Nancy Devlin Bram Roudijk Kristina Ludwig *Editors*

# Value Sets for EQ-5D-5L

A Compendium, Comparative Review & User Guide

Value Sets for EQ-5D-5L

Nancy Devlin • Bram Roudijk • Kristina Ludwig Editors

# Value Sets for EQ-5D-5L

A Compendium, Comparative Review & User Guide

*Editors* Nancy Devlin Centre for Health Policy University of Melbourne Melbourne, VIC, Australia

Bram Roudijk EuroQol Research Foundation Rotterdam, The Netherlands

Kristina Ludwig Department of Health Economics and Health Care Management, School of Public Health Bielefeld University Bielefeld, Germany

ISBN 978-3-030-89288-3 ISBN 978-3-030-89289-0 (eBook) https://doi.org/10.1007/978-3-030-89289-0

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# **Foreword by Michael Drummond**

Since its development by the EuroQol Group in 1990, the EQ-5D instrument has probably become the most widely used measure of health-related quality of life in economic evaluations of healthcare treatment and programmes. It is also frequently used in health technology assessments (HTAs), which are a key element in technology adoption decisions in several countries. Reasons for its success include it being generic, concise – and it being accompanied by the value sets that are required to support economic evaluation. This prominence of the EQ-5D means that it is important that the users of the instrument fully understand the attributes and limitations of these value sets. This is the aim of the book *Value Sets for EQ-5D-5L: A Compendium, Comparative Review & User Guide* by Devlin, Roudijk and Ludwig. The primary 'users' in this case are those using EQ-5D data in economic evaluation or other applications, and decision makers interpreting economic evaluations or HTAs as part of their decision-making processes.

The main impetus for the book is the development of the new '5L' version of the EQ-5D instrument. The original version had only three levels on each of the 5 dimensions of health-related quality of life, which in some dimensions resulted in a rather large or 'abrupt' change between levels. For example, in the dimension of 'mobility', a change from level two to level three is expressed as moving from 'some problems in walking about' to being 'confned to bed'. By moving from 3 to 5 levels on each of the dimensions, it is possible to characterise a more gradual change between health states, which is arguably more realistic.

The main implication of the decision to move from 3 to 5 levels was the need to develop a new series of value sets for the EQ-5D-5L. These represent the value of each of the 3125 health states defned by the instrument (i.e. individual combinations of the 5 levels and 5 dimensions) and are critical to the calculation of the quality-adjusted life years (QALYs) gained by healthcare treatments and programmes. This is no mean task, especially as there is no reason to believe that the preferences for health states of the general public would be the same in different jurisdictions. Indeed, more than 35 distinct value sets have been developed for the original 3L instrument, and decision makers in several jurisdictions require that the value set used in HTAs should be representative of the preferences of the population of the country concerned.

To undertake this task, the EuroQol Group embarked on an extensive programme of methodological and empirical research which is reported in the book. Rather than just replicate the approaches used in the development of value sets for the original instrument, the group took the opportunity to develop and strengthen the methods of valuing health states, with a view to specifying a common protocol. (This programme of research is discussed in Chaps. 2 and 3 of the book.)

Users of the new instrument will probably be most interested in the value sets themselves. These are described and classifed in the country-by-country overview given in Chap. 4. Looking at the classifcation given, it is hard to think of any critical information that is not given about the precise methods used to generate the value set for each country, the sample of individuals whose preferences were measured, and the main results obtained. I fnd this chapter useful both in providing information about each country, and for understanding the results for each country in the overall context of all the research conducted.

The comparisons between the various country value sets and issues of which value set to use are discussed in Chaps. 5 and 6. These choices can be quite complicated, especially during the period while value sets for the new instrument are still being developed. For example, if a value set is not currently available for EQ-5D-5L in my country, should I use a value set from a country I perceive to be similar to my own, or an earlier value set for my country from the 3L instrument, using the mapping/crosswalk algorithm that has been developed? What should I do if no value set for either instrument exists for my country?

The fnal chapter discusses the future for value sets. My take from it is that there will be continuing debate about the different results produced by different versions of the EQ-5D, what the change from 3 to 5 levels does to the sensitivity of the instrument, and the conditions under which it makes sense to develop a new value set for a particular jurisdiction. Being based in 'God's Own Country' (Yorkshire), I often wonder whether we should develop our own value set or continue to use those available for the UK! Some of these big questions will probably never be answered. But in the meantime, I have reached a simple conclusion. If you are a decision maker or researcher using the EQ-5D-5L, you should read this book.

Professor of Health Economics Michael Drummond University of York, York, UK

# **Foreword by Kim Rand**

The EuroQol Group started small, and made a concise instrument. After a short detour down the track towards a 6-dimension questionnaire, the EQ-5D was born in a form we easily recognize today, covering "no", "some", and "extreme" problems in mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. A visual analogue scale reminiscent of a thermometer completed the questionnaire, which took all of two pages. That's it.

Today, the EQ-5D is the most used instrument worldwide for measuring qualityadjusted life years (QALYs) for health-economic analyses, even though such use constitutes merely a fraction of publications using the instrument.

The brevity and simplicity of the EQ-5D is also its primary strength, and driver behind its apparent success. It is also the instrument's greatest weakness, and has spurned debates that continue unabated today: which dimensions of health should be included, how should they be described, and what levels of functioning should be provided?

The frst large-scale time trade-off (TTO) based national value set for the EQ-5D, from the seminal UK Measuring and Valuing Health (MVH) study, was published in 1997, initiating a furry of research and costly valuation studies for the 3-level version of the EQ-5D (EQ-5D-3L). In just a few years, the number of studies had risen to the point where potential users could easily lose track as to which value sets were available, and for what jurisdictions. The question as to which value set to use became a real issue. The 2007 book *EQ-5D Value Sets: Inventory, Comparative Review and User Guide* by Szende, Oppe, and Devlin came to the rescue, describing 8 TTO-based and 9 VAS-based national EQ-5D-3L value sets. Around the same time, the development of the new and current 5-level version of the EQ-5D was underway; after years of intense debates, it was decided that three levels of problems were impractically large grained as a descriptive tool. A new, fve level tool was developed: the EQ-5D-5L.

While the EQ-5D-5L remains the briefest of instruments in its class, and still takes up just two pages, the additional levels provided great challenges in terms of how the instrument should be valued. Over the past decade, the primary focus of the EuroQol Group has been a massive effort around the methods, logistics, tools, and decisions necessary to produce high-quality preference-based values for the instrument. As a consequence, 25 national value sets have been produced, and more are underway. While this is great news from a scientifc perspective, the co-existence of value sets for the 3L and 5L versions of the instrument has further complicated the question as to which value set to use.

This book, I am happy to say, does not provide a defnite answer. It does, however, provide insightful discussions around the topic, as well as simple and useful guidance as to how users of the EQ-5D worldwide can identify the value set best suited for their purposes.

The EQ-5D and associated value sets are powerful tools to guide decisionmaking in the health sector and beyond. However, value sets are derived in a particular setting, conventionally designed to refect the health preferences of the adult general population in a particular country. For use in public decision making, where legitimacy and transparency are increasingly required, decisions around what constitutes the best reference value set is a decision best placed at a national jurisdictional level. For any reader of this book who happens to be a decision maker in a country contemplating the use of the EQ-5D, I hope that this book will provide inspiration and insight to help develop national guidelines. We would, of course, be happy to be of assistance in this process.

