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

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

Estimating a preference-based index for a menopause specific health quality of life questionnaire

BACKGROUND: The aim of the study was to develop a menopause-specific, preference-based healthrelated quality-of-life (HRQoL) index reflecting both menopausal symptoms and potential sideeffects of Hormone Replacement Therapy (HRT). METHODS: The study had three phases: the development of a health state classification, a prospective valuation survey and the estimation of a model to interpolate HRQoL indices for all remaining health states as defined by the classification. A menopausal health state classification was developed with seven dimensions: hot flushes, aching joints/muscles, anxious/frightened feelings, breast tenderness, bleeding, vaginal dryness and undesirable androgenic signs. Each dimension contains between three and five levels and defines a total of 6,075 health states. A sample of 96 health states was selected for the valuation survey. These states were valued by a sample of 229 women aged 45 to 60, randomly selected from 6 general practice lists in Sheffield, UK. Respondents were asked to complete a time trade-off (TTO) task for nine health states, resulting in an average of 16.5 values for each health state. RESULTS: Mean health states valued range from 0.48 to 0.98 (where 1.0 is full health and zero is for states regarded as equivalent to death). Symptoms, as described by the classification system, can be rank-ordered in terms of their impact (from high to low) on menopausal HRQoL as follows: aching joints and muscles, bleeding, breast tenderness, anxious or frightened feelings, vaginal dryness, androgenic signs. Hot flushes did not significantly contribute to model fit. The preferred model produced a mean absolute error of 0.053, but suffered from bias at both ends of the scale. CONCLUSION: This article presents an attempt to directly value a condition specific health state classification. The overall fit was disappointing, but the results demonstrate that menopausal symptoms are perceived by patients to have a significant impact on utility. The overall effect is modest compared to the more generic health state descriptions such as the EQ-5D. The resultant algorithm generates a preference-based index that can be used economic evaluation and that reflects the impact of this condition

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

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

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

The validity and reliability of the my jump 2 app for measuring the reactive strength index and drop jump performance

BACKGROUNDː This is the first study to independently assess the concurrent validity and reliability of the My Jump 2 app for measuring drop jump performance. It is also the first to evaluate the app’s ability to measure the reactive strength index (RSI). METHODSː Fourteen male sport science students (age: 29.5 ± 9.9 years) performed three drop jumps from 20 cm and 40 cm (totalling 84 jumps), assessed via a force platform and the My Jump 2 app. Reported metrics included reactive strength index, jump height, ground contact time, and mean power. Measurements from both devices were compared using the intraclass correlation coefficient (ICC), Pearson product moment correlation coefficient (r), Cronbach’s alpha (α), coefficient of variation (CV) and Bland-Altman plots. RESULTSː Near perfect agreement was seen between devices at 20 cm for RSI (ICC = 0.95) and contact time (ICC = 0.99) and at 40 cm for RSI (ICC = 0.98), jump height (ICC = 0.96) and contact time (ICC = 0.92); with very strong agreement seen at 20 cm for jump height (ICC = 0.80). In comparison with the force plate the app showed good validity for RSI (20 cm: r = 0.94; 40 cm; r = 0.97), jump height (20 cm: r = 0.80; 40 cm; r = 0.96) and contact time (20 cm = 0.96; 40 cm; r = 0.98). CONCLUSIONSː The results of the present study show that the My Jump 2 app is a valid and reliable tool for assessing drop jump performance

The Capability Approach: A critical review of its application in health economics

The capability approach is an approach to assessing well-being developed by Amartya Sen. Interest in this approach has resulted in several attempts to develop questionnaires to measure and value capability at an individual level in health economics. The methods of measuring and valuing capability used in the questionnaires are critically reviewed in this paper. It is argued that the methods used to measure capability result in a capability profile that is often an inaccurate description of the individual’s true capability set. In addition, existing methods of valuing capability do not consider that capability is a set, consisting of multiple combinations of functionings rather than a single combination, which means that existing methods of valuing capability may be inadequate. The difficulties in measuring and valuing capability faced by existing questionnaires means that using the capability approach in economic evaluations will require a significant amount of further research