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Government policies to shift care into the community and demographic changes mean that unpaid (informal) caters will increasingly be relied on to deliver care, particularly to older people. As a result, careful consideration needs to be given to informal care in economic evaluations. Current methods for economic evaluations may neglect important aspects of informal care, This paper reports the development of a simple measure of the caring experience for use in economic evaluations, A meta-ethnography was used to reduce qualitative research to six conceptual attributes of caring, Sixteen semi-structured interviews were then conducted with carets of older people, to check the attributes and develop them into the measure. Six attributes of the caring experience comprise the final measure: getting on, organisational assistance, social support, activities, control, and fulfilment, The final measure (the Carer Experience Scale) focuses on the process of providing care, rather than health outcomes from caring, Arguably this provides a more direct assessment of carers' welfare, Following work to test and scale the measure, it may offer a promising way of incorporating the impact on carers in economic evaluations. Keywords: UK Informal care Quality of life measurement Evidence synthesis Interviews Economic evaluation Older people

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jhealeco.2006.04.002 Byline: Terry N. Flynn (a), Jordan J. Louviere (b), Tim J. Peters (c), Joanna Coast (d) Keywords: Discrete choice experiments; Utility; Attribute importance 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. Author Affiliation: (a) MRC Health Services Research Collaboration, Department of Social Medicine, University of Bristol, UK (b) Centre for the Study of Choice, University of Technology, Sydney, Australia (c) Department of Community Based Medicine, University of Bristol, UK (d) Health Economics Facility, University of Birmingham, UK Article History: Received 9 February 2005; Revised 27 March 2006; Accepted 19 April 2006

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.

Abstract Background Researchers are increasingly investigating the potential for ordinal tasks such as ranking and discrete choice experiments to estimate QALY health state values. However, the assumptions of random utility theory, which underpin the statistical models used to provide these estimates, have received insufficient attention. In particular, the assumptions made about the decisions between living states and the death state are not satisfied, at least for some people. Estimated values are likely to be incorrectly anchored with respect to death (zero) in such circumstances. Methods Data from the Investigating Choice Experiments for the preferences of older people CAPability instrument (ICECAP) valuation exercise were analysed. The values (previously anchored to the worst possible state) were rescaled using an ordinal model proposed previously to estimate QALY-like values. Bootstrapping was conducted to vary artificially the proportion of people who conformed to the conventional random utility model underpinning the analyses. Results Only 26% of respondents conformed unequivocally to the assumptions of conventional random utility theory. At least 14% of respondents unequivocally violated the assumptions. Varying the relative proportions of conforming respondents in sensitivity analyses led to large changes in the estimated QALY values, particularly for lower-valued states. As a result these values could be either positive (considered to be better than death) or negative (considered to be worse than death). Conclusion Use of a statistical model such as conditional (multinomial) regression to anchor quality of life values from ordinal data to death is inappropriate in the presence of respondents who do not conform to the assumptions of conventional random utility theory. This is clearest when estimating values for that group of respondents observed in valuation samples who refuse to consider any living state to be worse than death: in such circumstances the model cannot be estimated. Only a valuation task requiring respondents to make choices in which both length and quality of life vary can produce estimates that properly reflect the preferences of all respondents.

Abstract Background This work has investigated under what conditions confidence intervals around the differences in mean costs from a cluster RCT are suitable for estimation using a commonly used cluster-adjusted bootstrap in preference to methods that utilise the Huber-White robust estimator of variance. The bootstrap's main advantage is in dealing with skewed data, which often characterise patient costs. However, it is insufficiently well recognised that one method of adjusting the bootstrap to deal with clustered data is only valid in large samples. In particular, the requirement that the number of clusters randomised should be large would not be satisfied in many cluster RCTs performed to date. Methods The performances of confidence intervals for simple differences in mean costs utilising a robust (cluster-adjusted) standard error and from two cluster-adjusted bootstrap procedures were compared in terms of confidence interval coverage in a large number of simulations. Parameters varied included the intracluster correlation coefficient, the sample size and the distributions used to generate the data. Results The bootstrap's advantage in dealing with skewed data was found to be outweighed by its poor confidence interval coverage when the number of clusters was at the level frequently found in cluster RCTs in practice. Simulations showed that confidence intervals based on robust methods of standard error estimation achieved coverage rates between 93.5% and 94.8% for a 95% nominal level whereas those for the bootstrap ranged between 86.4% and 93.8%. Conclusion In general, 24 clusters per treatment arm is probably the minimum number for which one would even begin to consider the bootstrap in preference to traditional robust methods, for the parameter combinations investigated here. At least this number of clusters and extremely skewed data would be necessary for the bootstrap to be considered in favour of the robust method. There is a need for further investigation of more complex bootstrap procedures if economic data from cluster RCTs are to be analysed appropriately.