For non-economic use, and for research involving international comparison, different reasoning may come into play. Regardless of the purpose for which value sets are used, this book, which will be updated with new value sets in the future, provides an excellent source of structured information on available EQ-5D-5L value sets, including methods and other details around the studies from which they derive.

While I appreciate having such books in hardback format, to be retrieved from my bookshelf at need, I am delighted and proud that this book is also available online and for free as an open-source e-book. In the spirit of transparency, *Value Sets for EQ-5D-5L: A Compendium, Comparative Review & User Guide* by Devlin, Roudijk, and Ludwig presents to the world what the EuroQol Group has produced over the last decade, and highlights the important challenges we are currently facing. This book is not intended to stop the debates around the EQ-5D. Rather, it provides an overview of where we currently stand in terms of value sets, which should be of interest to end users, researchers, and decision makers. In the meantime, the EuroQol Group is funding an unprecedented volume of research in a wide range of areas, including a strong push forward for the youth version of the instrument. New books will be needed in just a few years.

In the meantime, enjoy!

Chair of the EuroQol Executive Committee, Kim Rand EuroQol Research Foundation, Rotterdam, The Netherlands Co-founder and Principal, Maths in Health B.V., Rotterdam, The Netherlands Senior Researcher, Health Services Research Unit, Akershus University Hospital, Lørenskog, Norway

# **Preface**

The aim of this book is to collate information about and provide guidance on the use of EQ-5D-5L value sets, providing an easy-to-use resource for users of the EQ-5D-5L instrument. By creating this compendium of value sets, our hope is not just to increase the accessibility of this material, but to also encourage users to be aware of how these value sets were created, the characteristics of value sets, their differences and similarities, and the implications for their use in analysing of EQ-5D-5L data.

The availability of value sets is one of the reasons the EQ-5D instruments are widely used in economic evaluations in healthcare and in population studies around the world. However, these value sets are generally published in peer-reviewed scientifc journals largely aimed at other researchers. The information presented about these studies differs somewhat between journals, authors, and studies. Furthermore, not all value sets are published Open Access, making it more diffcult for some (especially non-academic) users to access the value sets. This creates a place for a book pulling together all the relevant information into a single source and similar format, allowing for an easier comparison.

Our focus in this book is on value sets for the EQ-5D-5L. The development of a standardised international protocol for conducting such studies, and the related training and quality control processes that nowadays accompany it, now constitutes a 'mature technology' that was successfully employed to generate a considerable number of value sets since the frst wave of studies commenced in 2012. The timing therefore seems appropriate to produce this compendium of these value sets. In doing so, we build on the precedent set by Szende, Oppe, and Devlin's 2007 book on EQ-5D-3L value sets, which continues to be widely used and cited as a resource on value sets for that instrument.

We are grateful to the EuroQol Research Foundation for their support of our work and in particular for their commitment to Open Access publication of this book. We would also like to express our gratitude to the principal investigators of

the value set studies reported in this book for their support and input, and to all those who contributed to the authorship of chapters. This was a team effort and a credit to the collegiality and common purpose of the EuroQol Group in promoting the measurement and valuation of health.

We hope you fnd this book useful!

# **Acknowledgements**

We acknowledge with gratitude the EQ-5D-5L value set principal investigators, and their research teams in each country, for giving us permission to include their studies in this book, allowing us access to their valuation data, and for their input to and clarifcations on the summaries presented in Chap. 4. Without their efforts in conducting these studies, this book would not have been possible. We are also grateful to Kim Rand, chair of the Executive Committee, and to Elly Stolk, scientifc team leader, for their careful reviews of the chapters and thoughtful comments and suggestions, and to Rosalind Rabin and KMHO for helpful edits. Lastly, we would like to thank Gerben Bakker for his support with legal and copyright matters.

Funding for this book was provided by the EuroQol Research Foundation EQ169-2020RA. Views expressed in this book are not necessarily those of the EuroQol Group.

# **Contents**

# **About the Editors**

**Nancy Devlin** is Professor of Health Economics at the University of Melbourne, Australia, and senior visiting fellow at the Offce of Health Economics, London. She was 2019–2020 president of ISPOR and a past president of the EuroQol Group. Her principal areas of research are the measurement and valuation of health-related quality of life, priority setting in healthcare, and the cost effectiveness thresholds used in making judgements about value for money in healthcare. Her previous books include *Methods for Analysing and Reporting EQ-5D Data* (with Bas Janssen and David Parkin), *Using Patient Reported Outcomes to Improve Health Care* (with John Appleby and David Parkin), and *Economic Analysis in Health Care* (with Stephen Morris, David Parkin, and Anne Spencer). She was also a co-author (with Agota Szende and Mark Oppe) of the book on EQ-5D-3L values, *EQ-5D Valuation Sets: An Inventory, Comparative Review and User Guide.* Nancy contributed to methodological work underpinning the EQ-VT protocol and to the development of EQ-5D-5L value sets in Mexico, Ireland, and England.

**Bram Roudijk** works as a scientist at the business offce of the EuroQol Research Foundation in Rotterdam, the Netherlands. His main research interests are valuation of health-related quality of life, modelling health-related quality of life valuation data, and differences in preferences for health-related quality of life between countries and cultures. Bram is involved in supporting valuation studies for the EQ-5D family of instruments, including the EQ-5D-5L. Furthermore, Bram is a member of EuroQol's Valuation Working Group.

**Kristina Ludwig** works as a senior scientist in the Department of Health Economics and Health Care Management at Bielefeld University, Germany, and as a freelance senior scientist for the business offce of the EuroQol Research Foundation in Rotterdam, the Netherlands. Her main research area is the measurement and valuation of health-related quality of life in the context of economic evaluation and measuring patient preferences. During her position at the EuroQol offce, she provided support to various valuation studies for the EQ-5D-5L. She contributed methodological work for the refnement of the EQ-VT protocol and was signifcantly involved in the development of the value sets for the EQ-5D instruments, including EQ-5D-5L in Germany.

# **Contributors**

**Jan Busschbach** Section of Medical Psychology, Department of Psychiatry, Erasmus MC, Rotterdam, The Netherlands

**Nancy Devlin** Centre for Health Policy, University of Melbourne, Melbourne, VIC, Australia

**Aureliano Paolo Finch** EuroQol Research Foundation, Rotterdam, The Netherlands

**Bas Janssen** EuroQol Research Foundation, Rotterdam, The Netherlands Section of Medical Psychology, Department of Psychiatry, Erasmus MC, Rotterdam, The Netherlands

**Kristina Ludwig** Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany EuroQol Research Foundation, Rotterdam, The Netherlands

**Richard Norman** School of Population Health, Curtin University, Bentley, Australia

**Jan Abel Olsen** Department of Community Medicine, University of Tromsø – The Arctic University of Norway, Tromsø, Norway Division of Health Services, Norwegian Institute of Public Health, Oslo, Norway

