Quality-adjusted life years assessment using cabozantinib for patients with advanced hepatocellular carcinoma (aHCC) in the CELESTIAL trial.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 207-207 ◽  
Author(s):  
Ghassan K. Abou-Alfa ◽  
Patrick Mollon ◽  
Tim Meyer ◽  
Ann-Lii Cheng ◽  
Anthony B. El-Khoueiry ◽  
...  
Keyword(s):  
Minimal Important Difference ◽  
Health Utility ◽  
Advanced Hepatocellular Carcinoma ◽  
Life Questionnaire ◽  
Quality Adjusted Life Years ◽  
Small Reduction ◽  
Health States ◽  
Life Years ◽  
The Difference ◽  
Quality Adjusted Life

207 Background: In patients previously treated for aHCC, cabozantinib (cabo) led to longer overall survival and progression-free survival vs placebo (pbo) in the randomized, phase 3 CELESTIAL trial (NCT01908426; N = 707). CELESTIAL was stopped early for benefit at the second interim analysis. This post hoc analysis estimated the incremental quality-adjusted life years (QALYs) accrued in CELESTIAL. Methods: Health utility was elicited at each study visit using the EQ-5D-5L quality of life questionnaire. (completed by 82–100% of patients overall). UK crosswalk tariffs were applied for health states. Cumulative QALYs by patient were calculated by linear interpolation; for patients who were censored (31% of patients; including 9% within 100 days of randomization), the last observed utility value was carried forward to study end. The difference in restricted mean QALYs was calculated using generalized linear models, accounting for baseline utility, and with 0.06–0.08 QALYs considered the minimal important difference. Results: At day 50 after randomization (acute treatment phase), cabo was associated with a small reduction in mean total QALYs vs pbo (difference −0.003; 95% CI −0.005 to −0.002; p ≤ 0.001; n = 601 [cabo, n = 389; pbo, n = 212]). At day 100, there was a numerical benefit in mean total QALYs for cabo (difference +0.007; 95% CI −0.001 to 0.015; p = 0.103; n = 627 [cabo, n = 410; pbo, n = 217]), and at day 150 the difference was +0.032 QALYs (95% CI 0.017 to 0.047; p ≤ 0.001; n = 629 [cabo, n = 412; pbo, n = 217]) in favor of cabo. Over the entire follow-up, patients randomized to cabo accrued a mean of +0.092 (95% CI 0.016 to 0.169; p = 0.018; n = 700 [cabo, n = 465; pbo, n = 235]) additional QALYs compared with those receiving pbo. Using alternative Devlin weights for health states, the mean accrued QALYs with cabo was +0.115 vs pbo (95% CI 0.032 to 0.198; p = 0.007). Conclusions: Cabo was associated with an initial, small reduction in health utility. However, with continued treatment, health utility increased and at the end of the study there was a clinically and statistically significant benefit in mean QALYs in favor of cabo. Clinical trial information: NCT01908426.

scholarly journals Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods

2020 ◽  
Vol 24 (34) ◽  
pp. 1-68 ◽  
Author(s):  
Mónica Hernández Alava ◽  
Allan Wailoo ◽  
Stephen Pudney ◽  
Laura Gray ◽  
Andrea Manca
Keyword(s):  
Cost Effectiveness ◽  
Linear Regression ◽  
Case Studies ◽  
Health Utility ◽  
Health Technology ◽  
Quality Adjusted Life Years ◽  
Indirect Methods ◽  
Direct Mapping ◽  
Life Years ◽  
Quality Adjusted Life

