Abstract
A new version of the EQ‐5D, the EQ‐5D‐5L, is available. The aim of this study is to produce a value set to support use of EQ‐5D‐5L data in decision‐making.The study design followed an international research protocol. Randomly selected members of the English general public completed 10 time trade‐off and 7 discrete choice experiment tasks in face‐to‐face interviews.A 20‐parameter hybrid model was used to combine time trade‐off and discrete choice experiment data to generate values for the 3,125 EQ‐5D‐5L health states.Valuation data are available for 996 respondents. Face validity of the data has been demonstrated, with more severe health states generally given lower values. Problems with pain/discomfort and anxiety/depression received the greatest weight. Compared to the existing EQ‐5D‐3L value set, there are considerably fewer “worse than dead” states (5.1%, compared with over one third), and the minimum value is higher. Values range from −0.285 (extreme problems on all dimensions) to 0.950 (for health states 11211 and 21111).Results have important implications for users of the EQ‐5D‐5L both in England and internationally. Quality‐adjusted life year gains from interventions seeking to improve very poor health may be smaller using this value set and may previously have been overestimated.
Highlights
Health care decisions are made under uncertainty, whereby any decision may have a range of different outcomes
Each interview consisted of the following tasks: self‐reported health using EQ‐5D‐5L, self‐reported health on a 0–100 visual analogue scale; basic background questions; a practice time trade‐off (TTO) task; 10 TTO tasks; structured feedback questions regarding the TTO tasks; seven discrete choice experiment (DCE) tasks; structured DCE feedback questions; an open‐ended comment box; and further England‐specific background questions
We have reported a value set for the EQ‐5D‐5L, based on the preferences of a random sample of the English general public
Summary
Health care decisions are made under uncertainty, whereby any decision may have a range of different outcomes. To make the “best” decision, potential outcomes need ordering and valuing. Such decisions are made both at the individual level, such as choosing the optimal treatment for a patient, and at the national level, such as choosing how to allocate resources between treatments for different patient groups and across different health conditions. Evidence on patients' HRQL can be obtained using patient‐reported outcome (PRO) measures. These may be condition specific or generic (see Fayers and Machin, 2016, and Longworth et al, 2014, for further information).
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