Quality-Adjusted Life-Years without Constant Proportionality

Craig, Benjamin M.; Rand‐Hendriksen, Kim; Bailey, Henry; Stalmeier, Peep F. M.

doi:10.1016/j.jval.2018.02.004

Cited by 26 publications

(30 citation statements)

References 32 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…The lack of discounting in health valuations causes QALY values of "poor" health to be lower (downward bias) [6]. For example, each TTO response equates 10 years with EQ-5D-5L attributes to less time in full health; therefore, "good" health trades long-term life-years and "poor" health trades near-term life-years.…”

Section: Us Eq-5d-5l Value Setsmentioning

confidence: 99%

“…These value sets are based on health valuation studies and each study may use different elicitation techniques such as time trade-offs (TTOs) or discrete choice experiments. Apart from the elicitation technique, there are significant differences among value sets depending on the analysis and country of origin [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Poteet

Craig

2021

Patient

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Us Eq-5d-5l Value Setsmentioning

confidence: 99%

See 1 more Smart Citation

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

Poteet

Craig

2021

Patient

Self Cite

View full text Add to dashboard Cite

show abstract

“…After the IRB exemption determination, US adults were recruited from a marketing panel (Dynata ® ) targeting 18 demographic quotas (Protocol, see ESM) [29][30][31]. Under a double opt-in process, respondents consented when they first joined the panel and on the first page of the survey instrument.…”

Section: Fieldingmentioning

confidence: 99%

United States COVID-19 Vaccination Preferences (CVP): 2020 Hindsight

Craig

2021

Patient

View full text Add to dashboard Cite

Background Shortly after the 2020 US election, initial evidence on first-generation COVID-19 vaccines showed 70–95% efficacy and minimal risks. Yet, many US adults expressed reluctance. Aims The aim of this study was to compare persons willing and unwilling to be vaccinated against COVID-19 and to estimate the effects of vaccination attributes on uptake: proof of vaccination, vaccination setting, effectiveness, duration of immunity, and risk of severe side effects. Method Between 9 and 11 November 2020, 1153 US adults completed a discrete choice experiment (DCE) on Phase 2 of the CDC Vaccination Program (August 2021). Each of its eight choice tasks had three vaccination alternatives and “no vaccination for 6 months.” An opt-out inflated logit model was estimated to test for respondent differences and attribute effects. Results Respondent demographics were unrelated to one’s willingness to be vaccinated ( p value 0.533), but those with less education were more likely to be unwilling ( p < 0.001). Among those willing, uptake ranged from 61.70 to 97.75%, depending on the vaccination attributes. Effectiveness and safety had the largest effects. Offering proof of vaccination and a choice of setting increased uptake as much as increasing immunity from 3 to 6 months. Conclusions To maximize uptake, the CDC Program should standardize proof of vaccination and offer a choice of setting, instead of a one-size-fits-all approach. If the first-generation vaccines are efficacious, widely available, and free, overall predicted uptake is 68.81% by the end of Phase 2 (August 2021), which is well below the 75–90% needed for herd immunity. Further health preference research is necessary to uncover and address unwillingness and reluctance to vaccinate against COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s40271-021-00508-0.

show abstract

“…Nevertheless, many authors have found utility of life years to deviate from linearity in the ranges typically considered in TTO, 16,[21][22][23] where the severity of this deviation may even depend on how duration is described. 24 Such utility curvature will only affect TTO weights because this method depends critically on tradeoffs in duration. As shown in Bleichrodt, 5 if utility of life years is concave (ie, each extra year of life is worth less) instead of linear, TTO weights are biased downward.…”

Section: The Corrective Approach: Rationale and Overview Of Earlier Workmentioning

confidence: 99%

The Corrective Approach: Policy Implications of Recent Developments in QALY Measurement Based on Prospect Theory

2019

View full text Add to dashboard Cite

Background and Objectives: Common health state valuation methodology, such as time tradeoff (TTO) and standard gamble (SG), is typically applied under several descriptively invalid assumptions, for example, related to linear quality-adjusted life years (QALYs) or expected utility (EU) theory. Hence, the current use of results from health state valuation exercises may lead to biased QALY weights, which may in turn affect decisions based on economic evaluations using such weights. Methods have been proposed to correct responses for the biases associated with different health state valuation techniques. In this article we outline the relevance of prospect theory (PT), which has become the dominant descriptive alternative to EU, for health state valuations and economic evaluations. Methods and Results: We provide an overview of work in this field, which aims to remove biases from QALY weights. We label this "the corrective approach." By quantifying PT parameters, such as loss aversion, probability weighting, and nonlinear utility, it may be possible to correct TTO and SG responses for biases in an attempt to produce more valid estimates of preferences for health states. Through straightforward examples, this article illustrates the effects of this corrective approach and discusses several unresolved issues that currently limit the relevance of corrected weights for policy. Conclusions: Suggestions for research addressing these issues are provided. Nonetheless, if validly corrected health state valuations become available, we argue in favor of using these in economic evaluations.

show abstract

Quality-Adjusted Life-Years without Constant Proportionality

Cited by 26 publications

References 32 publications

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

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

United States COVID-19 Vaccination Preferences (CVP): 2020 Hindsight

The Corrective Approach: Policy Implications of Recent Developments in QALY Measurement Based on Prospect Theory

Contact Info

Product

Resources

About