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

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.

[1]  L. Thurstone.  A law of comparative judgment. , 1994 .

[2]  Richard D. Smith,et al.  Developing a Capability-Based Questionnaire for Assessing Well-Being in Patients with Chronic Pain , 2014, Social Indicators Research.

[3]  J. Bond,et al.  Developing attributes for a generic quality of life measure for older people: preferences or capabilities? , 2006, Social science & medicine.

[4]  Joffre Swait,et al.  ADVANCED CHOICE MODELS , 2006 .

[5]  P. Anand,et al.  Operationalising the capability approach for outcome measurement in mental health research. , 2013, Social science & medicine.

[6]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[7]  Aki Tsuchiya,et al.  Binary Choice Health State Valuation and Mode of Administration: Head-to-Head Comparison of Online and CAPI , 2013, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[8]  George Loewenstein,et al.  Whose quality of life? A commentary exploring discrepancies between health state evaluations of patients and the general public , 2003, Quality of Life Research.

[9]  A. Sen,et al.  The Standard of Living: The Standard of Living: Lecture II, Lives and Capabilities , 1987 .

[10]  J. Louviere,et al.  The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models , 1993 .

[11]  Rona Campbell,et al.  Variables associated with attendance at, and the perceived helpfulness of, meetings for people with multiple sclerosis. , 2003, Health & social care in the community.

[12]  E. Stamuli.  Health outcomes in economic evaluation: who should value health? , 2011, British medical bulletin.

[13]  Jordan J. Louviere,et al.  Best-Worst Scaling: Theory, Methods and Applications , 2015 .

[14]  Terry N. Flynn,et al.  Are Efficient Designs Used in Discrete Choice Experiments Too Difficult for Some Respondents? A Case Study Eliciting Preferences for End-of-Life Care , 2016, PharmacoEconomics.

[15]  J. Louviere,et al.  Some probabilistic models of best, worst, and best–worst choices , 2005 .

[16]  András Simonovits,et al.  Optimal Design of Pension Rule with Flexible Retirement: The Two-Type Case , 2006 .

[17]  A. Gawande.  Being mortal : illness, medicine and what matters in the end , 2014 .

[18]  Joffre Swait,et al.  Distinguishing taste variation from error structure in discrete choice data , 2000 .

[19]  Rb Jones.  Variables associated with attendance and outcome in agoraphobics offered treatment by group in vivo exposure. , 1988 .

[20]  F. D. de Charro,et al.  Sensitivity and perspective in the valuation of health status: whose values count? , 2000, Health economics.

[21]  David W. Johnson,et al.  Factors influencing patient choice of dialysis versus conservative care to treat end-stage kidney disease , 2012, Canadian Medical Association Journal.

[22]  A. Sen,et al.  Capability and Well-Being , 1991 .

[23]  A. Gawande.  Quantity and Quality of Life: Duties of Care in Life-Limiting Illness. , 2016, JAMA.

[24]  T. Peters,et al.  Best--worst scaling: What it can do for health care research and how to do it. , 2007, Journal of health economics.

[25]  C. Normand.  Measuring outcomes in palliative care: limitations of QALYs and the road to PalYs. , 2009, Journal of pain and symptom management.

[26]  Kevin J. Boyle,et al.  Investigating Internet and Mail Implementation of Stated-Preference Surveys While Controlling for Differences in Sample Frames , 2016 .

[27]  P. Dolan.  Whose Preferences Count? , 1999, Medical decision making : an international journal of the Society for Medical Decision Making.

[28]  Margaret F. Barton.  Conditional Logit Analysis of FCC Decisionmaking , 1979 .

[29]  David Parkin,et al.  Is there a case for using visual analogue scale valuations in cost-utility analysis? , 2006, Health economics.

[30]  Michiel C.J. Bliemer,et al.  Constructing Efficient Stated Choice Experimental Designs , 2009 .

[31]  I. Higginson,et al.  A new approach to eliciting patients' preferences for palliative day care: the choice experiment method. , 2005, Journal of pain and symptom management.

[32]  C. Normand.  Setting priorities in and for end-of-life care: challenges in the application of economic evaluation , 2012, Health Economics, Policy and Law.

[33]  M. Nussbaum.  CAPABILITIES AS FUNDAMENTAL ENTITLEMENTS: SEN AND SOCIAL JUSTICE , 2003 .

[34]  Mandy Ryan,et al.  Discrete choice experiments in health economics: a review of the literature. , 2012, Health economics.

[35]  E. Finkelstein,et al.  Preferences for end-of-life care among community-dwelling older adults and patients with advanced cancer: A discrete choice experiment. , 2015, Health policy.

[36]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[37]  T. Peters,et al.  An investigation of the construct validity of the ICECAP-A capability measure , 2012, Quality of Life Research.

[38]  J. Coast,et al.  Measuring and Valuing Outcomes for Care at the End of Life: The Capability Approach , 2016 .

[39]  T. Flynn.  Valuing citizen and patient preferences in health: recent developments in three types of best–worst scaling , 2010, Expert review of pharmacoeconomics & outcomes research.

[40]  J. Coast,et al.  A health economics response to the review of the Liverpool Care Pathway. , 2013, Journal of palliative medicine.

[41]  M. Qizilbash.  Well-Being, Adaptation and Human Limitations* , 2006, Royal Institute of Philosophy Supplement.

[42]  Tim J Peters,et al.  Prenatal risk factors for Caesarean section. Analyses of the ALSPAC cohort of 12,944 women in England. , 2005, International journal of epidemiology.

[43]  Jordan J. Louviere,et al.  Combining sources of preference data , 1998 .

[44]  Joanna Coast,et al.  Complex Valuation: Applying Ideas from the Complex Intervention Framework to Valuation of a New Measure for End-of-Life Care , 2016, PharmacoEconomics.

[45]  J. Coast,et al.  University of Birmingham Development of a self-report measure of capability wellbeing for adults: the ICECAP-A. , 2012 .

[46]  A. Sen,et al.  Choice, Welfare and Measurement , 1982 .

[47]  T. Peters,et al.  Scoring the Icecap-A Capability Instrument. Estimation of a UK General Population Tariff† , 2013, Health economics.

[48]  E. Finkelstein,et al.  Comparison of preferences for end-of-life care among patients with advanced cancer and their caregivers: A discrete choice experiment , 2015, Palliative medicine.

[49]  Joanna Coast,et al.  Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters. , 2010, Social science & medicine.

[50]  Deborah Marshall,et al.  Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. , 2013, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[51]  T. Flynn.  Using Conjoint Analysis and Choice Experiments to Estimate QALY Values , 2012, PharmacoEconomics.

[52]  T. Peters,et al.  Valuing the ICECAP capability index for older people. , 2008, Social science & medicine.

[53]  A. Briggs,et al.  Operationalising the capability approach as an outcome measure in public health: The development of the OCAP-18. , 2015, Social science & medicine.

[54]  J. Coast.  Strategies for the economic evaluation of end-of-life care: making a case for the capability approach , 2014, Expert review of pharmacoeconomics & outcomes research.

[55]  J. Coast,et al.  Development of a supportive care measure for economic evaluation of end-of-life care using qualitative methods , 2014, Palliative medicine.