Scoring the ICECAP-A capability instrument. Estimation of a UK general population tariff

This paper reports the results of a best–worst scaling (BWS) study to value the Investigating Choice Experiments Capability Measure for Adults (ICECAP-A), a new capability measure among adults, in a UK setting. A main effects plan plus its foldover was used to estimate weights for each of the four levels of all five attributes. The BWS study was administered to 413 randomly sampled individuals, together with sociodemographic and other questions. Scale-adjusted latent class analyses identified two preference and two (variance) scale classes. Ability to characterize preference and scale heterogene-ity was limited, but data quality was good, and the final model exhibited a high pseudo-r-squared. After adjusting for heterogeneity, a population tariff was estimated. This showed that ‘attachment ’ and ‘stability ’ each account for around 22 % of the space, and ‘autonomy’, ‘achievement ’ and ‘enjoyment ’ account for around 18 % each. Across all attributes, greater value was placed on the difference between the lowest levels of capability than between the highest. This tariff will enable ICECAP-A to be used in economic evaluation both within the field of health and across public policy generally. © 2013 The Authors. Health Economics published by John Wiley & Sons Ltd

Development of a self-report measure of capability wellbeing for adults: the ICECAP-A

Purpose The benefits of health and social care are not confined to patient health alone and therefore broader measures of wellbeing may be useful for economic evaluation.\ud This paper reports the development of a simple measure of capability wellbeing for adults (ICECAP-A).\ud Methods In-depth, informant-led, interviews to identify the attributes of capability wellbeing were conducted with 36 adults in the UK. Eighteen semi-structured, repeat interviews were carried out to develop a capability-based descriptive system for the measure. Informants were purposively selected to ensure variation in socio-economic status, age, sex, ethnicity and health. Data analysis was carried out inductively and iteratively alongside interviews, and findings were used to shape the questions in later interviews.\ud Results Five over-arching attributes of capability wellbeing were identified for the measure: ‘‘stability’’,‘‘attachment’’, ‘‘achievement’’, ‘‘autonomy’’ and ‘‘enjoyment’’. One item, with four response categories, was developed for each attribute for the ICECAP-A descriptive system.\ud Conclusions The ICECAP-A capability measure represents a departure from traditional health economics outcome measures, by treating health status as an influence over broader attributes of capability wellbeing. Further work is required to value and validate the attributes and test the sensitivity of the ICECAP-A to healthcare interventions

Scoring the ICECAP - a capability instrument : estimation of a UK general population tariff

This paper reports the results of a best–worst scaling (BWS) study to value the Investigating Choice Experiments Capability Measure for Adults (ICECAP-A), a new capability measure among adults, in a UK setting. A main effects plan plus its foldover was used to estimate weights for each of the four levels of all five attributes. The BWS study was administered to 413 randomly sampled individuals, together with sociodemographic and other questions. Scale-adjusted latent class analyses identified two preference and two (variance) scale classes. Ability to characterize preference and scale heterogeneity was limited, but data quality was good, and the final model exhibited a high pseudo-r-squared. After adjusting for heterogeneity, a population tariff was estimated. This showed that ‘attachment’ and ‘stability’ each account for around 22% of the space, and ‘autonomy’, ‘achievement’ and ‘enjoyment’ account for around 18% each. Across all attributes, greater value was placed on the difference between the lowest levels of capability than between the highest. This tariff will enable ICECAP-A to be used in economic evaluation both within the field of health and across public policy generally. © 2013 The Authors. Health Economics published by John Wiley & Sons Ltd

Use of the bootstrap in analysing cost data from cluster randomised trials: some simulation results

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

Estimating preferences for a dermatology consultation using Best-Worst Scaling: Comparison of various methods of analysis

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 ovariateadjusted 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 use 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

Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale

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

An investigation of the construct validity of the ICECAP-A capability measure

Abstract Purpose To investigate the construct validity of the ICECAP-A capability wellbeing measure. Methods A face-to-face interview-administered survey was conducted with 418 members of the UK general population, randomly sampled from the Postcode Address File. Pre-specified hypotheses were developed about the expected associations between individuals’ ICECAP-A responses and their socio-economic circumstances, health and freedom. The hypotheses were investigated using statistical tests of association. Results The ICECAP-A responses and scores reflected differences across different health and socioeconomic groups as anticipated, but did not distinguish individuals by the level of local deprivation. Mean ICECAP-A scores reflected individuals’ perceived freedom slightly more closely than did measures of health and happiness. Conclusion This study suggests that the ICECAP-A measure can identify expected differences in capability wellbeing in a general population sample. Further work could establish whether self-reported capabilities exhibit desirable validity and acceptability in sub-groups of the population such as patients, social care recipients and informal carers