scholarly journals Setting Dead at Zero: Applying Scale Properties to the QALY Model

2018 ◽  
Vol 38 (6) ◽  
pp. 627-634 ◽  
Author(s):  
Bram Roudijk ◽  
A. Rogier T. Donders ◽  
Peep F.M. Stalmeier
Keyword(s):  
Decision Making ◽  
Research Question ◽  
Health Utility ◽  
Discrete Choice Experiments ◽  
Health State ◽  
Ratio Scale ◽  
Health States ◽  
A Value ◽  
Scale Properties ◽  
Utility Scale

Introduction. Scaling severe states can be a difficult task. First, the method of measurement affects whether a health state is considered better or worse than dead. Second, in discrete choice experiments, different models to anchor health states on 0 (dead) and 1 (perfect health) produce varying amounts of health states worse than dead. Research Question. Within the context of the quality-adjusted life year (QALY) model, this article provides insight into the value assigned to dead and its consequences for decision making. Our research questions are 1) what are the arguments set forth to assign dead the number 0 on the health–utility scale? And 2) what are the effects of the position of dead on the health–utility scale on decision making? Methods. A literature review was conducted to explore the arguments set forth to assign dead a value of 0 in the QALY model. In addition, scale properties and transformations were considered. Results. The review uncovered several practical and theoretical considerations for setting dead at 0. In the QALY model, indifference between 2 health episodes is not preserved under changes of the origin of the duration scale. Ratio scale properties are needed for the duration scale to preserve indifferences. In combination with preferences and zero conditions for duration and health, it follows that dead should have a value of 0. Conclusions. The health–utility and duration scales have ratio scale properties, and dead should be assigned the number 0. Furthermore, the position of dead should be carefully established, because it determines how life-saving and life-improving values are weighed in cost–utility analysis.

From Clinical Outcomes to Health Utilities: The Role of Mapping to Bridge the Evidence Gap

2019 ◽  
Author(s):  
Mónica Hernández Alava
Keyword(s):  
Quality Of Life ◽  
Decision Making ◽  
Economic Evaluation ◽  
Health Utility ◽  
Health State ◽  
Health States ◽  
Descriptive System ◽  
Healthcare Technologies ◽  
Health State Utility Values

The assessment of health-related quality of life is crucially important in the evaluation of healthcare technologies and services. In many countries, economic evaluation plays a prominent role in informing decision making often requiring preference-based measures (PBMs) to assess quality of life. These measures comprise two aspects: a descriptive system where patients can indicate the impact of ill health, and a value set based on the preferences of individuals for each of the health states that can be described. These values are required for the calculation of quality adjusted life years (QALYs), the measure for health benefit used in the vast majority of economic evaluations. The National Institute for Health and Care Excellence (NICE) has used cost per QALY as its preferred framework for economic evaluation of healthcare technologies since its inception in 1999. However, there is often an evidence gap between the clinical measures that are available from clinical studies on the effect of a specific health technology and the PBMs needed to construct QALY measures. Instruments such as the EQ-5D have preference-based scoring systems and are favored by organizations such as NICE but are frequently absent from clinical studies of treatment effect. Even where a PBM is included this may still be insufficient for the needs of the economic evaluation. Trials may have insufficient follow-up, be underpowered to detect relevant events, or include the wrong PBM for the decision- making body. Often this gap is bridged by “mapping”—estimating a relationship between observed clinical outcomes and PBMs, using data from a reference dataset containing both types of information. The estimated statistical model can then be used to predict what the PBM would have been in the clinical study given the available information. There are two approaches to mapping linked to the structure of a PBM. The indirect approach (or response mapping) models the responses to the descriptive system using discrete data models. The expected health utility is calculated as a subsequent step using the estimated probability distribution of health states. The second approach (the direct approach) models the health state utility values directly. Statistical models routinely used in the past for mapping are unable to consider the idiosyncrasies of health utility data. Often they do not work well in practice and can give seriously biased estimates of the value of treatments. Although the bias could, in principle, go in any direction, in practice it tends to result in underestimation of cost effectiveness and consequently distorted funding decisions. This has real effects on patients, clinicians, industry, and the general public. These problems have led some analysts to mistakenly conclude that mapping always induces biases and should be avoided. However, the development and use of more appropriate models has refuted this claim. The need to improve the quality of mapping studies led to the formation of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Mapping to Estimate Health State Utility values from Non-Preference-Based Outcome Measures Task Force to develop good practice guidance in mapping.

scholarly journals Valuation Survey of EQ-5D-Y Based on the International Common Protocol: Development of a Value Set in Japan

