Mapping FACT-Melanoma Quality-of-Life Scores to EQ-5D Health Utility Weights

Askew, Robert L.; Swartz, Richard J.; Xing, Yan; Cantor, Scott B.; Ross, Merrick I.; Gershenwald, Jeffrey E.; Palmer, J. Lynn; Lee, Jeffrey E.; Cormier, Janice N.

doi:10.1016/j.jval.2011.04.003

Cited by 46 publications

(36 citation statements)

References 41 publications

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“…Mean FACT-M scores were lower than reported in a previous study of patients with stage IV melanoma [24,44]. This could be due to the fact that MCC is in general a more aggressive tumor than melanoma [6].…”

Section: Discussioncontrasting

confidence: 68%

Nonprogression with Avelumab Treatment Associated with Gains in Quality of Life in Metastatic Merkel Cell Carcinoma

Kaufman

Hünger²,

Hennessy

et al. 2017

Future Oncol.

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Section: Discussioncontrasting

confidence: 68%

Nonprogression with Avelumab Treatment Associated with Gains in Quality of Life in Metastatic Merkel Cell Carcinoma

Kaufman

Hünger²,

Hennessy

et al. 2017

Future Oncol.

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“…A mapping study by Askew et al reported a utilities of 0.85 for stage III melanoma (n = 100) and 0.86 for stage IV melanoma (n = 102). [17] Dixon et al, in a trial of interferon-alpha compared to placebo, collected EQ-5D over time, increasing from 0.76 at baseline (n = 111), to 0.77 at 3 months (n = 80), 0.82 at 12 months (n = 66), and finally to 0.83 at 24 months (n = 31) [18]; personal communication with the author. Two vignette studies were also available (bespoke descriptions of health states that are valued by the general public) – one based in Australia/UK (n = 77/63) published by Beusterien et al [19], and one based in Canada (n = 87).…”

Section: Discussionmentioning

confidence: 99%

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

Hatswell

Pennington

Pericleous

et al. 2014

Health Qual Life Outcomes

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BackgroundHealth-related quality of life is often collected in clinical studies, and forms a cornerstone of economic evaluation. This study had two objectives, firstly to report and compare pre- and post-progression health state utilities in advanced melanoma when valued by different methods and secondly to explore the validity of progression-based health state utility modelling compared to modelling based upon time to death.MethodsUtilities were generated from the ipilimumab MDX010-20 trial (Clinicaltrials.gov Identifier: NCT00094653) using the condition-specific EORTC QLQ-C30 (via the EORTC-8D) and generic SF-36v2 (via the SF-6D) preference-based measures. Analyses by progression status and time to death were conducted on the patient-level data from the MDX010-20 trial using generalised estimating equations fitted in Stata®, and the predictive abilities of the two approaches compared.ResultsMean utility showed a decrease on disease progression in both the EORTC-8D (0.813 to 0.776) and the SF-6D (0.648 to 0.626). Whilst higher utilities were obtained using the EORTC-8D, the relative decrease in utility on progression was similar between measures. When analysed by time to death, both EORTC-8D and SF-6D showed a large decrease in utility in the 180 days prior to death (from 0.831 to 0.653 and from 0.667 to 0.544, respectively). Compared to progression status alone, the use of time to death gave similar or better estimates of the original data when used to predict patient utility in the MDX010-20 study. Including both progression status and time to death further improved model fit. Utilities seen in MDX010-20 were also broadly comparable with those seen in the literature.ConclusionsPatient-level utility data should be analysed prior to constructing economic models, as analysis solely by progression status may not capture all predictive factors of patient utility and time to death may, as death approaches, be as or more important. Additionally this study adds to the body of evidence showing that different scales lead to different health state values. Further research is needed on how different utility instruments (the SF-6D, EORTC-8D and EQ-5D) relate to each other in different disease areas.Electronic supplementary materialThe online version of this article (doi:10.1186/s12955-014-0140-1) contains supplementary material, which is available to authorized users.

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“…11 Our model has RMSE between 0.04 and 0.06 when tested in three validation data sets, lower than most algorithms that mapped from disease-specific descriptive to generic utility instruments. 40 We considered various approaches to mapping utilities 11,12 and chose linear regression because it performed well compared with other models, including Tobit, 41,42 two-part 43 or censored least absolute deviation, 44 while having significant advantages in ease of use, implementation and interpretability. We mapped the scores on the six scales of the PCI, rather than item scores, to PORPUS-U utility.…”

Section: Discussionmentioning

confidence: 99%

Predicting utility scores for prostate cancer: mapping the Prostate Cancer Index to the Patient-Oriented Prostate Utility Scale (PORPUS)

Bremner

Mitsakakis

Wilson

et al. 2013

Prostate Cancer Prostatic Dis

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Our algorithm can calculate PORPUS-U utility scores from PCI scores, thus supplementing descriptive quality of life measures with utility scores in PC patients. Utilities derived from mapping algorithms are useful for assigning utility to groups of patients but are less accurate at predicting utility of individual patients. We are exploring statistical methods to improve the mapping of utilities from descriptive instruments.

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Mapping FACT-Melanoma Quality-of-Life Scores to EQ-5D Health Utility Weights

Abstract: The OLS mapping function demonstrated better predictive ability and will facilitate the derivation of utilities when direct population preference measures are not available.

Cited by 46 publications

References 41 publications

Nonprogression with Avelumab Treatment Associated with Gains in Quality of Life in Metastatic Merkel Cell Carcinoma

Nonprogression with Avelumab Treatment Associated with Gains in Quality of Life in Metastatic Merkel Cell Carcinoma

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

Predicting utility scores for prostate cancer: mapping the Prostate Cancer Index to the Patient-Oriented Prostate Utility Scale (PORPUS)

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