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

Hatswell, Anthony J.; Pennington, Becky; Pericleous, L.; Rowen, Donna; Lebmeier, M.; Lee, Dong Hoon

doi:10.1186/s12955-014-0140-1

Cited by 42 publications

(30 citation statements)

References 22 publications

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“…The EORTC-8D has previously been found to be broadly comparable to the EQ-5D [40], but that was within the same dataset that the EORTC-8D was developed from; thus, wider evaluation is required. This study provides one of the first external assessments of the instrument in comparison with EQ-5D-3L (see Hatswell et al [41] for a comparison of EORTC-8D to SF-6D).…”

Section: Discussionmentioning

confidence: 99%

Condition-specific or generic preference-based measures in oncology? A comparison of the EORTC-8D and the EQ-5D-3L

Lorgelly

Doble²,

Rowen

et al. 2016

Qual Life Res

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PurposeIt has been argued that generic health-related quality of life measures are not sensitive to certain disease-specific improvements; condition-specific preference-based measures may offer a better alternative. This paper assesses the validity, responsiveness and sensitivity of a cancer-specific preference-based measure, the EORTC-8D, relative to the EQ-5D-3L.MethodsA longitudinal prospective population-based cancer genomic cohort, Cancer 2015, was utilised in the analysis. EQ-5D-3L and the EORTC QLQ-C30 (which gives EORTC-8D values) were asked at baseline (diagnosis) and at various follow-up points (3 months, 6 months, 12 months). Baseline values were assessed for convergent validity, ceiling effects, agreement and sensitivity. Quality-adjusted life-years (QALYs) were estimated and similarly assessed. Multivariate regression analyses were employed to understand the determinants of the difference in QALYs.ResultsComplete case analysis of 1678 patients found that the EQ-5D-3L values at baseline were significantly lower than the EORTC-8D values (0.748 vs 0.829, p < 0.001). While the correlation between the instruments was high, agreement between the instruments was poor. The baseline health state values using both instruments were found to be sensitive to a number of patient and disease characteristics, and discrimination between disease states was found to be similar. Mean generic QALYs (estimated using the EQ-5D-3L) were significantly lower than condition-specific QALYs (estimated using the EORTC-8D) (0.860 vs 0.909, p < 0.001). The discriminatory power of both QALYs was similar.ConclusionsWhen comparing a generic and condition-specific preference-based instrument, divergences are apparent in both baseline health state values and in the estimated QALYs over time for cancer patients. The variability in sensitivity between the baseline values and the QALY estimations means researchers and decision makers are advised to be cautious if using the instruments interchangeably.

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Section: Discussionmentioning

confidence: 99%

Condition-specific or generic preference-based measures in oncology? A comparison of the EORTC-8D and the EQ-5D-3L

Lorgelly

Doble²,

Rowen

et al. 2016

Qual Life Res

Self Cite

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“…The standard progression-based utility approach has the limitation of not being able to capture the decline of cancer patient utilities towards the end of life. The previous studies have quantified the independent impact of time to death on cancer patient utilities, which were found to decline towards death [18][19][20]. Another reason for choosing a more complex method for modelling utility is the pseudo-progression experienced by some patients treated with immunotherapies (e.g., nivolumab and ipilimumab).…”

Section: Overviewmentioning

confidence: 99%

“…To avoid total dependence of utility modelling on progression status and to account for the decline of utility with regard to time to death [18], a more complex method that captures both progression status and time to death was considered better suited for modelling utility in this analysis. The EQ-5D data collected in the pivotal CheckMate 066 trial were used in a longitudinal model with both progression status and whether within 30 days of death as covariates, so that utility estimates can be obtained for all four combinations of progression status and time to death (e.g., progression free and more than 30 days from death).…”