2021 ◽  
pp. 0272989X2110018
Author(s):  
Takeru Shiroiwa ◽  
Shunya Ikeda ◽  
Shinichi Noto ◽  
Takashi Fukuda ◽  
Elly Stolk
Keyword(s):  
Discrete Choice ◽  
Health Care Policy ◽  
Mixed Logit ◽  
Health State ◽  
Choice Data ◽  
Social Functions ◽  
Value Set ◽  
A Value ◽  
Value Sets ◽  
Utility Scale

Background EQ-5D-Y is a preference-based measure for children and adolescents (aged 8–15 y). This is the first study to develop an EQ-5D-Y value set for converting EQ-5D-Y responses to index values. Methods We recruited 1047 respondents (aged 20–79 y) from the general population, stratified by gender and age group, in 5 Japanese cities. All data were collected through face-to-face surveys. Respondents were asked to value EQ-5D-Y states for a hypothetical 10-y-old child from a proxy perspective using composite time tradeoff (cTTO) and a discrete choice experiment (DCE). The discrete choice data were analyzed using a mixed logit model. Latent DCE values were then converted to a 0 (death)/1 (full health) scale by mapping them to the cTTO values. Results The mean observed cTTO value of the worst health state [33333] was 0.20. Analysis of the DCE data showed that the coefficients of the domains related to mental functions (“Having pain or discomfort” and “Feeling worried, sad, or unhappy”) were larger than those for the domains related to physical and social functions. By converting latent DCE values to a utility scale, we constructed a value set for EQ-5D-Y. No inconsistencies were observed. The minimum predicted score was 0.288 [33333], and the second-best score was 0.957 [12111]. Conclusion A value set for EQ-5D-Y was successfully constructed. This is the first survey of an EQ-5D-Y value set. Interpreting the differences between EQ-5D-Y and EQ-5D-5L value sets is a future task with implications for health care policy.

Health Utility Measurement

2011 ◽  
pp. 787-813 ◽  
Author(s):  
Donna Rowen ◽  
John Brazier
Keyword(s):  
Economic Evaluation ◽  
Health Utility ◽  
Health State ◽  
Health States ◽  
Health State Description ◽  
Utility Measurement ◽  
Current State ◽  
Increasing Demand ◽  
Demand For Health

Measuring and valuing health is a major component of economic evaluation, meaning that health utility measurement has been growing in popularity in recent years due to the increasing demand for health state values in economic models and evaluations. The main issues in health utility measurement are how to describe health states, how to value the health state description and whose values should be used. This article briefly outlines these main issues and then focuses on recent methodological developments in health utility measurement. It assesses the current state of health utility measurement and discusses the question of assessment of a health state to be used in economic evaluation. The discussion whether experience utility should be used rather than conventional preference-based utility raises important issues about perspective and the role of various factors.

Incorporating Mortality in Health Utility Measures

2020 ◽  
Vol 40 (7) ◽  
pp. 862-872
Author(s):  
Barry Dewitt ◽  
George W. Torrance
Keyword(s):  
Health Utility Index ◽  
Health Utility ◽  
Quality Adjusted Life Year ◽  
The State ◽  
Multidimensional Space ◽  
Health States ◽  
Utility Systems ◽  
Design Choice ◽  
Scale Properties ◽  
Utility Measures

The creation of multiattribute health utility systems requires design choices that have profound effects on the utility model, many of which have been documented and studied in the literature. Here we describe one design choice that has, to the best of our knowledge, been unrecognized and therefore ignored. It can emerge in any multiattribute decision analysis in which one or more essential outcomes cannot be described in terms of the multiattribute space. In health applications, the state of being dead is such an outcome. When the remaining health is conceptualized as a multidimensional space, determining the utility of the state of being dead requires using the interval-scale properties of cardinal utility, combined with elicited utilities for the state of being dead and the all-worst state, to produce a utility function in which the state of being dead has a utility of 0 and full health has a utility of 1 (i.e., the quality-adjusted life-year scale). Although previously unrecognized, there are two approaches to accomplish that step, and they produce different results in almost all cases. As a corollary, the choice of approach determines the proportion of states rated as worse than dead by the system. For example, in the Health Utility Index 3 (HUI3), the method used classifies 78% of the 972,000 unique health states in the classification system as worse than dead, and that proportion increases to 85% when the HUI3 is recalculated using the alternative approach. Studies of populations with significant morbidity are the most likely to be sensitive to the design choice. Those who design utility measures should be aware that they are using a researcher degree of freedom when they decide how to scale the state of being dead.

