Are people ethical? An experimental approach

Individual decision making, ethics, experimental economics

A comparison of methods for converting DCE values onto the full health-dead QALY scale

Cardinal preference elicitation techniques such as time trade-off (TTO) and Standard Gamble (SG) receive criticism for their complexity and difficulties in using them in more vulnerable populations. Ordinal techniques such as discrete choice experiment (DCE) and Best Worst Scaling (BWS) are easier, but values generated by them are not anchored onto the full health-dead 1-0 QALY scale required for use in economic evaluation. This paper explores new methods for converting modelled DCE latent values onto the full health-dead QALY scale: (1) anchoring assuming worst state is equal to being dead; (2) anchoring DCE values using dead as valued in the DCE; (3) anchoring DCE values using TTO value for worst state; (4) mapping DCE values onto TTO; (5) combining DCE and TTO data in a hybrid model. We use postal DCE data (n=263) and TTO data (n=307) collected by interview in a general population valuation study of an asthma condition-specific measure (AQL-5D). Methods (4) and (5) using mapping and hybrid models perform best; the anchor-based methods perform relatively poorly. These new methods have a useful role for producing values on the QALY scale from ordinal techniques such as DCE and BWS for use in cost utility analyses

Exploring the consistency of the SF-6D

Objective: The six dimensional health state short form (SF-6D) was designed to be derived from the short-form 36 health survey (SF-36). The purpose of this research was to compare the SF-6D index values generated from the SF 36 (SF-6D(SF-36)) with those obtained from the SF-6D administered as an independent instrument (SF-6D(Ind)). The goal was to assess the consistency of respondents answers to these two methods of deriving the SF-6D. Methods: Data were obtained from a sample of the Portuguese population (n = 414). Agreement between the instruments was assessed on the basis of a descriptive system and their indexes. The analysis of the descriptive system was performed by using a global consistency index and an identically classified index. Agreement was also explored by using correlation coefficients. Parametric tests were used to identify differences between the indexes. Regression models were estimated to understand the relationship between them. Results: The SF-6D(Ind) generates higher values than does the SF-6D(SF-36), There were significant differences between the indexes across sociodemographic groups. There was a significant ceiling effect in the SF-6D(Ind) a but not in the SF-6D(SF-36). The correlation between the indexes was high but less than what was anticipated. The global consistency index identified the dimensions with larger differences. Considerable differences were found in two dimensions, possibly as a result of different item contexts. Further research is needed to fully understand the role of the different layouts and the length of the questionnaires in the respondents' answers. Conclusions: The results show that as the SF-6D was designed to derive utilities from the SF-36 it should be used in this way and not as an independent instrument.Fundacao para a Ciencia e a Tecnologia (FCT

Mapping onto Eq-5 D for patients in poor health

<p>Abstract</p> <p>Background</p> <p>An increasing amount of studies report mapping algorithms which predict EQ-5 D utility values using disease specific non-preference-based measures. Yet many mapping algorithms have been found to systematically overpredict EQ-5 D utility values for patients in poor health. Currently there are no guidelines on how to deal with this problem. This paper is concerned with the question of why overestimation of EQ-5 D utility values occurs for patients in poor health, and explores possible solutions.</p> <p>Method</p> <p>Three existing datasets are used to estimate mapping algorithms and assess existing mapping algorithms from the literature mapping the cancer-specific EORTC-QLQ C-30 and the arthritis-specific Health Assessment Questionnaire (HAQ) onto the EQ-5 D. Separate mapping algorithms are estimated for poor health states. Poor health states are defined using a cut-off point for QLQ-C30 and HAQ, which is determined using association with EQ-5 D values.</p> <p>Results</p> <p>All mapping algorithms suffer from overprediction of utility values for patients in poor health. The large decrement of reporting 'extreme problems' in the EQ-5 D tariff, few observations with the most severe level in any EQ-5 D dimension and many observations at the least severe level in any EQ-5 D dimension led to a bimodal distribution of EQ-5 D index values, which is related to the overprediction of utility values for patients in poor health. Separate algorithms are here proposed to predict utility values for patients in poor health, where these are selected using cut-off points for HAQ-DI (> 2.0) and QLQ C-30 (< 45 average of QLQ C-30 functioning scales). The QLQ-C30 separate algorithm performed better than existing mapping algorithms for predicting utility values for patients in poor health, but still did not accurately predict mean utility values. A HAQ separate algorithm could not be estimated due to data restrictions.</p> <p>Conclusion</p> <p>Mapping algorithms overpredict utility values for patients in poor health but are used in cost-effectiveness analyses nonetheless. Guidelines can be developed on when the use of a mapping algorithms is inappropriate, for instance through the identification of cut-off points. Cut-off points on a disease specific questionnaire can be identified through association with the causes of overprediction. The cut-off points found in this study represent severely impaired health. Specifying a separate mapping algorithm to predict utility values for individuals in poor health greatly reduces overprediction, but does not fully solve the problem.</p

