Sample sizes for the SF-6D preference based measure of health from the SF-36: a practical guide

Background Health Related Quality of Life (HRQoL) measures are becoming more frequently used in clinical trials and health services research, both as primary and secondary endpoints. Investigators are now asking statisticians for advice on how to plan and analyse studies using HRQoL measures, which includes questions on sample size. Sample size requirements are critically dependent on the aims of the study, the outcome measure and its summary measure, the effect size and the method of calculating the test statistic. The SF-6D is a new single summary preference-based measure of health derived from the SF-36 suitable for use clinical trials and in the economic evaluation of health technologies. Objectives To describe and compare two methods of calculating sample sizes when using the SF-6D in comparative clinical trials and to give pragmatic guidance to researchers on what method to use. Methods We describe two main methods of sample size estimation. The parametric (t-test) method assumes the SF-6D data is continuous and normally distributed and that the effect size is the difference between two means. The non-parametric (Mann-Whitney MW) method assumes the data are continuous and not normally distributed and the effect size is defined in terms of the probability that an observation drawn at random from population Y would exceed an observation drawn at random from population X. We used bootstrap computer simulation to compare the power of the two methods for detecting a shift in location. Results This paper describes the SF-6D and retrospectively calculated parametric and nonparametric effect sizes for the SF-6D from a variety of studies that had previously used the SF-36. Computer simulation suggested that if the distribution of the SF-6D is reasonably symmetric then the t-test appears to be more powerful than the MW test at detecting differences in means. Therefore if the distribution of the SF-6D is symmetric or expected to be reasonably symmetric then parametric methods should be used for sample size calculations and analysis. If the distribution of the SF-6D is skewed then the MW test appears to be more powerful at detecting a location shift (difference in means) than the t-test. However, the differences in power (between the t and MW tests) are small and decrease as the sample size increases. Conclusions We have provided a clear description of the distribution of the SF-6D and believe that the mean is an appropriate summary measure for the SF-6D when it is to be used in clinical trials and the economic evaluation of new health technologies. Therefore pragmatically we would recommend that parametric methods be used for sample size calculation and analysis when using the SF-6D.sample size; health-related quality of life; SF-36; preference-based measures of health; bootstrap simulation

Using health state utility values from the general population to approximate baselines in decision analytic models when condition specific data are not available

Decision analytic models in healthcare require baseline health related quality of life (HRQoL) data to accurately assess the benefits of interventions. The use of inappropriate baselines such as assuming the value of perfect health (EQ-5D = 1) for not having a condition may overestimate the benefits of some treatment and thus distort policy decisions informed by cost per QALY thresholds. The primary objective was to determine if data from the general population are appropriate for baseline health state utility values (HSUVs) when condition-specific data are not available. Methods: Data from four consecutive Health Surveys for England were pooled. Self-reported health status and EQ-5D data were extracted and used to generate mean HSUVs for cohorts with or without prevalent health conditions. These were compared with mean HSUVs from all respondents irrespective of health status. Results: Over 45% of respondents (n=41,174) reported at least one health condition and almost 20% reported at least two. Our results suggest that data from the general population could be used to approximate baseline HSUVs in some analyses but not all. In particular, HSUVs from the general population would not be an appropriate baseline for cohorts who have just one health condition. In these instances, if condition-specific data are not available, data from respondents who report they do not have a prevalent health condition may be more appropriate. Exploratory analyses suggest the decrement on HRQoL may not be constant across ages for all conditions and these relationships may be condition-specific. Additional research is required to validate our findings.health state utility values; baseline; quality of life; EQ-5D; age-adjusted

A review of studies mapping (or cross walking) from non-preference based measures of health to generic preference-based measures

This paper presents a systematic review of current practice in mapping between nonpreference based measures and generic preference-based measures. It reviews the studies identified by a systematic search of the published literature and the grey literature. This review seeks to address the feasibility and overall validity of this approach, the circumstances when it should be considered and to bring together any lessons for future mapping studies.mapping; cross walking; preference-based measures; QALYs

A comparison of United States and United Kingdom EQ-5D health states valuations using a nonparametric Bayesian method