scholarly journals Manipulating the 5 Dimensions of the EuroQol Instrument: The Effects on Self-Reporting Actual Health and Valuing Hypothetical Health States

2019 ◽  
Vol 39 (4) ◽  
pp. 380-392 ◽  
Author(s):  
Aki Tsuchiya ◽  
Nick Bansback ◽  
Arne Risa Hole ◽  
Brendan Mulhern
Keyword(s):  
Discrete Choice ◽  
Online Survey ◽  
Self Report ◽  
Discrete Choice Experiments ◽  
Health State ◽  
Health States ◽  
Different Types ◽  
Health State Valuations ◽  
The Uk ◽  
Actual Health

Background. The EQ-5D instrument has 5 dimensions. This article reports on the effects of manipulating a) the order in which the 5 dimensions are presented (appearing first v. last), b) splitting of the composite dimensions (“pain or discomfort” and “anxiety or depression”), and c) removing or “bolting off” 1 of the 5 EQ-5D dimensions at a time. The effects were examined in 2 contexts: 1) self-reporting health and 2) health state valuations. Methods. Three different types of discrete choice experiments (DCE) including a duration attribute were designed. An online survey with 12 subtypes, each with 10 DCE tasks, was designed and completed by 2494 members of the UK general public. Results. Of the 3 manipulations in the self-reporting context, only b) splitting anxiety or depression had a significant effect. In the health state valuation context, b) splitting level 5 pain or discomfort (relative to pain) and splitting level 5 anxiety or depression (relative to anxiety) had significant effects as did c) bolting off dimensions. Conclusions. We find that the values given to certain health dimensions are sensitive to the way in which it is described and the other health dimensions presented. Of particular interest is the effect of splitting composite dimensions: a given EQ-5D(-5L) profile may mean different things depending on whether the profile is used to self-report one’s health or to value hypothetical states, so that the health state values of EQ-5D(-5L) in population tariffs may not correspond to the states that patients self-report themselves in.

scholarly journals Crosswalk EQ-5D-5L Value Set for Slovenia

2020 ◽  
Vol 59 (3) ◽  
pp. 189-194 ◽  
Author(s):  
Valentina Prevolnik Rupel ◽  
Marko Ogorevc
Keyword(s):  
General Population ◽  
Health State ◽  
Health States ◽  
Value Set ◽  
Euroqol Group ◽  
A Value ◽  
Distribution Of Values ◽  
Severe Health State ◽  
The Mean ◽  
Informational Value

AbstractIntroductionDue to the availability of the EQ-5D-5L instrument official translation into Slovenian its use is widespread in Slovenia. However, the health profiles obtained in many studies cannot be ascribed their appropriate values as the EQ-5D-5L value set does not yet exist in Slovenia. Our aim was to estimate an interim EQ-5D-5L value set for Slovenia using the crosswalk methodology developed by the EuroQol Group on the basis of the EQ-5D-3L Slovenian TTO value set. Our secondary aim was to compare the interim values obtained with the EQ-5D-3L Slovenian values.MethodsTo obtain a Slovenian interim EQ-5D-5L value set, we applied the crosswalk methodology developed by the EuroQol Group to the Slovenian EQ-5D-3L TTO value set. We examined the differences between values by comparing the mean 3L and 5L value scores and the distribution of values across all respondents.ResultsBy definition, 3-level and 5-level versions have the same range (from 1 to −0.495) and a health state coded 22222 in the 3-level version corresponds to 33333 in the 5-level version. While the addition of a “slight” severity level (22222) in the 5-level version has a low informational value, the addition of a “severe” health state (44444) covers larger range of the scale. The 5-level version results in fewer health states being valued below 0 and above 0.8.ConclusionThe EQ-5D-5L value set, based on the crosswalk methodology, should be used until a value set for the EQ-5D-5L is derived from preferences elicited directly from a representative sample of the Slovenian general population.