**Mark Oppe** Maths in Health B.V., Rotterdam, The Netherlands

**David Parkin** Offce of Health Economics, University of London, London, UK

**Simon Pickard** Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA

**Juan Manuel Ramos-Goñi** Maths in Health B.V., Rotterdam, The Netherlands

**Bram Roudijk** EuroQol Research Foundation, Rotterdam, The Netherlands

**Elly Stolk** EuroQol Research Foundation, Rotterdam, The Netherlands

**Ben van Hout** School of Health and Related Research, University of Sheffeld, Sheffeld, UK

Pharmerit International, York, UK

**Zhihao Yang** Health Services Management Department, Guizhou Medical University, Gui'an, People's Republic of China

# **Chapter 1 The Development of the EQ-5D-5L and its Value Sets**

**Nancy Devlin, Simon Pickard, and Jan Busschbach**

**Abstract** This chapter introduces the EQ-5D-5L questionnaire and its development by the EuroQol Group. The availability of the EQ-5D-5L, and the growing evidence of its pivotal role as a measurement system, generated a demand for 'values' to accompany it that would enable the use of EQ-5D-5L data in the estimation of quality-adjusted life-years (QALYs) and other applications where EQ-5D-5L profle data needs to be summarised by a single number. Chapter 1 sets out the main aim of the book: to provide an accessible source of information and guidance to support users of EQ-5D-5L and its value sets. Specifcally, the book aims to improve users' understanding of how EQ-5D-5L value sets are generated using the internationally standardised EQ-VT protocol; to raise awareness of the characteristics and properties of value sets; and to inform users' choice of which value set to select for which purpose, and how that choice may affect analysis. The chapter concludes with an overview of the content of the book.

# **1.1 The EQ-5D as an Instrument for Measuring and Valuing Health**

Since the 1990s, the EQ-5D instrument has held a pivotal role in the measurement of self-reported health status and health-related quality of life (HRQoL) (Devlin and Brooks 2017). The availability of a *concise* generic instrument for measuring

J. Busschbach Section of Medical Psychology, Department of Psychiatry, Erasmus MC, Rotterdam, The Netherlands

N. Devlin (\*)

Centre for Health Policy, University of Melbourne, Melbourne, VIC, Australia e-mail<EMAIL_ADDRESS>
S. Pickard

Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA

patients' and population self-reported health1 meant that it could be included, with minimal responder burden, in clinical trials, observational studies, and population health surveys. More recently, it has become the cornerstone of routine outcomes measurement in health care systems such as the English NHS PROMs programme and Sweden's national quality registers. The ability of the EQ-5D to measure HRQoL in a *generic* manner has the important advantage of yielding data that can readily be compared across disease areas and between patient and population subgroups, and against population norms. This broad comparability of EQ-5D data is particularly crucial in providing evidence that quantifes health benefts in a standardised and transparent manner to inform decisions regarding alternative ways of using health care resources.

The EQ-5D was developed by the EuroQol Group, then a small group of academics which has now grown into an international network of multidisciplinary researchers with more than 100 members worldwide (Devlin and Brooks 2017). The development of the EQ-5D was motivated in part by the specifc goal of providing evidence on the outcomes of health care programmes in a manner that would facilitate economic evaluation. One of the considerations underpinning the development of the instrument was that it would be accompanied by the 'values' (sometimes also referred to as 'utilities', 'quality of life weights', the 'EQ-5D Index' or 'EQ Index weights') that would enable the quality adjustment of life years as required for the estimation of quality-adjusted life-years (QALYs) used in cost effectiveness analysis (Drummond et al. 2015). The availability of value sets for this purpose has been a notable part of the success and uptake of EQ-5D instruments.

The value sets that accompany EQ-5D instruments provide a means of summarising, via a single number, how good or bad health status is as described by the EQ-5D. The responses to the EQ-5D instrument – that is, the particular combination of levels which are indicated on each of the fve dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), by those completing it – can be described as EQ-5D 'profles' (see Box 1.1). The original version of the EQ-5D, the EQ-5D-3L, has three response levels for each of the fve dimensions, describing a total of 35 = 243 possible profles (Brooks 1996). The focus of this book is on the later fve level version, the EQ-5D-5L, development of which is described in more detail in the following section – which describes a total of 55 = 3125 profles (Herdman et al. 2011).

The value sets for these instruments provide a single value for each of the possible profles described by them. These values lie on a scale anchored at 1 (full health) and 0 (dead), as is required for the estimation of QALYs. The values are built up from a set of sub-weights which represent the relative importance of each level of problem in each dimension, and indicate how good or bad these are overall, when combined in EQ-5D-5L profles. The term *value set* refers to a set of values

<sup>1</sup>There is ongoing debate about the term health-related quality of life (HRQoL) and whether EQ-5D measures HRQoL or self-perceived health status (Brazier and Karimi 2016). For simplicity, in the remainder of this book, we refer to the EQ-5D-5L value sets as providing values for health states or health as described by the EQ-5D-5L.

#### **Box 1.1: EQ-5D Questionnaires, EQ-5D Profles and Values** EQ-5D questionnaires comprise two key parts:

#### **The EQ-5D-5L questionnaire**

© EuroQol Research Foundation. EQ-5DTM is a trademark of the EuroQol Research Foundation. Reproduced by permission of EuroQol Research Foundation. Reproduction of this version is not allowed. For reproduction, use or modifcation of the EQ-5D (any version), please register your study by using the online EQ registration page: www.euroqol.org.

The EQ VAS is an important part of the questionnaire and provides the patients' overall assessment of their own health on a visual analogue scale. However, many applications of EQ-5D data, including the estimation of QALYs for economic evaluation, focus instead on the use of EQ-5D profle

#### **Box 1.1** (continued)

data. The profle data, and the use of value sets to summarise those data, is the focus of this book, and therefore the EQ VAS is not discussed further. There are in fact many ways of analysing EQ-5D profle data, as detailed in Devlin et al. (2020). One of these ways is by weighting the profle using values sets. This is the most common way of using EQ-5D data in cost effectiveness analysis. This book focuses on the value sets available for the EQ-5D-5L and their use in weighting EQ-5D-5L profle data.

The value sets provide a way of converting the profles into a single number that refects how good or bad people think they are. The values are usually obtained using stated preference methods, and yield values that lie on a scale anchored by the value of 1 for full health, and 0 for dead. EQ-5D values cannot be higher than 1, but values <0 are possible, and indicate health states considered on average to be worse than dead (WTD). Value sets are generally intended to represent the average preferences of local/national populations – so EQ-5D value sets differ between countries. See Chap. 4 for a summary of the available value sets for EQ-5D-5L, and Chap. 6 for information about the differences and similarities between them.

for all possible profles defned by a particular EQ-5D instrument, and is occasionally also referred to by other names, such as an EQ-5D 'tariff' or 'social values.' For the purposes of this book, we will use the terms value and value set.

These values are usually based on the average preferences of the relevant adult general population, obtained using stated preference methods such as the Time Trade-Off (TTO). These stated preference methods aim to elicit values which have the desired properties for estimating QALYs (see Box 5.1 in Chap. 5). Indeed, the availability of EQ-5D values which are suitable for this purpose has led to the EQ-5D being the most widely recommended questionnaire for use in the cost effectiveness evidence submitted to Health Technology Appraisal (HTA) bodies. The EQ-5D is recommended in 85% of HTA guidelines (Kennedy-Martin et al. 2020), including those of the UK's National Institute for Health and Care Excellence (NICE 2013).

