elsevier BV. - 0277-9536 .- 1873-5347. ; 70:12, s. 1957-65
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Tidskriftsartikel (refereegranskat)abstract
- Health services researchers are increasingly using discrete choice experiments (DCEs) to model a latent variable, be it health, health-related quality of life or utility. Unfortunately it is not widely recognised that failure to model variance heterogeneity correctly leads to bias in the point estimates. This paper compares variance heterogeneity latent class models with traditional multinomial logistic (MNL) regression models. Using the ICECAP-O quality of life instrument which was designed to provide a set of preference-based general quality of life tariffs for the UK population aged 65+, it demonstrates that there is both mean and variance heterogeneity in preferences for quality of life, which covariate-adjusted MNL is incapable of separating. Two policy-relevant mean groups were found: one group that particularly disliked impairments to independence was dominated by females living alone (typically widows). Males who live alone (often widowers) did not display a preference for independence, but instead showed a strong aversion to social isolation, as did older people (of either sex) who lived with a spouse. Approximately 6-10% of respondents can be classified into a third group that often misunderstood the task. Having a qualification of any type and higher quality of life was associated with smaller random component variances. This illustrates how better understanding of random utility theory enables richer inferences to be drawn from discrete choice experiments. The methods have relevance for all health studies using discrete choice tasks to make inferences about a latent scale, particular QALY valuation exercises that use DCEs, best-worst scaling and ranking tasks.
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