Few studies have compared preference values of health states obtained in different countries. This paper applies a nonparametric model to estimate and compare EQ-5D health state valuation data obtained from two countries using Bayesian methods. The data set is the US and UK EQ-5D valuation studies where a sample of 42 states defined by the EQ-5D was valued by representative samples of the general population from each country using the time trade-off technique. We estimate a function applicable across both countries which explicitly accounts for the differences between them, and is estimated using the data from both countries. The paper discusses the implications of these results for future applications of the EQ-5D and further work in this field.preference-based health measure; nonparametric methods; time trade-off; EQ-5D

Health state values for the HUI 2 descriptive system: results from a UK survey

This paper reports the results of a study to estimate a statistical health state valuation model for a revised version of the Health Utilities Index Mark 2, using Standard Gamble health state preference data. A sample of 51 health states were valued by a sample of the 198 members of the UK general population. Models are estimated for predicting health state valuations for all 8,000 states defined by the revised HUI2. The recommended model produces logical and significant coefficients for all levels of all dimensions in the HUI2. These coefficients appear to be robust across model specifications. This model performs well in predicting the observed health state values within the valuation sample and for a separate validation sample of health states. However, there are concerns over large prediction errors for two health states in the valuation sample. These problems must be balanced against concerns over the validity of using the VAS based health state valuation data of the original HUI2 valuation model.HUI2

Estimating a preference-based index from the Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM): valuation of CORE-6D

Background: The Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM) is used to evaluate the effectiveness of psychological therapies in people with common mental disorders. The objective of this study was to estimate a preference-based index for this population using CORE-6D, a health state classification system derived from CORE-OM consisting of a 5-item emotional component and a physical item, and to demonstrate a novel method for generating states that are not orthogonal. Methods: Rasch analysis was used to identify 11 plausible ‘emotional’ health states from CORE-6D (rather than conventional statistical design that would generate implausible states). By combining these with the 3 response levels of the physical item of CORE-6D, 33 plausible health states can be described, of which 18 were selected for valuation. An interview valuation survey of 220 members of public in South Yorkshire, UK, was undertaken using the time-trade-off method to value the 18 health states; regression analysis was subsequently used to predict values for all possible states described by CORE-6D. Results: A number of multivariate regression models were built to predict values for the 33 plausible health states of CORE-6D, using the Rasch logit value of the emotional health state and the response level of the physical item as independent variables. A cubic model with high predictive value (adjusted R squared 0.990) was finally selected, which can be used to predict utility values for all 927 states described by CORE-6D. Conclusion: The CORE-6D preference-based index will enable the assessment of cost-effectiveness of interventions for people with common mental disorders using existing and prospective CORE-OM datasets. The new method for generating states may be useful for other instruments with highly correlated dimensions

Mapping Functions in Health-Related Quality of Life: Mapping From Two Cancer-Specific Health-Related Quality-of-Life Instruments to EQ-5D-3L.

BACKGROUND: Clinical trials in cancer frequently include cancer-specific measures of health but not preference-based measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting EQ-5D values. This study aims to investigate a range of potential models to develop mapping functions from 2 widely used cancer-specific measures (FACT-G and EORTC-QLQ-C30) and to identify the best model. METHODS: Mapping models are fitted to predict EQ-5D-3L values using ordinary least squares (OLS), tobit, 2-part models, splining, and to EQ-5D item-level responses using response mapping from the FACT-G and QLQ-C30. A variety of model specifications are estimated. Model performance and predictive ability are compared. Analysis is based on 530 patients with various cancers for the FACT-G and 771 patients with multiple myeloma, breast cancer, and lung cancer for the QLQ-C30. RESULTS: For FACT-G, OLS models most accurately predict mean EQ-5D values with the best predicting model using FACT-G items with similar results using tobit. Response mapping has low predictive ability. In contrast, for the QLQ-C30, response mapping has the most accurate predictions using QLQ-C30 dimensions. The QLQ-C30 has better predicted EQ-5D values across the range of possible values; however, few respondents in the FACT-G data set have low EQ-5D values, which reduces the accuracy at the severe end. CONCLUSIONS: OLS and tobit mapping functions perform well for both instruments. Response mapping gives the best model predictions for QLQ-C30. The generalizability of the FACT-G mapping function is limited to populations in moderate to good health

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

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