Deriving a Preference-Based Measure for Myelofibrosis from the EORTC QLQ-C30 and the MF-SAF

AbstractBackgroundUtility values are required for economic evaluation using cost-utility analyses. Often, generic measures such as the EuroQol five-dimensional questionnaire are used, but this may not appropriately reflect the health-related quality of life of patients with cancer including myelofibrosis.ObjectiveTo derive a condition-specific preference-based measure for myelofibrosis using appropriate existing measures, the Myelofibrosis-Symptom Assessment Form and the European Organisation for Research and Treatment of Cancer Quality of Life 30 Questionnaire.MethodsData from the Controlled Myelofibrosis Study with Oral JAK Inhibitor Treatment trial (n = 309) were used to derive the health state classification system. Psychometric and factor analyses were used to determine the dimensions of the classification system. Psychometric and Rasch analyses were then used to select an item to represent each dimension. Item selection was validated with experts. A selection of health states was valued by members of the general population using time trade-off. Finally, health state values were modeled using regression analysis to produce utility values for every state.ResultsThe Myelofibrosis 8 dimensions has eight dimensions: physical functioning, emotional functioning, fatigue, itchiness, pain under ribs on the left side, abdominal discomfort, bone or muscle pain, and night sweats. Regression models were estimated using time trade-off data from 246 members of the general population valuing a total of 33 states. The best performing model was a random effects maximum likelihood model producing utility values ranging from 0.089 to 1.ConclusionsThe Myelofibrosis 8 dimensions is a condition-specific preference-based measure for myelofibrosis. This measure can be used to generate utility values for myelofibrosis for any data set containing the Myelofibrosis-Symptom Assessment Form and the European Organisation for Research and Treatment of Cancer Quality of Life 30 Questionnaire data

Preparatory study for the revaluation of the EQ-5D tariff: methodology report.