Background Cost-effectiveness analysis using quality-adjusted life-years as the measure of health benefit is commonly used to aid decision-makers. Clinical studies often do not include preference-based measures that allow the calculation of quality-adjusted life-years, or the data are insufficient. ‘Mapping’ can bridge this evidence gap; it entails estimating the relationship between outcomes measured in clinical studies and the required preference-based measures using a different data set. However, many methods for mapping yield biased results, distorting cost-effectiveness estimates. Objectives Develop existing and new methods for mapping; test their performance in case studies spanning different preference-based measures; and develop methods for mapping between preference-based measures. Data sources Fifteen data sets for mapping from non-preference-based measures to preference-based measures for patients with head injury, breast cancer, asthma, heart disease, knee surgery and varicose veins were used. Four preference-based measures were covered: the EuroQoL-5 Dimensions, three-level version (n = 11), EuroQoL-5 Dimensions, five-level version (n = 2), Short Form questionnaire-6 Dimensions (n = 1) and Health Utility Index Mark 3 (n = 1). Sample sizes ranged from 852 to 136,327. For mapping between generic preference-based measures, data from FORWARD, the National Databank for Rheumatic Diseases (which includes the EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, in its 2011 wave), were used. Main methods developed Mixture-model-based approaches for direct mapping, in which the dependent variable is the health utility value, including adaptations of methods developed to model the EuroQoL-5 Dimensions, three-level version, and beta regression mixtures, were developed, as were indirect methods, in which responses to the descriptive systems are modelled, for consistent multidirectional mapping between preference-based measures. A highly flexible approach was designed, using copulas to specify the bivariate distribution of each pair of EuroQoL-5 Dimensions, three-level version, and EuroQoL-5 Dimensions, five-level version, responses. Results A range of criteria for assessing model performance is proposed. Theoretically, linear regression is inappropriate for mapping. Case studies confirm this. Flexible, direct mapping methods, based on different variants of mixture models with appropriate underlying distributions, perform very well for all preference-based measures. The precise form is important. Case studies show that a minimum of three components are required. Covariates representing disease severity are required as predictors of component membership. Beta-based mixtures perform similarly to the bespoke mixture approaches but necessitate detailed consideration of the number and location of probability masses. The flexible, bi-directional indirect approach performs well for testing differences between preference-based measures. Limitations Case studies drew heavily on EuroQoL-5 Dimensions. Indirect methods could not be undertaken for several case studies because of a lack of coverage. These methods will often be unfeasible for preference-based measures with complex descriptive systems. Conclusions Mapping requires appropriate methods to yield reliable results. Evidence shows that widely used methods such as linear regression are inappropriate. More flexible methods developed specifically for mapping show that close-fitting results can be achieved. Approaches based on mixture models are appropriate for all preference-based measures. Some features are universally required (such as the minimum number of components) but others must be assessed on a case-by-case basis (such as the location and number of probability mass points). Future research priorities Further research is recommended on (1) the use of the monotonicity concept, (2) the mismatch of trial and mapping distributions and measurement error and (3) the development of indirect methods drawing on methods developed for mapping between preference-based measures. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 34. See the NIHR Journals Library website for further project information. This project was also funded by a Medical Research Council grant (MR/L022575/1).

A FALLACY OF THE MULTIPLICATIVE QALY MODEL FOR LOW-QUALITY WEIGHTS IN STUDENTS AND PATIENTS JUDGING HYPOTHETICAL HEALTH STATES

2001 ◽  
Vol 17 (4) ◽  
pp. 488-496 ◽  
Author(s):  
Peep F. M. Stalmeier ◽  
Gretchen B. Chapman ◽  
Angela G. E. M. de Boer ◽  
Jan J. B. van Lanschot
Keyword(s):  
Poor Health ◽  
Quality Adjusted Life Years ◽  
Positive Health ◽  
Present Contribution ◽  
Health States ◽  
Reversal Phenomenon ◽  
Life Years ◽  
Preference Reversal Phenomenon ◽  
Quality Adjusted Life

