Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data

Older people's valuation of health-related aspects of quality of life may be altered by response shift, where they lower expectations of aspects of well-being that are believed to naturally deteriorate with age. Policy-makers may wish to adjust estimated preferences if these reflect past inequities in health funding rather than the true production possibilities. Response shift might be quantified by changing the context of the choice task. The ICECAP-O valuation exercise achieved this by asking a binary choice holistic decision of respondents, in addition to the case 2 best-worst choice task among the five attributes. Answers to the former are more likely to be subject to response shift since they involve traditional trade-offs. Answers to the latter reflect only ‘relative disutility’ of various impairments.

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

[2]  M. Sprangers,et al.  Integrating response shift into health-related quality of life research: a theoretical model. , 1999, Social science & medicine.

[3]  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.

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

[5]  Linda Court Salisbury,et al.  Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test , 2010, Mark. Sci..

[6]  P. Dolan Developing methods that really do value the ‘Q’ in the QALY , 2008, Health Economics, Policy and Law.

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

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

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

[10]  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.

[11]  J. Louviere,et al.  Probabilistic models of set-dependent and attribute-level best-worst choice , 2008 .

[12]  Denzil G. Fiebig,et al.  The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity , 2010, Mark. Sci..

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

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

[15]  Zvi Griliches,et al.  Specification Error in Probit Models , 1985 .

[16]  D. Kahneman,et al.  Interpretations of Utility and Their Implications for the Valuation of Health , 2008 .

[17]  P. Ubel,et al.  The impact of considering adaptation in health state valuation. , 2005, Social science & medicine.

[18]  Peter Burge,et al.  Best-worst scaling vs. discrete choice experiments: an empirical comparison using social care data. , 2011, Social science & medicine.