BACKGROUND: EQ-5D is a widely used generic measure of health with a 'tariff', or preference weights, obtained from the general population, using time trade-off (TTO). PRET (Preparatory study for the Re-valuation of the EQ-5D Tariff project) contributes towards the methodology for its revaluation. METHODS: Stage 1 examined key assumptions typically involved in health-state valuations through a series of binary choice exercises, namely that health-state preferences are independent of (1) duration of the state; (2) whose health it is (i.e. perspective); (3) length of 'lead time' (a mechanism to value all states on the same scale, including those who are worse than being dead); (4) when health events take place (time preference); and (5) satisfaction associated with the state. Further topics addressed were (6) exhaustion of lead time in the worst state; (7) health-state valuation using discrete choice experiments (DCEs) with a duration attribute; and (8) binary choice administration of lead time - time trade-off (LT-TTO). Stage 1 consisted of an online survey with 6000 respondents. Stage 2 compared the results above to those of an identical survey conducted in 200 face-to-face computer-assisted personal interviews (CAPIs), covering topics (1) to (7). Stages 3 and 4 examined - in more detail and depth - issues taken from stage 1. Stage 3 consisted of CAPI surveys of a representative UK sample of 300, using examples of TTO, LT-TTO, and DCE with duration, each followed by extensive feedback questions. Stage 4 was a more intensive exercise involving a qualitative analysis of people's thought processes during both binary choice and iterative health-state valuation exercises. Data were collected through 'think-aloud' methods in 30 interviews of a convenience sample. RESULTS: Stage 1 found that health-state values are not independent of (1) duration of the state but there is no clear pattern; (2) whose health it is; (3) the duration of 'lead time' but there was no clear pattern; (4) when health events take place; or (5) satisfaction associated with the state. Furthermore, (6) exhaustion of lead time in the worst state was subject to substantial framing effects; (7) the five-level version of the EQ-5D (EQ-5D-5L) can be valued using DCE with duration as an attribute; and (8) binary choice LT-TTO can be administered in an online environment. Stage 2 found that although online surveys and CAPI surveys resulted in different compositions of respondents, at the aggregate, their responses to the experimental questions covering (1) to (7) above were not statistically significantly different from each other. Stages 3 and 4 found that TTO and LT-TTO were easier than DCE with duration; respondents did not necessarily trade across all attributes of EQ-5D; some respondents found it difficult to distinguish between the two worst levels of EQ-5D-5L, and some respondents may be thinking about the impact of their ill health on their family. CONCLUSIONS: In order for the National Institute for Health and Care Excellence to make the most appropriate decisions, the EQ-5D tariff needs to incorporate the latest understanding of health-state preferences. PRET contributed to the knowledge base on the conduct of health-state valuation studies. FUNDING: The Medical Research Council (MRC)-National Institute for Health Research (NIHR) Methodology Research Programme funded the PRET project (MRC ref. G0901500), and the EuroQol Group funded the PRET-AS project (Preparatory study for the Re-valuation of the EQ-5D Tariff project - Additional Sample) as an extension to the PRET project with formal agreement from the MRC

Deriving a preference-based measure for cancer using the EORTC QLQ-C30 : a confirmatory versus exploratory approach

Background: To derive preference-based measures from various condition-specific descriptive health-related quality of life (HRQOL) measures. A general 2-stage method is evolved: 1) an item from each domain of the HRQOL measure is selected to form a health state classification system (HSCS); 2) a sample of health states is valued and an algorithm derived for estimating the utility of all possible health states. The aim of this analysis was to determine whether confirmatory or exploratory factor analysis (CFA, EFA) should be used to derive a cancer-specific utility measure from the EORTC QLQ-C30. Methods: Data were collected with the QLQ-C30v3 from 356 patients receiving palliative radiotherapy for recurrent or metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter based on a conceptual model (the established domain structure of the QLQ-C30: physical, role, emotional, social and cognitive functioning, plus several symptoms) and clinical considerations (views of both patients and clinicians about issues relevant to HRQOL in cancer). The dimensions determined by each method were then subjected to item response theory, including Rasch analysis. Results: CFA results generally supported the proposed conceptual model, with residual correlations requiring only minor adjustments (namely, introduction of two cross-loadings) to improve model fit (increment χ2(2) = 77.78, p 75% observation at lowest score), 6 exhibited misfit to the Rasch model (fit residual > 2.5), none exhibited disordered item response thresholds, 4 exhibited DIF by gender or cancer site. Upon inspection of the remaining items, three were considered relatively less clinically important than the remaining nine. Conclusions: CFA appears more appropriate than EFA, given the well-established structure of the QLQ-C30 and its clinical relevance. Further, the confirmatory approach produced more interpretable results than the exploratory approach. Other aspects of the general method remain largely the same. The revised method will be applied to a large number of data sets as part of the international and interdisciplinary project to develop a multi-attribute utility instrument for cancer (MAUCa)

Comparison of general population, patient, and carer utility values for dementia health states.

