Advocating a Paradigm Shift in Health-State Valuations: The Estimation of Time-Preference Corrected QALY Tariffs

Jonker, Marcel F.; Donkers, Bas; Bekker‐Grob, Esther W. de; Stolk, Elly

doi:10.1016/j.jval.2018.01.016

Cited by 35 publications

(27 citation statements)

References 35 publications

(40 reference statements)

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“…Time-preference-corrected quality-adjusted life year (QALY) tariffs avoid confounding between quality of life and time preferences without resulting in time-dependent QALY tariffs. 1 In nominal terms, each QALY still represents 1 year in perfect health, which conforms to the conventional QALY assumptions. However, when QALYs are compared across time, they can and should be discounted to properly reflect time preferences.…”

Section: Introductionmentioning

confidence: 74%

On the Optimization of Bayesian D-Efficient Discrete Choice Experiment Designs for the Estimation of QALY Tariffs That Are Corrected for Nonlinear Time Preferences

Jonker

Bliemer

2019

Value in Health

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Objectives: This article explains how to optimize Bayesian D-efficient discrete choice experiment (DCE) designs for the estimation of quality-adjusted life year (QALY) tariffs that are unconfounded by respondents' time preferences. Methods:The calculation of Bayesian D-errors is explained for DCE designs that allow for the disentanglement of respondents' time and health-state preferences. Time preferences are modelled via an exponential, hyperbolic, or power discount function and the performance of the proposed DCE designs is compared with that of several conventional DCE designs that do not take nonlinear time preferences into account.Results: Based on the achieved D-error, asymptotic standard error, and estimated sample size to obtain statistically significant estimates of the discount rate parameters, the proposed designs outperform the conventional DCE designs. Conclusions:We recommend that applied researchers use appropriately optimized DCE designs for the estimation of QALY tariffs that are corrected for time preferences. The TPC-QALY software package that accompanies this article makes the recommended designs easily accessible for health-state valuation researchers.

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Section: Introductionmentioning

confidence: 74%

On the Optimization of Bayesian D-Efficient Discrete Choice Experiment Designs for the Estimation of QALY Tariffs That Are Corrected for Nonlinear Time Preferences

Jonker

Bliemer

2019

Value in Health

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“…One area in particular is the assumption of linear time preferences and how this may violate basic human behavior. Recent studies have shown that allowing for nonlinear time preferences, through the use of discounting functions, provides a better statistical fit [6,15]. These differences could lead to misallocation of resources and incorrect implementation of interventions against COVID-19 depending on which value set is chosen.…”

Section: Discussionmentioning

confidence: 99%

QALYs for COVID-19: A Comparison of US EQ-5D-5L Value Sets

Poteet

Craig

2021

Patient

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Background In economic evaluations, quality-adjusted life-years (QALYs) can serve as a unit of measurement for disease burden. Obtaining QALY values for COVID-19 presents a challenge owing to the availability of two US EQ-5D-5L value sets and the potentially asymptomatic presentation of the disease. The first value set was completed allowing for the discounting of future health outcomes while the second value set is undiscounted. Objective The objective of this study was to compare the distribution of QALY values using a national survey and the two published value sets; and to estimate the association between COVID-19 outcomes and QALY losses. Methods Between 9 and 11 November, 2020, 1153 US adults completed the EQ-5D-5L instrument (five items and a visual analog scale) as well as self-reported their demographics, COVID-19 symptoms, and memberships to populations that are at risk of COVID-19 infection. The two US value sets were applied to the EQ-5D-5L responses to produce QALY values. We estimated the mean QALYs by visual analog scale decile and a generalized linear model of COVID-19 outcomes. Results The discounted values are higher than the undiscounted values for each visual analog scale decile owing to methodological differences. Persons at increased risk, with a fever in the past day, and with one or more other symptoms have significantly greater QALY losses ( p < 0.01). Overall, non-institutionalized individuals at risk of symptomatic clinical COVID-19 equal 0.68 for the 2016 value set (95% confidence interval 0.49–0.87) and 0.10 for the 2017 value set (95% confidence interval − 0.31 to 0.51) QALYs. Conclusions Multiple studies have shown that decision makers discount future health outcomes, which increase QALY values. This study confronts the practical implications of these methodological advances for use in COVID-19 economic evaluations. Health economists will be able to use the QALY values in this study to better evaluate health interventions against COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s40271-021-00509-z.

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“…The choice sets will be explained as textual and visual scenarios. We will use visual elements to reduce the likelihood of possible dropping due to choice set complexity or illiteracy that will help participants to quickly identify the levels [ 23 , 40 , 41 ]. An example of a DCE choice task is shown in Fig.…”

Section: Study Design/methodsmentioning

confidence: 99%

Application of discrete choice experiments to estimate value of life: a national study protocol in Iran

Mirzaee

Takian

Farzadfar

et al. 2021

Cost Eff Resour Alloc

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Background Global concerns regarding the significant burden of non-communicable diseases and injuries (NCDIs) exist from both public health and economic perspectives. Our research focuses on the reduction of fatal risks due to NCDIs and the citizens’ preferences about health programs and intervention to reduce premature death due to NCDIs. Governments and health authorities need reliable evidence and information to prioritize the interests of their citizens. One crucial piece of evidence to justify the resources spent on NCDIs is the value derived from the interventions on prevention and NCDIs control. This concept is usually called “Value of Statistical Life” (VSL), meaning the monetary value that individuals place on changes in the risk levels of life- threatening events. To the best of our knowledge, for the first time, our study will estimate the statistical value of life for selected interventions for the prevention and control of NCDIs at both national and sub-national levels in the context of Iran. This paper reports the development of a national protocol through Discrete Choice Experiments (DCEs) method. Methods and designs Our study comprises several stages: (a) a literature review to identify the attributes and levels of the prevention programs and Willingness to Pay (WTP) for reducing the NCDI’s fatal risks; (b) experimental design to assessing, prioritizing, and finalizing the identified attributes and levels; (c) instrumental design to conduct face-to-face structured survey interviews of 3180 respondents aged 18–69 across the entire country; (d) statistical analysis to estimate the results through the Mixed Multinomial logit (MMNL) model. Discussion We anticipate that our findings will help build a stronger empirical basis for monetizing the value of small changes in selected fatality risks. It paves the way for other national or vast VSL estimates for NCDIs, as well as other major causes of morbidity and mortality in the context of Iran, and perhaps other low and middle-income countries (LMICs).

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Advocating a Paradigm Shift in Health-State Valuations: The Estimation of Time-Preference Corrected QALY Tariffs

Abstract: Our results highlight the relevance and even necessity of a paradigm shift in health valuation studies in favor of time-preference corrected QALY tariffs, with potentially important implications for health technology assessment calculations and regulatory decisions.

Cited by 35 publications

References 35 publications

On the Optimization of Bayesian D-Efficient Discrete Choice Experiment Designs for the Estimation of QALY Tariffs That Are Corrected for Nonlinear Time Preferences

On the Optimization of Bayesian D-Efficient Discrete Choice Experiment Designs for the Estimation of QALY Tariffs That Are Corrected for Nonlinear Time Preferences

QALYs for COVID-19: A Comparison of US EQ-5D-5L Value Sets

Application of discrete choice experiments to estimate value of life: a national study protocol in Iran

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