Common scale valuations across different preference-based measures: estimation using rank data

Background: Different preference-based measures (PBMs) used to estimate Quality Adjusted Life Years (QALYs) provide di¤erent utility values for the same patient. Differences are expected since values have been obtained using different samples, valuation techniques and descriptive systems. Previous studies have estimated the relationship between pairs of PBMs using patient self-reported data. However, there is a need for an approach capable of generating values directly on a common scale for a range of PBMs using the same sample of general population respondents and valuation technique but keeping the advantages of the different descriptive systems. Methods: General public survey data (n=501) where respondents ranked health states described using subsets of six PBMs were analysed. We develop a new model based on the mixed logit to overcome two key limitations of the standard rank ordered logit model, namely, the unrealistic choice pattern (Independence of Irrelevant Alternatives) and the independence of repeated observations. Results: There are substantial differences in the estimated parameters between the two models (mean di¤erence 0.07) leading to di¤erent orderings across the measures. Estimated values for the best states described by di¤erent PBMs are substantially and significantly di¤erent using the standard model, unlike our approach which yields more consistent results. Limitations: Data come from a exploratory study that is relatively small both in sample size and coverage of health states. Conclusions: This study develops a new, �exible econometric model specifically designed to reflect appropriately the features of rank data. Results support the view that the standard model is not appropriate in this setting and will yield very different and apparently inconsistent results. PBMs can be compared using a common scale by implementation of this new approach

Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems

Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states. Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map. Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index. Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of Quality Adjusted Life Years in people with common mental health problems

Estimation of a preference based single index from the sexual quality of life questionnaire (SQOL) using ordinal data

There is increasing interest in using ordinal methods to estimate cardinal values for health states to calculate quality adjusted life years. This paper reports the estimation of models of rank data and discrete choice experiment (DCE) data to derive a preference-based index from a condition specific measure relating to sexual health and to compare the results to values generated from time trade-off valuation (TTO). The DCE data were analysed using a random effects probit model and the DCE predicted values were rescaled according to the highest and lowest predicted TTO values corresponding to the best and worst SQOL health states respectively. The rank data were analysed using a rank ordered logit model and re-scaled using two alternative methods. Firstly, re-scaling the rank predicted values using identical methods to those employed for DCE and secondly, re-scaling the rank model coefficients by dividing each level coefficient by the coefficient relating to death. The study raises some important issues about the use of ordinal data to produce cardinal health state valuations

Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems

Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states. Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map. Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index. Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of quality adjusted life years in people with common mental health problems.Rasch analysis; health-related quality of life; condition-specific measure; preference-based health; health states; CORE-6D; CORE-OM; mental health; quality-adjusted life years

The simultaneous valuation of states from multiple instruments using ranking and VAS data: methods and preliminary results

Background: Previous methods of empirical mapping involve using regressions on patient or general population self-report data from datasets involving 2 or more instruments. This approach relies on overlap in the descriptive systems of the measures, but key dimensions may not be present in both measures. Furthermore, this assumes it is appropriate to use different instruments on the same population, which may not be the case for all patient groups. The aim of the study described here is to develop a new method of mapping using general population preferences for hypothetical health states defined by the descriptive systems of different measures. This paper presents a description of the methods used in the study and reports on the results of the valuation study including details about the respondents, feasibility and quality (e.g. response rate, completion and consistency) and descriptive results on VAS and ranking data. The use of these results to estimate mapping functions between instruments will be presented in a companion paper. Methods: The study used interviewer administered versions of ranking and VAS techniques to value 13 health states defined by each of 6 instruments: EQ-5D (generic), SF-6D (generic), HUI2 (generic for children), AQL-5D (asthma specific), OPUS (social care specific), ICECAP (capabilities). Each interview involved 3 ranking and visual analogue scale (VAS) tasks with states from 3 different instruments where each task involves the simultaneous valuation of multiple instruments. The study includes 13 health and well-being states for each instrument (16 for EQ-5D) that reflect a range of health state values according to the published health state values for each instrument and each health state is valued approximately 75-100 times. Results: The sample consists of 499 members of the UK general population with a reasonable spread of background characteristics (response rate=55%). The study achieved a completion rate of 99% for all states included in the rank and rating tasks and 94.8% of respondents have complete VAS responses and 97.2% have complete rank responses. Interviewers reported that it is doubtful for 4.1% of respondents that they understood the tasks, and 29.3% of respondents stated that they found the tasks difficult. The results suggest important differences in the range of mean VAS and mean rank values per state across instruments; for example, mean VAS values for the worst state vary across instruments from 0.075 to 0.324. Respondents are able to change the ordering of states between the rank and VAS tasks and 12.0% of respondents have one or more differences in their rank and VAS orderings for every task. Conclusions: This study has demonstrated the feasibility of simultaneously valuing health states from different preference-based instruments. The preliminary analysis of the results presented here provides the basis for a new method of mapping between measures based on general population preferences