The EuroQol Group was and continues to be a pioneer in the development of local/national value sets. The development of EQ-5D-3L value sets was, as an international research effort, unparalleled in the availability of country-specifc values (Szende et al. 2007). There are currently EQ-5D-3L value sets available for 35 countries and, for the EQ-5D-5L, 25 countries, with still further value set studies underway or planned. Both the EQ-5D-3L and the EQ-5D-5L, and the value sets which accompany them, continue to be used next to each other in many countries. The value sets facilitated the use of data from EQ-5D instruments in the estimation of QALYs based on local preferences, as well as in other, 'non-economic' applications where EQ-5D profle data are summarised in a way that refects the relative importance of the different dimensions.

# **1.2 The Development of the EQ-5D-5L**

In 2005 the EuroQol Group initiated efforts to develop an expanded-level version of the EQ-5D-3L. This was motivated by concerns by some stakeholders about limitations of the original instrument, particularly ceiling effects and changes in health that were too small to be detected by the three-level version. Studies which had been undertaken by EuroQol Group members prior to 2005 had shown that various experimental fve-level versions of EQ-5D could reduce ceiling effects while at the same increasing reliability and sensitivity (discriminatory power) and maintaining feasibility (Janssen et al. 2008a, b; Pickard et al. 2007a, b).

The development and testing of the EQ-5D-5L is reported in Herdman et al. (2011). A decision was made early in the new instrument's development to retain the same fve dimensions as the EQ-5D-3L, but to expand the number of response levels. This could in principle have been achieved simply by adding two 'unlabelled' intermediate levels between the existing three. However, in order to arrive at values for the EQ-5D-5L profles, each health state to be evaluated by respondents needed to be capable of being described by fve sentences. This in turn required a label for each level. An example of an EQ-5D-5L health state, displayed in the manner it might be presented in a stated preference task, is shown in Fig. 1.1. The state described in Fig. 1.1 is the same combination of levels and dimensions as the example in Box 1.1 i.e., it is EQ-5D-5L profle 21325.

Herdman et al. (2011) describe the process by which these labels were established, using both English and Spanish as root languages in order to support further translation and adaptation of the new instrument. Severity labels for 5 levels in each dimension were identifed using response scaling. Selecting labels at approximately the 25th, 50th, and 75th centiles produced two alternative 5-level versions. Focus groups were used to investigate the face and content validity of the two versions, including hypothetical health states generated from those versions. This showed evidence in favour of the wording 'slight-moderate-severe' problems, with level one described as 'no problems' in each dimension, and level fve being 'unable to' in the EQ-5D functional dimensions (mobility, self-care, usual activities) and 'extreme problems' in the pain/discomfort and anxiety/depression dimensions.

**Fig. 1.1** An example of an EQ-5D-5L health 'state' described by fve sentences

The fnal version of the fve-level instrument which emerged from this work is described in the EQ-5D-5L User Guide (EuroQol Group 2019). Beside the increased number of levels of the dimensions, the 5-level version of the EQ-5D has other notable features which represent improvements on the EQ-5D-3L. Most importantly, the wording of the mobility dimension is improved: the most severe level of the mobility dimension of the EQ-5D-3L is 'confned to bed', which means that it cannot capture severe problems with mobility that do not involve being confned to bed. This acts to limit its usefulness both in detecting problems with mobility and in capturing improvements in mobility resulting from treatment (Oppe et al. 2011). In the EQ-5D-5L, the most severe level of mobility has been changed to 'unable to walk about'.

These improvements have yielded a number of advantages for the EQ-5D-5L over the EQ-5D-3L. These are summarised by Devlin et al. (2018) and include:

Overall, the evidence suggests that the EQ-5D-5L retains the principal benefts of EQ-5D-3L—its brevity and validity in a wide range of conditions—and produces a more accurate measurement of patient health than the EQ-5D-3L (Devlin et al. 2018). These advantages have been recognised by users and use of the EQ-5D-5L has rapidly increased. There are now more than 130 language versions of the EQ-5D-5L available.

# **1.3 The need for EQ-5D-5L Values**

The availability of the EQ-5D-5L, and the supporting evidence of its improved measurement system, generated a demand for values to accompany it, to allow use of its data in the estimation of QALYs and any other applications where EQ-5D-5L profle data need to be summarised by a preference-weighted single number.

In anticipation of the need to provide EQ-5D-5L value sets, the EuroQol Group initiated an ambitious programme of methodological research, running in parallel with the development of the EQ-5D-5L instrument, and aimed at producing an internationally standardised state-of-the-art valuation protocol. This was timely, as most of the EQ-5D-3L value sets were based on the so called 'MVH-protocol' developed in the early 1990s (Dolan 1997). There was a lack of consistency in the design and implementation of that protocol between value sets studies. Furthermore, limitations of the MVH protocol had been recognised, suggesting improved methods were required for valuation of the EQ-5D-5L.

The aim was therefore not just to improve on the instrument, but to also ensure that the valuation of EQ-5D-5L profles would be based on the best possible stated preference methods – and to provide a well-described, standard valuation study protocol which could be felded in a consistent way across different countries. This would ensure that the value sets generated for the new instrument would, as far as possible, be comparable across countries. That is, that differences between the EQ-5D-5L value sets which are observed would refect the local variations in preferences and opinions which they are intended to capture, rather than being confounded by differences in methods.

As it was anticipated that value sets would take several years to be developed and disseminated, an interim solution was to map EQ-5D-5L data to the EQ-5D-3L instrument by linking descriptive systems, and to use the value sets that already existed for the EQ-5D-3L (van Hout et al. 2011) (further explanation of mapping is provided in Chap. 5). While this provided a practical stop-gap means of summarising EQ-5D-5L data, these mapped values were recognised to be suitable only as a temporary solution as these indirect methods introduce additional error variance, and would still rely upon old and non-standardised value sets. Further, one might question whether values sets for the EQ-5D-3L, developed in the 1990s, would be an adequate representation of the average preferences of today's societies. There are numerous reasons to consider the need to update value sets, including changes in the underlying preferences of populations, improvements in the methods available to value health; changes in the distribution of population demographics; and concerns about potential bias in previous studies (Pickard 2015) – these issues are discussed further in Chap. 7.

In order to arrive at an improved and standardised valuation protocol, the EuroQol Group therefore commissioned a substantial programme of research to develop and test methods suitable for creating new value sets for the EQ-5D-5L that was initiated while the descriptive system was under development. The program of research – which is detailed in Chap. 2 – was started with the intention of providing investigators around the world with the tools to conduct a valuation study that would follow a standardised protocol and produce high quality data based on validated methods that supported comparisons between countries. These efforts culminated in an international protocol for conducting EQ-5D-5L valuation studies, which has been used to produce the 25 country-specifc value sets which are summarised in Chap. 4 of this book (Fig. 1.2).

This endeavour is unique in scale and ambition in the feld of HRQoL valuation and represents a signifcant body of work with direct relevance to decision makers and impact on health care policy internationally.

# **1.4 The Aims of this Book**

The book draws together and summarises, for the frst time, the body of evidence on EQ-5D-5L value sets that has been produced internationally from the EuroQol Group's programme of research and protocol development.

The primary aim of the book is to provide an accessible source of information and guidance to support users of EQ-5D-5L and its value sets. Specifcally, we aim to improve users' understanding of how value sets are generated; raise awareness of the characteristics and properties of value sets; and inform users' choice of which value set to select for particular application, and how that choice may affect their analysis and conclusions. Moreover, the book will also be useful to health economics and outcomes researchers specialising in HRQoL who want to obtain information on the research practises and protocols developed by the EuroQol Group to support EQ-5D-5L valuation.