Objectives: In quality-adjusted life-years (QALY) models, it is customary to weigh life-years with quality of life via multiplication. As a consequence, for positive health states a longer duration has more QALYs than a shorter duration (i.e., longer is better). However, we have found that for poor health states, many prefer to live only a limited amount of time (i.e., longer is worse). Such preferences are said to be maximum endurable time (MET). In the present contribution, the following questions are asked: a) How low does the utility have to be in order for a MET to arise? and b) Do MET preferences occur when patients judge hypothetical health states?Methods and Results: We reanalyzed data from 176 students for the hypothetical health states of “living with migraines” and “living with metastasized cancer.” For utilities smaller than 0.7 (ranging from 0 to 1), the MET preference rate was larger than 50%. High MET preference rates were also found in two new studies on migraine and esophageal cancer patients, who evaluated hypothetical health states related to their disease.Conclusions: We discuss the interpretation of the MET preferences and the preference reversal phenomenon. Standard QALY models imply that longer is better. However, we find that more often, longer is worse for poorly evaluated health states. Consider the following question: are 3 years with a weight of 0.3 equally as valuable as 1 year with a weight of 0.9? Our results suggest that the 3-year period may be less valuable because for poor health, many will prefer a 1-year over a 3-year period.

scholarly journals Predicting Quality Adjusted Life Years in young people attending primary mental health services

2021 ◽  
Author(s):  
Matthew Phillip Hamilton ◽  
Caroline X Gao ◽  
Kate Maree Filia ◽  
Jana Marcelle Menssink ◽  
Sonia Sharmin ◽  
...  
Keyword(s):  
Mental Health ◽  
Psychological Distress ◽  
Mental Health Services ◽  
Young People ◽  
Health Services ◽  
Health Utility ◽  
Depression And Anxiety ◽  
Quality Adjusted Life Years ◽  
Life Years ◽  
Quality Adjusted Life

Background: Quality Adjusted Life Years (QALYs) are often used in economic evaluations, yet utility weights for deriving them are rarely directly measured in mental health services. Objectives: We aimed to: (i) identify the best Transfer To Utility (TTU) algorithms and predictors for an adolescent specific Multi-Attribute Utility Instrument - the Assessment of Quality of Life - six dimensions (AQoL-6D) and (ii) assess ability of TTU algorithms to predict longitudinal change. Methods: We recruited 1107 young people attending Australian primary mental health services, collecting data at two time points, three months apart. Five linear and three generalised linear models were explored to identify the best TTU algorithm. Forest models were used to explore predictive ability of six candidate measures of psychological distress, depression and anxiety and linear / generalised linear mixed effect models were used to construct longitudinal predictive models for AQoL-6D change. Results: A depression measure (Patient Health Questionnaire-9) was the strongest independent predictor of health utility. Linear regression models with complementary log-log transformation of utility score were the best preforming models. Between-person associations were slightly larger than within-person associations for most of the predictors. Conclusions: Adolescent AQoL-6D utility can be derived from a range of psychological distress, depression and anxiety measures. TTU algorithms estimated from cross-sectional data may slightly bias QALY predictions. Toolkits: The TTU models produced by this study can be searched, retrieved and applied to new data to generate QALY predictions with the Youth Outcomes to Health Utility (youthu) R package - https://ready4-dev.github.io/youthu.

The Ranking Properties of Healthy-Years Equivalents and Quality-Adjusted Life-Years Under Certainty and Uncertainty

1995 ◽  
Vol 11 (1) ◽  
pp. 40-48 ◽  
Author(s):  
Magnus Johannesson
Keyword(s):  
Expected Utility ◽  
The Other ◽  
Quality Adjusted Life Years ◽  
Health States ◽  
Trade Off ◽  
Individual Preferences ◽  
Life Years ◽  
Ex Ante ◽  
Quality Adjusted Life ◽  
Health Profiles

AbstractThis paper investigates the theoretical properties of healthy-years equivalents (HYEs) and quality-adjusted life-years (QALYs). A distinction is made between ex ante HYEs (EA-HYEs) and expected HYEs (EXP-HYEs) and between risk-neutral quality-adjusted life-years (RN-QALYs) and risk-adjusted quality adjusted life-years (RA-QALYs). In the case of certainty, HYEs always rank health profiles according to individual preferences, whereas QALYs only rank health profiles according to individual preferences if constant proportional trade-off holds for all health states and if additive independence of quality in different periods holds. In the case of uncertainty, EA-HYEs always rank risky health profiles the same way as expected utility. The assumptions needed for the other measures to rank risky health profiles the same way as expected utility are: risk neutrality with respect to healthy time for EXP-HYEs; risk neutrality with respect to time in all health states and additive independence of quality in different periods for RN-HYEs; and constant proportional risk posture with respect to time in all health states and additive independence of quality in different periods for RA-QALYs.