Toward a Centralized, Systematic Approach to the Identification, Appraisal, and Use of Health State Utility Values for Reimbursement Decision Making: Introducing the Health Utility Book (HUB)

2019 ◽  
Vol 39 (4) ◽  
pp. 371-379 ◽  
Author(s):  
Feng Xie ◽  
Michael Zoratti ◽  
Kelvin Chan ◽  
Don Husereau ◽  
Murray Krahn ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Cost Effectiveness ◽  
Systematic Approach ◽  
Health Utility ◽  
Reimbursement Decision ◽  
Health State ◽  
Life Years ◽  
Health State Utility Values

Cost-utility analysis (CUA) is a widely recommended form of health economic evaluation worldwide. The outcome measure in CUA is quality-adjusted life-years (QALYs), which are calculated using health state utility values (HSUVs) and corresponding life-years. Therefore, HSUVs play a significant role in determining cost-effectiveness. Formal adoption and endorsement of CUAs by reimbursement authorities motivates methodological advancement in HSUV measurement and application. A large body of evidence exploring various methods in measuring HSUVs has accumulated, imposing challenges for investigators in identifying and applying HSUVs to CUAs. First, large variations in HSUVs between studies are often reported, and these may lead to different cost-effectiveness conclusions. Second, issues concerning the quality of studies that generate HSUVs are increasingly highlighted in the literature. This issue is compounded by the limited published guidance and methodological standards for assessing the quality of these studies. Third, reimbursement decision making is a context-specific process. Therefore, while an HSUV study may be of high quality, it is not necessarily appropriate for use in all reimbursement jurisdictions. To address these issues, by promoting a systematic approach to study identification, critical appraisal, and appropriate use, we are developing the Health Utility Book (HUB). The HUB consists of an HSUV registry, a quality assessment tool for health utility studies, and a checklist for interpreting their use in CUAs. We anticipate that the HUB will make a timely and important contribution to the rigorous conduct and proper use of health utility studies for reimbursement decision making. In this way, health care resource allocation informed by HSUVs may reflect the preferences of the public, improve health outcomes of patients, and maintain the efficiency of health care systems.

scholarly journals Using Discrete Choice Experiments with Duration to Model EQ-5D-5L Health State Preferences

2016 ◽  
Vol 37 (3) ◽  
pp. 285-297 ◽  
Author(s):  
Brendan Mulhern ◽  
Nick Bansback ◽  
Arne Risa Hole ◽  
Aki Tsuchiya
Keyword(s):  
Discrete Choice ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Health State ◽  
Two Stage ◽  
Health States ◽  
Stage Design ◽  
Choice Sets ◽  
Two Stage Design ◽  
The Uk

Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing estimated utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs were assessed. Results: The zero prior value design produced a model with coefficients that were generally logically ordered, but the non-zero prior value design resulted in a set of less ordered coefficients where some differed significantly. The two-stage design resulted in ordered and significant coefficients. The non-zero prior value design may include more “difficult” choice sets, based on the proportions choosing each profile. Conclusions: There is some indication of compromised “respondent efficiency”, suggesting that the use of non-zero prior values will not necessarily result in better overall precision. It is feasible to design discrete choice experiments in two stages by allocating duration values to EQ-5D-5L health state pairs based on estimates from prior studies.

scholarly journals Do health preferences differ among Asian populations? A comparison of EQ-5D-5L Discrete Choice Experiments data from 11 Asian studies

2021 ◽  
Author(s):  
Zhihao Yang ◽  
Fredrick Purba ◽  
Asrul Akmal Shafie ◽  
Ataru Igarashi ◽  
Eliza Wong ◽  
...  
Keyword(s):  
Discrete Choice ◽  
Population Sample ◽  
Health Utility ◽  
Discrete Choice Experiments ◽  
Relative Importance ◽  
Asian Studies ◽  
Health States ◽  
Asian Populations ◽  
Euroqol Group ◽  
Main Effects

Abstract Introduction For establishing EQ-5D-5L value set, the EuroQol Group developed a standardized EQ-VT protocol. Both time-trade off (TTO) method and discrete choice experiment (DCE) method are used in the EQ-VT protocol. Published studies did not make use of the DCE data for comparison purpose. This study aims to compare the health preferences among 11 Asian studies using the DCE data collected in their EQ-5D-5L valuation studies. Methods In the EQ-VT protocol, 196 pairs of EQ-5D-5L health states were valued by a general population sample using DCE method for all 11 studies. DCE data was obtained from the study PI. Three different main-effects models were fitted for each study. Coefficients were first tested between studies. Next, the relative importance of dimensions and levels was calculated and compared. Results The number of statistically differed coefficients ranged from 5 to 16, out of 20 main-effects coefficients. For the relative importance, there is not a universal preference pattern that fits all studies, but with some common characteristics e.g. mobility is the most weighted dimension except for Vietnam; the relative importance of levels were approximately 20% for level 2, 30% for level 3, 70% for level 4 across all studies. Discussion This study confirmed that health preferences heterogeneity among Asian populations. It is therefore justifiable to announce that national/regional value sets should be used for calculating health utility.

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