We begin in Chap. 2 by detailing the process of developing the research protocol underpinning EQ-5D-5L valuation studies. This included a methodological programme of work and international pilot testing; development of a protocol; the frst wave of studies and the conclusions drawn from those early studies; modifcation and strengthening of the protocol and quality assurance processes; and use of the revised protocol in subsequent waves of value set studies. The chapter indicates the considerable learning and progress that was made through this journey of designing and refning the protocol.

Chapter 3 sets out the various aspects of the study design and the basis on which methodological choices were made with respect to the stated preference methods to use; the sub-set of states to value using these methods; and minimum sample size needed.

Chapter 4 provides a reference source and 'thumbnail overview' of the characteristics of the value set in each of 25 countries. In each case, we provide a summary of the value set itself and its characteristics, a worked example of the calculation of values from it; information on the sample from which values were obtained; the methods used in analysing the data and modelling the value set; and the uptake by local HTA bodies and other health care decision makers.

Chapter 5 provides guidance to those who have collected EQ-5D-5L data and want to know how to choose between the value sets reported in Chap. 4. This includes consideration of the purpose of using value sets, how to proceed when there is no value set for a specifc country of interest or where there is more than one value set for a given country; and when it is appropriate to use mapping to obtain EQ-5D-5L values.

Chapter 6 draws together the value sets summarised in Chap. 4, and compares and contrasts their characteristics, reporting original comparative analysis undertaken specifcally for this book. To what extent are there similarities between EQ-5D-5L value sets across countries – and are there important differences between them? Our intention in Chap. 6 is to encourage users to be aware of the specifc properties of the value sets they select to use.

We conclude in Chap. 7 by refecting on the value sets produced to date and considering a number of questions about future directions for this body of work. For example, what is the 'shelf-life' of a value set – and what factors should prompt an update, in order to ensure that value sets represent an adequate representation of the average preferences of a society? What methodological questions remain – and how are improvements or variations in methods reconciled with the need for consistency in the evidence presented to HTA bodies and other users?

The book includes a glossary of terms for those unfamiliar with the EQ-5D and the valuation of the EQ-5D-5L.

The stated vision of the EuroQol Group is the aim "to improve decisions about health and health care throughout the world by developing, promoting and supporting the use of instruments with the widest possible applicability for the measurement and valuation of health" (EuroQol Group 2021). We hope this book contributes to that aim, and that it supports your use of EQ-5D-5L to provide evidence for better health care decision making.

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Chapter 2 The Development and Strengthening of Methods for Valuing EQ-5D-5L – An Overview**

**Elly Stolk, Juan Manuel Ramos-Goñi, Kristina Ludwig, Mark Oppe, and Richard Norman**

**Abstract** The introduction of the EQ-5D-5L offered an opportunity to develop a standardised valuation protocol, the EQ-VT protocol, with improved methods for health state valuation that enables comparison of the resulting value sets between countries. This chapter summarises the process of developing and strengthening the methods for valuing EQ-5D-5L in the EQ-VT protocol which underpins the valuation studies reported in this book. This includes an overview of the methodological research programme that informed the initial EQ-VT protocol and a description of the key elements of the protocol and the included valuation techniques, i.e. composite time trade-off and discrete choice experiments. This chapter also discusses the frst wave of EQ-5D-5L valuation studies which used the protocol and the resulting conclusions; the subsequent modifcation and strengthening of the EQ-VT protocol including a quality control procedure; and experience with use of the improved EQ-VT protocol in the subsequent waves of EQ-5D-5L valuation studies. The chapter concludes with an overview of the lessons learned during this journey of evidence-based refnement of the EQ-VT protocol from version 1.0 to the current version 2.1.

E. Stolk

EuroQol Research Foundation, Rotterdam, The Netherlands

J. M. Ramos-Goñi · M. Oppe Maths In Health B.V., Rotterdam, The Netherlands

K. Ludwig (\*)

EuroQol Research Foundation, Rotterdam, The Netherlands e-mail<EMAIL_ADDRESS>
Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany

R. Norman School of Population Health, Curtin University, Bentley, Australia

# **2.1 Development of the EQ-VT Protocol**

Over the past 25 years, approaches taken to the valuation of EQ-5D-3L have not changed much from those used in Dolan (1997). While issues had been noted in regard to valuing the EQ-5D-3L, the desire to produce new EQ-5D-3L value sets using the same approaches as previously lessened the impetus for change. The introduction of the EQ-5D-5L offered an opportunity to explore how methods for health state valuation could be improved to produce an updated valuation protocol (Oppe et al. 2014). To arrive at a protocol that could be supported broadly, the initial development – and later refnement – of that protocol coincided with an extensive programme of methodological research within the EuroQol Group. This chapter summarises the research that was undertaken, the results that underpinned the initial version of the EQ-5D-5L valuation protocol and later modifcations, and the main lessons learned from the international EQ-5D-5L valuation work.

While the research programme had a broader scope, the focus was on two different methods to elicit preferences for health states, time trade-off (TTO) and discrete choice experiments (DCEs). TTO had emerged as the frst method of choice in earlier valuation studies, and the introduction of the EQ-5D-5L did not change that. Yet, concerns had been expressed about extremely low values that could be produced for states worse than dead (WTD), requiring arbitrary rescaling (Janssen et al. 2013) and therefore refnement of the TTO method was pursued within the research programme. Lead time TTO (LT-TTO) had been identifed as a possible TTO approach that could mitigate issues valuing states WTD (Robinson and Spencer 2006; Tilling et al. 2010; Devlin et al. 2011) and therefore the relative merits of that approach were explored (Attema and Versteegh 2013; Devlin et al. 2013; Versteegh et al. 2013). DCE was at that time recognised as a promising new method for health state valuation (e.g. Salomon 2003; McCabe et al. 2006), and having become more widely used in other aspects of health economics (Ryan 2004). DCE was, therefore the second focus of the research programme and was studied both as a potential alternative to TTO and as a complement. DCE has the beneft of having a generally simpler task compared with TTO, requiring simple choices rather than completion of an iterative process, with potential signifcant benefts for data collection. Questions around the way to collect and model DCE-data therefore also were addressed. Based on the desire to replace the props used in TTO interviews (e.g. TTO board) by computers and develop a computerised TTO procedure, all tasks were integrated into a digital aid (the EuroQol-Valuation Technology, EQ-VT), which was developed in conjunction with the protocol. As a result, the protocol is commonly referred to as the EQ-VT protocol.

We will not cover all fndings of the research programme in this chapter. However, several fndings require particular highlighting:

1. while the conducted research on LT-TTO produced ample proof of concept for the use of LT-TTO in health state valuation, values for states better than dead (BTD) seemed to be subject to a downward bias. Therefore, composite TTO (cTTO) was introduced (Janssen et al. 2013), which uses conventional TTO for the valuation of states BTD, and LT-TTO for states WTD;

Further results obtained in the methodological research programme have been documented in 19 journal articles. Oppe et al. (2014) described how those results supported the development of the EQ-VT protocol version 1.0.