The simultaneous valuation of states from multiple instruments using ranking and VAS data: methods and preliminary results

Background: Previous methods of empirical mapping involve using regressions on patient or general population self-report data from datasets involving two or more instruments. This approach relies on overlap in the descriptive systems of the measures, but key dimensions may not be present in both measures. Furthermore this assumes it is appropriate to use different instruments on the same population, which may not be the case for all patient groups. The aim of the study described here is to develop a new method of mapping using general population preferences for hypothetical health states defined by the descriptive systems of different measures. This paper presents a description of the methods used in the study and reports on the results of the valuation study including details about the respondents, feasibility and quality (e.g. response rate, completion and consistency) and descriptive results on VAS and ranking data. The use of these results to estimate mapping functions between instruments will be presented in a companion paper. Methods: The study used interviewer administered versions of ranking and VAS techniques to value 13 health states defined by each of 6 instruments: EQ-5D (generic), SF-6D (generic), HUI2 (generic for children), AQL-5D (asthma specific), OPUS (social care specific), ICECAP (capabilities). Each interview involved 3 ranking and visual analogue scale (VAS) tasks with states from 3 different instruments where each task involves the simultaneous valuation of multiple instruments. The study includes 13 health and well-being states for each instrument (16 for EQ-5D) that reflect a range of health state values according to the published health state values for each instrument and each health state is valued approximately 75-100 times. Results: The sample consists of 499 members of the UK general population with a reasonable spread of background characteristics (response rate=55%). The study achieved a completion rate of 99% for all states included in the rank and rating tasks and 94.8% of respondents have complete VAS responses and 97.2% have complete rank responses. Interviewers reported that it is doubtful for 4.1% of respondents that they understood the tasks, and 29.3% of respondents stated that they found the tasks difficult. The results suggest important differences in the range of mean VAS and mean rank values per state across instruments, for example mean VAS values for the worst state vary across instruments from 0.075 to 0.324. Respondents are able to change the ordering of states between the rank and VAS tasks and 12.0% of respondents have one or more differences in their rank and VAS orderings for every task. Conclusions: This study has demonstrated the feasibility of simultaneously valuing health states from different preference-based instruments. The preliminary analysis of the results presented here provides the basis for a new method of mapping between measures based on general population preferences.preference-based measures of health; quality of life; mapping; visual analogue scale; ranking

Estimation of a preference based single index from the sexual quality of life questionnaire (SQOL) using ordinal data

There is increasing interest in using ordinal methods to estimate cardinal values for health states to calculate quality adjusted life years. This paper reports the estimation of models of rank data and discrete choice experiment (DCE) data to derive a preference-based index from a condition specific measure relating to sexual health and to compare the results to values generated from time trade-off valuation (TTO). The DCE data were analysed using a random effects probit model and the DCE predicted values were rescaled according to the highest and lowest predicted TTO values corresponding to the best and worst SQOL health states respectively. The rank data were analysed using a rank ordered logit model and re-scaled using two alternative methods. Firstly, re-scaling the rank predicted values using identical methods to those employed for DCE and secondly, re-scaling the rank model coefficients by dividing each level coefficient by the coefficient relating to death. The study raises some important issues about the use of ordinal data to produce cardinal health state valuations.sexual health, quality of life, preference-based measures

Estimation of a preference based single index from the sexual quality of life questionnaire (SQOL) using ordinal data

There is increasing interest in using ordinal methods to estimate cardinal values for health states to calculate quality adjusted life years. This paper reports the estimation of models of rank data and discrete choice experiment (DCE) data to derive a preference-based index from a condition specific measure relating to sexual health and to compare the results to values generated from time trade-off valuation (TTO). The DCE data were analysed using a random effects probit model and the DCE predicted values were rescaled according to the highest and lowest predicted TTO values corresponding to the best and worst SQOL health states respectively. The rank data were analysed using a rank ordered logit model and re-scaled using two alternative methods. Firstly, re-scaling the rank predicted values using identical methods to those employed for DCE and secondly, re-scaling the rank model coefficients by dividing each level coefficient by the coefficient relating to death. The study raises some important issues about the use of ordinal data to produce cardinal health state valuations