Abstract
PurposeThe EORTC QLU-C10D is a new multi-attribute utility instrument derived from the EORTC QLQ-C30, a widely used cancer-specific quality of life questionnaire. It covers ten dimensions: physical, role, social, emotional functioning, pain, fatigue, sleep, appetite, nausea, and bowel problems. To allow national health attitudes to be reflected, country-specific valuations are being performed by collaboration of the Multi-Attribute Utility Cancer (MAUCa) Consortium and the EORTC. The purpose of this paper is to provide German value sets (utility weights) for the QLU-C10D.MethodsValuations were run in a web-based setting in two general population samples of approximately 2000 adults in total. As the German version of the QLQ-C30 is presently undergoing a revision of the wording of one response category, valuations for both the current and the new version were performed (Germany 1 and 2). Utilities were elicited using a discrete choice experiment (DCE). Data were analyzed by conditional logistic regression and mixed logits.ResultsCompletion rates were 88.3% (1002/1135) and 90.4% (1016/1124) for Germany 1 and Germany 2 valuations, respectively. Dimensions with the largest impact on utility weights were, in this order: physical functioning, pain, role functioning, social functioning and nausea (same ordering for both German versions). Several violations of the logical ordering of levels were observed for Germany 1; this was largely improved for Germany 2.ConclusionThis study established German utility weights for the cancer-specific utility instrument QLU-C10D.
Highlights
To inform rational decision making in health care, the results of economic evaluations have become increasingly important in the past decades
To estimate qualityadjusted life year (QALY), life years are weighted by the utility of the health state experienced, where a value of 1 characterizes full health and 0 indicates a health state as poor as being dead
The main advantage of multi-attribute utility instruments (MAUI) is that once utility weights have been determined for the set of health states covered, these weights can be applied in future Cost utility analyses (CUA) without any further valuations
Summary
To inform rational decision making in health care, the results of economic evaluations have become increasingly important in the past decades. Cost utility analyses (CUA) have been recommended as the method of choice by various health authorities, such as NICE in the UK [1]. The primary outcome in a CUA is incremental cost per qualityadjusted life year (QALY). To estimate QALYs, life years are weighted by the utility of the health state experienced, where a value of 1 characterizes full health and 0 indicates a health state as poor as being dead. There are various methods to obtain the required utilities for CUAs. One approach that has increasingly gained popularity is the use of multi-attribute utility instruments (MAUI) [2]. The main advantage of MAUIs is that once utility weights have been determined for the set of health states covered, these weights can be applied in future CUAs without any further valuations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