# **2.2 Description of the EQ-VT Protocol**

# *2.2.1 Contents of the Protocol*

From its origins in 2012, some elements of the EQ-VT protocol have evolved but the overall structure has been retained, comprising the following six parts:

After a general welcome and explanation of the purpose of the study, selfreported health as measured by the EQ-5D-5L including EQ VAS and background questions regarding age, gender and experience with illness are asked. The third section then introduces respondents to the cTTO valuation tasks (see Figs. 2.1a and 2.1b). The interviewer uses the example health state "being in a wheelchair" to explain how to interpret and carry out the cTTO tasks. After cTTO task understanding is confrmed, respondents move on to value ten EQ-5D-5L health states and answer three debriefng questions regarding the cTTO tasks. In the next part of the interview, the interviewer explains how to carry out the DCE. The respondents are asked to complete seven forced-choice paired comparisons of EQ-5D-5L health states without a "duration" attribute (see Fig. 2.2), meaning the choice is simply between two EQ-5D-5L health states independent of time. Following this, respondents answer three debriefng questions regarding the DCE tasks. In the concluding part of the interview, the respondents can leave feedback and are thanked for their participation.

**Fig. 2.1a** Presentation of the composite time trade-off used in the EQ-VT protocol: better than dead task. (© 2021 EuroQol Research Foundation. Reprinted with permission)

**Fig. 2.1b** Presentation of the composite time trade-off used in the EQ-VT protocol: worse than dead task. (© 2021 EuroQol Research Foundation. Reprinted with permission)

The cTTO approach begins with the 'conventional' TTO with the frst question being ten years in the health state being valued versus ten years in full health (see Fig. 2.1a), and only shifts to an LT-TTO when the respondent considers the health state to be WTD. In that case, the following LT-TTO task involves a twenty-year time frame: ten years of lead time followed by ten years in the EQ-5D-5L health

**Fig. 2.2** Presentation of a discrete choice experiment task used in the EQ-VT protocol. (© 2021 EuroQol Research Foundation. Reprinted with permission)

state being valued (see Fig. 2.1b). The resulting cTTO values range from −1 (trading all of the lead time) to 1 (trading no years in full health) in 0.05 increments. The exact iteration scheme is reported elsewhere (Oppe et al. 2016). The underlying experimental design including the health state selection for both the cTTO and DCE tasks, and other study design considerations such as sample size requirements, is addressed in Chap. 3.

To ensure that respondents can give valid and meaningful responses during the cTTO task, they frst get the opportunity to experience the cTTO task by completing the wheelchair example and are made aware that they will be asked to evaluate a set of other health states in the same way. After that – still in the wheelchair example – they learn, amongst other things, how their responses will be interpreted, what the range of possible answers is, how the task proceeds in a slightly different way when a state WTD is encountered, and how they need to interpret the health states. Delivering these instructions is challenging for the interviewer, as most are not prompted on screen, the task is diffcult for some respondents, and the interview needs to be completed in a standardised and neutral way. Furthermore, a high level of task engagement is expected from the respondent, and this depends on the level of engagement demonstrated by the interviewer and the quality of interactions with the respondent. Since the wheelchair example is the point in the interview where all of this needs to be discussed, this section is the key to the successful implementation of the EQ-VT protocol; especially the cTTO part.

# *2.2.2 Why the cTTO Task was Adopted*

Concern with the way in which values for states WTD were produced in EQ-5D-3L value sets motivated much of the research carried out to develop the new protocol. It is well known that a standard TTO task, by contrasting a ten-year life in a disease state to a shorter life of t years in full health, can only produce positive values. In this task the value x of the disease state is given by t/10 at the point where the respondent is indifferent between the options. Since lifespans cannot have a negative value, t cannot be negative and so only values for x in the [0, 1] range can be observed. If respondents indicated that they preferred immediate death over living for ten years in the disease state, a modifed task was offered inviting the respondents to compare a health profle including t years in full health followed by 10−t years in ill health to immediate death. Here the value x of health is given by −t/(10−t). As the difference between t and 10 can be made infnitely small (e.g. counted in years, months, weeks, days or smaller units), the value of this negative ratio statistic can become extremely large. To counteract the effect on mean values, an arbitrary transformation was applied to bound the negative values at −1. Various options to transform the data have been proposed, however, the choice between them remained arbitrary but could affect the results substantially.

LT-TTO offers – in theory – a unifed approach for the valuation of states BTD and WTD. As in standard TTO, respondents consider how good or bad it would be to spend ten years in a state of impaired health. However, the period of impaired health does not start now, but starts ten years from now so that the total remaining lifespan is 20 years. This is compared to a life that has t years in full health and the duration t is varied between 0 and 20 to identify indifference. The value x of the disease state can be computed by solving 10+10x=t which gives a positive value for all t>10 and a negative value for all t<10.1 However, due to presence of a bias, described below, the cTTO approach was preferred over LT-TTO.

Regarding LT-TTO, larger lead times *ceteris paribus* extend the range of negative values that can be observed, but higher bounds on the maximum hypothetical lifespan and lower bounds on the size of the trade-off unit also need to be considered. Key fndings were that values for states BTD seemed to be affected by a downward bias in the LT-TTO task, and larger ratios of lead time to disease time amplifed this problem. A possible explanation is that respondents considered what portion of their remaining years to trade off without recognising that trading into the lead time implied a WTD response. Therefore, the decision was made to use standard TTO in the valuation of states BTD and only adopt LT-TTO for the valuation of WTD states. Consistent with previous valuation studies, the standard TTO was again specifed with a ten-year time frame and in the LT-TTO frame a lead time of ten years was offered to even out the changes in value associated with the trade-off unit (years) in the BTD and WTD size of the scale. The name 'composite' TTO was adopted as the name for the TTO protocol adopting standard TTO for the valuation of states BTD and lead time TTO for states WTD.

<sup>1</sup>For example: if t=16, the formula 10+10x=t will read 10+10x=16, thus 10x=6, which solves to x=0.6. If t=6, the same formula reads 10+10x=6, thus 10x=−4, and x=−0.4.

# *2.2.3 Why the DCE Task was Adopted*

In most EQ-5D-3L valuation studies, respondents received multiple valuation tasks, of increasing complexity and from the start it was assumed that the EQ-VT protocol would also include at least two types of stated preference tasks. But which tasks?

The non-standardised protocols for EQ-5D-3L valuation (see Chap. 1) supplied researchers with rank, VAS (visual analogue scale) valuation and TTO responses. At the discretion of the study teams, the collected data were used in various ways. In the early years, both VAS- and TTO-based value sets were developed while the ranking task was seen as a useful precursor. Gradually, however, the views on these methods started to shift. TTO became the method of frst choice and the use of VAS valuation started to decline. Alternatives to VAS valuation were considered for the EQ-VT protocol. At the same time, the underused potential of rank data started to be recognised (e.g. Salomon 2003; Craig and Busschbach 2009; Craig et al. 2009). In the EQ-VT protocol, the ranking and VAS valuation tasks eventually were displaced by DCE. This method is akin to a ranking task.

