Literature DB >> 26409613

Deriving a Preference-Based Measure for Myelofibrosis from the EORTC QLQ-C30 and the MF-SAF.

Clara Mukuria1, Donna Rowen2, John E Brazier2, Tracey A Young2, Beenish Nafees3.   

Abstract

BACKGROUND: Utility values are required for economic evaluation using cost-utility analyses. Often, generic measures such as the EuroQol five-dimensional questionnaire are used, but this may not appropriately reflect the health-related quality of life of patients with cancer including myelofibrosis.
OBJECTIVE: To derive a condition-specific preference-based measure for myelofibrosis using appropriate existing measures, the Myelofibrosis-Symptom Assessment Form and the European Organisation for Research and Treatment of Cancer Quality of Life 30 Questionnaire.
METHODS: Data from the Controlled Myelofibrosis Study with Oral JAK Inhibitor Treatment trial (n = 309) were used to derive the health state classification system. Psychometric and factor analyses were used to determine the dimensions of the classification system. Psychometric and Rasch analyses were then used to select an item to represent each dimension. Item selection was validated with experts. A selection of health states was valued by members of the general population using time trade-off. Finally, health state values were modeled using regression analysis to produce utility values for every state.
RESULTS: The Myelofibrosis 8 dimensions has eight dimensions: physical functioning, emotional functioning, fatigue, itchiness, pain under ribs on the left side, abdominal discomfort, bone or muscle pain, and night sweats. Regression models were estimated using time trade-off data from 246 members of the general population valuing a total of 33 states. The best performing model was a random effects maximum likelihood model producing utility values ranging from 0.089 to 1.
CONCLUSIONS: The Myelofibrosis 8 dimensions is a condition-specific preference-based measure for myelofibrosis. This measure can be used to generate utility values for myelofibrosis for any data set containing the Myelofibrosis-Symptom Assessment Form and the European Organisation for Research and Treatment of Cancer Quality of Life 30 Questionnaire data.
Copyright © 2015. Published by Elsevier Inc.

Entities:  

Keywords:  EORTC QLQ-C30; MF-SAF; myelofibrosis; preference-based measure

Mesh:

Substances:

Year:  2015        PMID: 26409613     DOI: 10.1016/j.jval.2015.07.004

Source DB:  PubMed          Journal:  Value Health        ISSN: 1098-3015            Impact factor:   5.725


  7 in total

1.  Discrepancies between the Dermatology Life Quality Index and utility scores.

Authors:  Fanni Rencz; Petra Baji; László Gulácsi; Sarolta Kárpáti; Márta Péntek; Adrienn Katalin Poór; Valentin Brodszky
Journal:  Qual Life Res       Date:  2015-12-18       Impact factor: 4.147

Review 2.  The Role of Condition-Specific Preference-Based Measures in Health Technology Assessment.

Authors:  Donna Rowen; John Brazier; Roberta Ara; Ismail Azzabi Zouraq
Journal:  Pharmacoeconomics       Date:  2017-12       Impact factor: 4.981

Review 3.  A Review of Ruxolitinib for the Treatment of Myelofibrosis: A Critique of the Evidence.

Authors:  Ros Wade; Robert Hodgson; Mousumi Biswas; Melissa Harden; Nerys Woolacott
Journal:  Pharmacoeconomics       Date:  2017-02       Impact factor: 4.981

4.  Development of a preference-based heart disease-specific health state classification system using MacNew heart disease-related quality of life instrument.

Authors:  Sanjeewa Kularatna; Donna Rowen; Clara Mukuria; Steven McPhail; Gang Chen; Brendan Mulhern; Jennifer A Whitty; Joshua Byrnes; Paul Scuffham; John Atherton; Stefan Höfer; William Parsonage
Journal:  Qual Life Res       Date:  2021-05-26       Impact factor: 4.147

5.  Australian Utility Weights for the EORTC QLU-C10D, a Multi-Attribute Utility Instrument Derived from the Cancer-Specific Quality of Life Questionnaire, EORTC QLQ-C30.

Authors:  Madeleine T King; Rosalie Viney; A Simon Pickard; Donna Rowen; Neil K Aaronson; John E Brazier; David Cella; Daniel S J Costa; Peter M Fayers; Georg Kemmler; Helen McTaggart-Cowen; Rebecca Mercieca-Bebber; Stuart Peacock; Deborah J Street; Tracey A Young; Richard Norman
Journal:  Pharmacoeconomics       Date:  2018-02       Impact factor: 4.981

6.  EXPAND, a dose-finding study of ruxolitinib in patients with myelofibrosis and low platelet counts: 48-week follow-up analysis.

Authors:  Alessandro M Vannucchi; Peter A W Te Boekhorst; Claire N Harrison; Guangsheng He; Marianna Caramella; Dietger Niederwieser; Françoise Boyer-Perrard; Minghui Duan; Nathalie Francillard; Betty Molloy; Monika Wroclawska; Heinz Gisslinger
Journal:  Haematologica       Date:  2018-11-15       Impact factor: 9.941

7.  Valuation of SF-6Dv2 Health States in China Using Time Trade-off and Discrete-Choice Experiment with a Duration Dimension.

Authors:  Jing Wu; Shitong Xie; Xiaoning He; Gang Chen; Gengliang Bai; Da Feng; Ming Hu; Jie Jiang; Xiaohui Wang; Hongyan Wu; Qunhong Wu; John E Brazier
Journal:  Pharmacoeconomics       Date:  2021-02-18       Impact factor: 4.981

  7 in total

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