Utility values to estimate quality-adjusted life years (QALYs) for use in cost-utility analyses are usually elicited from members of the general population. Public attitudes and understanding of dementia in particular may mean that values elicited from the general population may differ from patients and carers for dementia health states. This study examines how the population impacts utility values elicited for dementia health states using interviewer-administered time tradeoff valuation of health states defined by the dementia-specific preference-based measures DEMQOL-U (patient-report) and DEMQOL-Proxy-U (carer-report). Eight DEMQOL-U states were valued by 78 members of the UK general population and 71 patients with dementia of mild severity. Eight DEMQOL-Proxy-U states were valued by 77 members of the UK general population and 71 carers of patients with dementia of mild severity. Random-effects generalized least squares regression estimated the impact of population, dementia health state, and respondent sociodemographic characteristics on elicited values, finding that values for dementia health states differed by population and that the difference varied across dementia health states. Patients with dementia and carers of patients with dementia gave systematically lower values than members of the general population that were not due to differences in the sociodemographic characteristics of the populations. Our results suggest that the population used to produce dementia health state values could impact the results of cost-utility analyses and potentially affect resource allocation decisions; yet, currently, only general population values are available for usage

Comparing Generic and Condition-Specific Preference-Based Measures in Epilepsy: EQ-5D-3L and NEWQOL-6D

Background: There is debate about the psychometric characteristics of the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L) for use in epilepsy. In response to the concerns, an epilepsy-specific preference-based measure (NEWQOL-6D) was developed. The psychometric characteristics of the NEWQOL-6D, however, have not been assessed. Objectives: To investigate the validity and responsiveness of the EQ-5D-3L and the Quality of Life in Newly Diagnosed Epilepsy Instrument-six dimensions (NEWQOL-6D) for use in the assessment of treatments for newly diagnosed focal epilepsy. Methods: The analysis used data from the Standard And New Antiepileptic Drugs trial including patients with focal epilepsy. We assessed convergent validity using correlations, and known-group validity across different epilepsy and general health severity indicators using analysis of variance and effect sizes. The responsiveness of the measures to change over time was assessed using standardized response means. We also assessed agreement between the measures. Results: There was some level of convergence and agreement between the measures in terms of utility score but divergence in the concepts measured by the descriptive systems. Both instruments displayed known-group validity, with significant differences between severity groups, and generally slightly larger effect sizes for the NEWQOL-6D across the epilepsy-specific indicators. Evidence for responsiveness was less clear, with small to moderate standardized response means demonstrating different levels of change across different indicators. Conclusions: There was an overall tendency for the NEWQOL-6D to better reflect differences across groups, but this does not translate into large absolute utility differences. Both the EQ-5D-3L and the NEWQOL-6D show some evidence of validity for providing utility values for economic evaluations in newly diagnosed focal epilepsy

Patient-reported utilities in advanced or metastatic melanoma, including analysis of utilities by time to death

Background: Health-related quality of life is often collected in clinical studies, and forms a cornerstone of economic evaluation. This study had two objectives, firstly to report and compare pre- and post-progression health state utilities in advanced melanoma when valued by different methods and secondly to explore the validity of progression-based health state utility modelling compared to modelling based upon time to death. Methods: Utilities were generated from the ipilimumab MDX010-20 trial (Clinicaltrials.gov Identifier: NCT00094653) using the condition-specific EORTC QLQ-C30 (via the EORTC-8D) and generic SF-36v2 (via the SF-6D) preference-based measures. Analyses by progression status and time to death were conducted on the patient-level data from the MDX010-20 trial using generalised estimating equations fitted in Stata®, and the predictive abilities of the two approaches compared. Results: Mean utility showed a decrease on disease progression in both the EORTC-8D (0.813 to 0.776) and the SF-6D (0.648 to 0.626). Whilst higher utilities were obtained using the EORTC-8D, the relative decrease in utility on progression was similar between measures. When analysed by time to death, both EORTC-8D and SF-6D showed a large decrease in utility in the 180 days prior to death (from 0.831 to 0.653 and from 0.667 to 0.544, respectively). Compared to progression status alone, the use of time to death gave similar or better estimates of the original data when used to predict patient utility in the MDX010-20 study. Including both progression status and time to death further improved model fit. Utilities seen in MDX010-20 were also broadly comparable with those seen in the literature. Conclusions: Patient-level utility data should be analysed prior to constructing economic models, as analysis solely by progression status may not capture all predictive factors of patient utility and time to death may, as death approaches, be as or more important. Additionally this study adds to the body of evidence showing that different scales lead to different health state values. Further research is needed on how different utility instruments (the SF-6D, EORTC-8D and EQ-5D) relate to each other in different disease areas