There were several reasons to choose DCE. For example, one reason for including DCE was because of the different nature of the instrument being valued, i.e., the EQ-5D-5L, rather than the EQ-5D-3L. The subtler differences between levels – especially at the mild end of the descriptive system – meant some people might not be willing to trade off any life years; whereas the DCE could still obtain preferences between mild states. Furthermore, DCEs were widely recognised as a promising new method for valuing health and shown to be feasible for EQ-5D (Stolk et al. 2010). Lastly, a DCE task can be set up in different ways and depending on the chosen confguration, it can produce values (a) on a latent scale or (b) values directly anchored on the QALY (Quality Adjusted Life Year) scale if either the attribute "duration" or the alternative "dead" is included in the DCE (Norman et al. 2016). In the latter case, DCEs yield values that can have the same cardinal measurement properties as TTO, but with a more straightforward and less costly data collection process. Anticipating future developments, it was also considered important to include DCE (instead of VAS or rank) now, to familiarise more researchers with the DCE method and promote learning.

The DCE task included in the EQ-5D-5L was a basic one, comparing two EQ-5D-5L health states without reference to lifespan, i.e., number of years lived in each state. Methodological research that guided this decision had suggested that this basic approach produces robust results, whereas the approaches that could produce values on the QALY scale initially suffered from unexplained high variability in the results, and researchers had different ideas about how to make these advanced tasks work. Therefore, it seemed unwise to push for a harmonised method when the protocol for EQ-5D-5L valuation was introduced. However, it was agreed to continue research about other DCE approaches and see if issues with those approaches could be resolved (for further discussion on this, see Chap. 7).

# *2.2.4 Value Set Generation*

Subsequent to completion of the EQ-VT data collection protocol, value set generation can be based on either hybrid models that draw on both types of data at the same time (i.e., cTTO and DCE), or on cTTO data only. The DCE data cannot be used independently as a basis for value set generation because DCE values are estimated on a latent scale and lack the interpretation of health state values that are anchored at 0 (dead) and 1 (full health). The option to generate a value set based on two types of data has the beneft of providing extra assurance about the ability to construct a value set based on data collected in the valuation study.

While cTTO and DCE results provide two measures of the same construct, preferences for health, perfect agreement of cTTO and DCE results is not to be expected due to the differences between the methods:

Choices between methods for value set generation must refect judgments about the relative merits of each method, given theoretical considerations and/or the properties of the empirical data. If the two data sources agree, that could be an argument to include all data deriving a value set with greater precision. Conversely, if there are discrepancies, it might be questioned which is the "correct" one and it might be considered problematic to combine the two data sources. However, the latter might be considered a fallacious argument, because there exists no gold standard against which the values derived from cTTO or DCE can be judged. Discrepancies therefore can also be looked at as providing complementary information.

As neither line of reasoning will be universally accepted, the EQ-VT protocol sets the frame for eliciting health state preferences and the local research team makes the decision about the way of value set generation (e.g. type of data included and modelling).

# **2.3 How the EQ-VT Protocol Updates Evolved**

After the frst wave of EQ-5D-5L valuation studies (Canada, China, England, Netherlands, and Spain) were completed using the new EQ-VT protocol, it became apparent that there was scope to improve on the frst version of the protocol, especially by strengthening it on the implementation side. In some of those initial studies, issues with the cTTO data were observed such as strong clustering effects, limited coverage of the value range, and high number of inconsistencies.2 The data issues seemed to refect low levels of task engagement of the respondents and/or the interviewers, leading to detrimental effects on quality of cTTO valuations. It was recognised that these issues clustered in interviewers and were not universally present, leading to the hypothesis that the data issues represented interviewer effects. This motivated the development and integration of a procedure that would allow the data to be monitored in real time to detect the presence of any issue and to enable timely interventions: a quality control (QC) procedure (Ramos-Goñi et al. 2017). In addition, the introduction of three practice cTTO tasks following the wheelchair example and the inclusion of confrmatory pop-ups for each cTTO task to validate answers before storing led to EQ-VT version 1.1 (see Table 2.1).

In addition, a comprehensive EQ-VT research programme was launched to test a range of suggestions for strategies that could help to prevent the data quality issues and interviewer effects from occurring. Shah et al. (2015) described the studies (N=7) that were done aimed at remedying cTTO data issues and improving EQ-VT. All studies were set up as experiments with at least two arms, allowing results obtained from a modifed version of the protocol (experimental arm) to be compared to the EQ-VT version 1.1 (control arm). The battery of tests included:

**Table 2.1** Overview of EQ-VT elements by protocol versions

Note: The cross mark shows that an element was included in the protocol version

*cTTO* composite time trade-off, *DCE* discrete choice experiment, *EQ-VT* EuroQol-Valuation Technology, *QC* quality control

a Sometimes used as optional element

<sup>2</sup>There were spikes (i.e., clustering of values at −1, −0.5, 0, 0.5, 1), lower than expected values for mild health states (i.e., a big gap to 1) and a low number of negative values (i.e., few WTD values). Moreover, there was a high number of inconsistencies overall and with regard to the worst possible health state 55555 (i.e., valuing less severe health states lower than the value for 55555).

The collected data provided strong support for integration of a QC module, as it improved data quality markedly. It also supported implementation of the feedback module (see Fig. 2.3), since respondents frequently appreciated having the option to review and reconsider their own responses if needed. The other tested modifcations did not produce clear benefts (Shah et al. 2015). Interviewer effects, clustering of cTTO values, and inconsistencies were strongly reduced in valuation studies that applied the updated EQ-VT protocol (Ramos-Goñi et al. 2017; Stolk et al. 2019). Guided by results obtained in this work, the EQ-VT has received two updates in 2013 (EQ-VT 1.1) and 2014 (EQ-VT 2.0). Later on, in 2017 one more update was implemented (EQ-VT 2.1), which altered the fow of the wheelchair example to include more prompts for interviewers (Stolk et al. 2019). Box 2.1 provides further details on the QC procedure, as implemented from protocol version 1.1 onwards.

**Fig. 2.3** Example of the feedback module used in the EQ-VT protocol since version 2.0. (© 2021 EuroQol Research Foundation. Reprinted with permission)

#### **Box 2.1: QC Procedure Since EQ-VT Protocol 1.1**

A QC procedure was introduced to monitor the interviewer's protocol compliance and interviewer effects as well as the face validity of the data. By looking at four QC criteria it is possible to determine whether an individual interview is of "suspect" quality. If any of the four following criteria is met for an individual interview, it is fagged:

Initial QC reports are used to evaluate whether interviewers met the minimum quality requirements. If 40% out of the ten interviews are fagged as being of *suspect* quality by using the QC tool provided by the EuroQol Executive Offce, all interviews thus far conducted by that interviewer will be removed and the interviewer will be retrained. After further ten interviews, interviewer's performance and compliance are re-evaluated. If again 40% or more interviews are fagged, these interviews will also be removed and the interviewer is removed from the interviewer team. A threshold value of 40% was selected because fagged interviews could hold genuine responses (e.g., respondents who quickly build their opinion and perform the cTTO tasks). Additionally, this allows interviewers to grow into their roles when they built up experience with valuation interviews following the EQ-VT protocol.

During the entire study, the local study team continuously monitors data quality. Later QC reports allow to refect on interviewers' performance, discuss possible improvements and intervene when the performance of an interviewer worsens.