Section: Health-related Quality Of Lifementioning

confidence: 99%

The cost-effectiveness of nivolumab monotherapy for the treatment of advanced melanoma patients in England

et al. 2018

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Background Nivolumab was the first programmed death receptor 1 (PD-1) immune checkpoint inhibitor to demonstrate long-term survival benefit in a clinical trial setting for advanced melanoma patients. Objective To evaluate the cost effectiveness of nivolumab monotherapy for the treatment of advanced melanoma patients in England. Methods A Markov state-transition model was developed to estimate the lifetime costs and benefits of nivolumab versus ipilimumab and dacarbazine for BRAF mutation-negative patients and versus ipilimumab, dabrafenib, and vemurafenib for BRAF mutation-positive patients. Covariate-adjusted parametric curves for time to progression, pre-progression survival, and post-progression survival were fitted based on patient-level data from two trials and long-term ipilimumab survival data. Indirect treatment comparisons between nivolumab, ipilimumab, and dacarbazine were informed by these covariate-adjusted parametric curves, controlling for differences in patient characteristics. Kaplan-Meier data from the literature were digitised and used to fit progression-free and overall survival curves for dabrafenib and vemurafenib. Patient utilities and resource use data were based on trial data or the literature. Patients are assumed to receive nivolumab until there is no further clinical benefit, assumed to be the first of progressive disease, unacceptable toxicity, or 2 years of treatment. Results Nivolumab is the most cost-effective treatment option in BRAF mutation-negative and mutation-positive patients, with incremental cost-effectiveness ratios of £24,483 and £17,362 per quality-adjusted life year, respectively. The model results are most sensitive to assumptions regarding treatment duration for nivolumab and the parameters of the fitted parametric survival curves. Conclusions Nivolumab is a cost-effective treatment for advanced melanoma patients in England.

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“…Hatswell, et al [30] showed that an analysis of patient level data is essential prior to populating an economic model. In many oncology models, progression status is used to define health states in the model and utilities/costs are assigned to these states.…”

Section: Cost Effectiveness Modelling Employing Individual Patient Damentioning

confidence: 99%

“…In many oncology models, progression status is used to define health states in the model and utilities/costs are assigned to these states. In the example of metastatic melanoma, Hatswell, et al [30] determined utilities according to progression status and time to death and showed that the use of time to death gave similar or better estimates of the original data when used to predict patient utility in the IPD dataset.…”

Section: Cost Effectiveness Modelling Employing Individual Patient Damentioning

confidence: 99%

How to Appropriately Extrapolate Costs and Utilities in Cost-Effectiveness Analysis

et al. 2017

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Costs and utilities are key inputs into any cost-effectiveness analysis (CEA). Their estimates are typically derived from individual patient level data collected as part of clinical studies whose follow up duration is often too short to allow a robust quantification of the likely costs and benefits a technology will yield over the entire patient's lifetime.In the absence of long-term data, some form of temporal extrapolation -to project short-term evidence over a longer time horizon -will be required. Temporal extrapolation inevitably involves assumptions regarding the behaviour of the quantities of interest beyond the time horizon supported by the clinical evidence.Unfortunately, the implications for decisions made on the basis of evidence derived following this practice and the degree of uncertainty surrounding the validity of any assumptions made are often not fully appreciated.The issue is compounded by the absence of methodological guidance concerning the extrapolation of nontime to event outcomes such as costs and utilities. This paper considers current approaches to predict long-term costs and utilities, highlights some of the challenges with the existing methods, and provides recommendations for future applications. It finds that, typically, economic evaluation models employ a simplistic approach to temporal extrapolation of costs and utilities. For instance, their parameters (e.g. mean) are typically assumed to be homogeneous with respect to both time and patients' characteristics. Furthermore, costs and utilities have often been modelled to follow the dynamics of the associated time-to-event outcomes. However, cost and utility estimates may be more nuanced, and it is important to ensure extrapolation is carried out appropriately for these parameters. Key points1. Need to consider how costs and utilities are extrapolated in a cost-effectiveness model 2. There is no guidance on methods for extrapolating costs and utilities 3. Existing applications use various approaches with little justification

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

Cited by 42 publications

References 22 publications

Condition-specific or generic preference-based measures in oncology? A comparison of the EORTC-8D and the EQ-5D-3L

Condition-specific or generic preference-based measures in oncology? A comparison of the EORTC-8D and the EQ-5D-3L

The cost-effectiveness of nivolumab monotherapy for the treatment of advanced melanoma patients in England

How to Appropriately Extrapolate Costs and Utilities in Cost-Effectiveness Analysis

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