Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life

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Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life. / Huynh, Elisabeth; Coast, Joanna; Rose, John; Kinghorn, Philip; Flynn, Terry.

In: Social Science and Medicine, Vol. 189, 01.09.2017, p. 114-128.

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Huynh, Elisabeth ; Coast, Joanna ; Rose, John ; Kinghorn, Philip ; Flynn, Terry. / Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life. In: Social Science and Medicine. 2017 ; Vol. 189. pp. 114-128.

Bibtex

@article{4983ba1c2cf24aa381bbcddb2214710f,
title = "Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life",
abstract = "End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure with a large number of members of the general public could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life.",
keywords = "UK , capability, measurement , ICECAP measures , wellbeing , end of life , best-worst scaling , discrete choice experiment",
author = "Elisabeth Huynh and Joanna Coast and John Rose and Philip Kinghorn and Terry Flynn",
year = "2017",
month = sep
day = "1",
doi = "10.1016/j.socscimed.2017.07.012",
language = "English",
volume = "189",
pages = "114--128",
journal = "Social Science and Medicine",
issn = "0037-7856",
publisher = "Reed-Elsevier (India) Private Limited",

}

RIS

TY - JOUR

T1 - Values for the ICECAP-Supportive Care Measure (ICECAP-SCM) for use in economic evaluation at end of life

AU - Huynh, Elisabeth

AU - Coast, Joanna

AU - Rose, John

AU - Kinghorn, Philip

AU - Flynn, Terry

PY - 2017/9/1

Y1 - 2017/9/1

N2 - End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure with a large number of members of the general public could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life.

AB - End of life care may have elements of value that go beyond health. A generic measure of the benefits of end of life care could be helpful to decision makers. Such a measure, based on the capability approach, has recently been developed: the ICECAP Supportive Care Measure. This paper reports the first valuation exercise for that measure, with data from 6020 individuals collected from an on-line general population panel during June 2013. Individuals were asked to complete a stated choice experiment that combined best-worst scaling and a standard discrete choice experiment. Analysis of the best-worst data used limited dependent variable models within the random utility framework including the multinomial logit models and latent class choice model analysis. Exploratory steps were taken to determine the similarity of the best-worst and DCE data before formal testing and pooling of the two data sources. Combined data were analysed in a heteroscedastic conditional logit model adjusting for continuous scale. Two sets of tariffs were generated, one from the best-worst data capturing only main effects, and a second from the pooled data allowing for two-way interactions. Either tariff could be used in economic evaluation of interventions at the end of life, although there are advantages and disadvantages with each. This extensive valuation exercise for the ICECAP Supportive Care Measure with a large number of members of the general public could be complemented in the future with best-worst scaling studies amongst those experiencing the end of life.

KW - UK

KW - capability

KW - measurement

KW - ICECAP measures

KW - wellbeing

KW - end of life

KW - best-worst scaling

KW - discrete choice experiment

U2 - 10.1016/j.socscimed.2017.07.012

DO - 10.1016/j.socscimed.2017.07.012

M3 - Article

VL - 189

SP - 114

EP - 128

JO - Social Science and Medicine

JF - Social Science and Medicine

SN - 0037-7856

ER -