Estimation of Health Utility and Quality Adjusted Life Years in Bilateral Hand Transplantation

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Ankita Harijee ◽  
Krishnakumar Thankappan ◽  
Mohit Sharma ◽  
Nagesh Noothanapati NageswaraRao ◽  
Tejal Patel ◽  
...  
Keyword(s):  
Health Utility ◽  
Quality Adjusted Life Years ◽  
Hand Transplantation ◽  
Life Years ◽  
Quality Adjusted Life

scholarly journals Using Published Health Utilities in Cost-Utility Analyses: Discrepancies and Issues in Cardiovascular Disease

2021 ◽  
pp. 0272989X2110045
Author(s):  
Ting Zhou ◽  
Zhiyuan Chen ◽  
Hongchao Li ◽  
Feng Xie
Keyword(s):  
Cardiovascular Disease ◽  
Health Utility ◽  
Cost Utility ◽  
Quality Adjusted Life Years ◽  
Utility Analysis ◽  
Health Utilities ◽  
Life Years ◽  
Reported Health ◽  
Quality Adjusted Life ◽  
Value Of Health

Background Health utilities are commonly used as quality weights to calculate quality-adjusted life years in cost-utility analysis (CUA). However, if published health utilities are not properly used, the credibility of CUA could be affected. Objectives To identify discrepancies in using published health utilities in CUAs for cardiovascular disease (CVD). Methods CVD CUAs in the Tufts Cost-Effectiveness Analysis Registry that reported health utilities were included in the analysis. References cited for health utilities in these CUAs were reviewed to identify the original health utility studies. The description and value of health utilities used in the CUA were compared with those reported in the original utility studies. Logistic regression was used to identify the factors that can predict the discrepancy. Results A total of 585 eligible CUAs published between 1977 and 2016 were identified and reviewed. Of these studies, 74.5% were published between 2007 and 2016. 442 CUAs that used a total of 2235 health utilities published in 203 original utility studies were included for the comparison. As compared with those utilities originally reported, only 596 (26.7%) health utilities had the same description and value, whereas 991 health utilities (44.3%) differed in both description and value. Of 1290 health utilities with a different description, 69.1% were due to different severity or disease. No explanation or justification was provided for 1171 (87.4%) of 1340 health utilities with different value. Conclusions There are concerning discrepancies in using published health utilities for CVD CUAs. Given the important role health utilities play in CUAs, authors of CUAs should always refer to the original studies for health utilities and be transparent about how published health utilities are selected and incorporated into CUAs.

Review and estimation of disutility for joint health states of severe and nonsevere hypoglycemic events in diabetes

2021 ◽  
Author(s):  
Xuanqian Xie ◽  
Jennifer Guo ◽  
Karen E Bremner ◽  
Myra Wang ◽  
Baiju R Shah ◽  
...  
Keyword(s):  
Construct Validity ◽  
Literature Search ◽  
Economic Evaluations ◽  
Quality Adjusted Life Years ◽  
Health States ◽  
Life Years ◽  
Joint Health ◽  
Biased Estimates ◽  
Quality Adjusted Life ◽  
Additive Methods

Aim: Many economic evaluations used linear or log-transformed additive methods to estimate the disutility of hypoglycemic events in diabetes, both nonsevere (NSHEs) and severe (SHEs). Methods: We conducted a literature search for studies of disutility for hypoglycemia. We used additive, minimum and multiplicative methods, and the adjusted decrement estimator to estimate the disutilities of joint health states with both NSHEs and SHEs in six scenarios. Results: Twenty-four studies reported disutilities for hypoglycemia in diabetes. Based on construct validity, the adjusted decrement estimator method likely provides less biased estimates, predicting that when SHEs occur, the additional impact from NSHEs is marginal. Conclusion: Our proposed new method provides a different perspective on the estimation of quality-adjusted life-years in economic evaluations of hypoglycemic treatments.

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