# **2.4 Lessons Learned**

The evidence from the valuation studies and the comprehensive EQ-VT research programme (Shah et al. 2015) led to increased awareness of how challenging the interview is, both for the respondent and the interviewer. Data issues driven by interviewer effects showed that the interviewer and his/her skills are pivotal in the success of the interview, especially for the cTTO tasks. The amount of guidance to respondents affects their engagement and task understanding, and thereby accuracy and reliability of responses. The DCE task may be more robust to interviewer effects, but it may also be that data issues are simply relatively more transparent in cTTO data. While technical aspects of the tasks were the key focus before the frst valuation studies were launched, focus shifted to the human interaction side of the task after the frst wave, which is equally important and clearly needed more attention.

The changes made to the protocol can be categorised into (a) monitoring of and providing support to the interviewers and (b) supporting the respondents. As outlined above, the introduction of the QC procedure since version 1.1 with accompanying QC tool enables monitoring of the protocol compliance of the *interviewers* and interviewer effects. Moreover, it facilitates the support of the interviewer by providing data-based feedback. The items measured are reported elsewhere (Ramos-Goñi et al. 2017). In order to support the *respondents*, it was realised that an extended introduction and practising cTTO is necessary before the valuation tasks can be carried out in the intended way: three additional practice states and a dynamic question after the wheelchair example were added as EQ-VT elements. Depending on the respondent's response for the wheelchair task, in the dynamic question the respondent is asked to imagine a health state that is much better or much worse than being in a wheelchair in order to move to the other part of the evaluation space in the cTTO. Moreover, as mistakes and/or learning effects can still occur, confrmatory pop-ups after each task and the feedback module presented in Fig. 2.3 were additionally included. The latter presents respondents with the rank ordering implied by their cTTO valuations and provide the opportunity to fag problematic valuations for removal from the data. Further details on the EQ-VT elements and its changes are provided elsewhere (Stolk et al. 2019).

To prepare interviewers for their role in the execution of a study, the EuroQol Executive Offce started to work more closely with study teams. Besides making available an interviewer script and EQ-VT software tailored to the needs of each team, the EuroQol Executive Offce now also offers training for the local research team, who in turn train their interviewers (a 'train the trainer' approach). While this training helps, due to the complexity of the interview and because the topics taught are abstract until the interviewers start doing interviews, it will not prevent all issues. Learning on the job – as supported by the QC process – therefore has a big additional impact to promote performance of interviewers, using information on the behaviour of an interviewer to tailor and deliver a personalised set of additional instructions. The initial training addresses therefore a mix of topics related to content and process, to build up interviewer skills and to discuss collaboration when the study is ongoing.

Related topics that need consideration are the selection of interviewers, the logistics of data collection, and, more broadly, how investigators and interviewers can work together most effectively. This part of the study is not standardised, but the EuroQol Executive Offce can offer recommendations. To date, working with a small team of dedicated students, who travel together with a data coordinator throughout the country, and collect data in weekly round of ten interviews per

interviewer, serves as an example of good practice (e.g. Pickard et al. 2019; Shafe et al. 2019; Welie et al. 2020). Students have relevant background knowledge, are familiar with the concepts validity and bias, are keen to learn, want to do well, and do not mind having their performance assessed. Working as a group allows the data coordinator to deliver effective feedback, and individuals are likely to be receptive to it, since they see other interviewers work on similar issues. Undertaking the work as a group makes everyone more focused on the goal of the study.

# **2.5 Concluding Remarks**

Over the last ten years the accumulation of extensive multinational evidence supported the development and the subsequent refnement of a standardised EQ-VT protocol for conducting national EQ-5D-5L valuation studies. A multinational research programme examined alternative approaches for eliciting health state preferences, developed methods to improve data quality and demonstrated the robustness of these approaches across languages and countries.

The EQ-VT protocol was developed in a way that evidence-based refnements are anticipated. Across the different versions of the protocol, EQ-VT 1.0 to the current version 2.1, the valuation tasks have remained the same, but later versions pay more attention to the optimal implementation of these tasks combined with a QC procedure. The refnements of the EQ-VT protocol have been shown to improve data quality and minimize interviewer effects.

The EQ-VT protocol has to date successfully been applied in about 30 countries worldwide and, at time of writing, 25 of these have been published. These 25 value sets are summarised in Chap. 4, and their similarities and differences are described in Chap. 6. Even though the improved valuation protocol with its QC process provides a solid basis for estimating national EQ-5D-5L value sets, there remain methodological questions that can be addressed in future research (see Chap. 7). This might further improve the EQ-VT protocol.

# **References**

Devlin N, Buckingham K, Shah K, Tsuchiya A, Tilling C, Wilkinson G, van Hout B (2013) A comparison of alternative variants of the lead and lag time TTO. Health Econ 22(5):517–532

Dolan P (1997) Modeling valuations for EuroQol health states. Med Care 35(11):1095–1108

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Chapter 3 Experimental Design for the Valuation of the EQ-5D-5L**

#### **Mark Oppe, Richard Norman, Zhihao Yang, and Ben van Hout**

**Abstract** The EQ-VT protocol for valuing the EQ-5D-5L offered the opportunity to develop a standardised experimental design to elicit EQ-5D-5L values. This chapter sets out the various aspects of the EQ-VT design and the basis on which methodological choices were made in regard to the stated preference methods used, i.e., composite time trade-off (cTTO) and discrete choice experiments (DCE). These choices include the sub-set of EQ-5D-5L health states to value using these methods; the number of cTTO and DCE valuation tasks per respondent; the minimum sample size needed; and the randomisation schema. This chapter also summarises the research studies developing and testing alternative experimental designs aimed at generating a "Lite" version of the EQ-VT design. This "Lite" version aimed to reduce the number of health states in the design, and thus the sample size, to increase the feasibility of undertaking valuation studies in countries with limited resources or recruitment possibilities. Finally, this chapter outlines remaining methodological issues to be addressed in future research, focusing on refnement of current design strategies, and identifcation of new designs for novel valuation approaches.

M. Oppe (\*)

Z. Yang

B. van Hout School of Health and Related Research, University of Sheffeld, Sheffeld, UK

Pharmerit International, York, UK

Maths in Health B.V., Rotterdam, The Netherlands e-mail<EMAIL_ADDRESS>
R. Norman School of Population Health, Curtin University, Bentley, Australia

Health Services Management Department, Guizhou Medical University, Gui'an, People's Republic of China

# **3.1 Introduction**

As explained in Chap. 2, having decided that the protocol for valuing EQ-5D-5L would include both composite time trade-off (cTTO) and discrete choice experiment (DCE) as elicitation techniques, the next step was to provide a study design that would enable the researchers to identify a model that would appropriately predict values for all 3125 potential health states. For this, choices needed to be made about:

It was envisaged that not all respondents needed to value the same health states and that respondents could be randomised over different blocks of health states. The aim of this chapter is to describe the basis for the protocol designs and the factors that were considered in developing them. In addition, alternative designs and directions for future research with respect to designs will be addressed.

Valuation studies do not test hypotheses, and as such there is no classic power calculation as with randomised clinical trials. Generally, the more subjects and the more data per health state decreases the standard errors around the value for each health state, decreases the standard errors around the model estimates and one would expect it to decrease the probability of misspecifcation. A traditional method to test different designs is by simulating experiments (i.e., simulate respondents' answers to the tasks – informed by prior evidence on how people respond) and compare the simulated means with the means one would expect. The simulated experiments are analysed to determine whether the model that is being estimated corresponds with the model which underlies the simulations (the true model) and what the width is of the confdence intervals surrounding the estimates.

Within the above considerations it was also decided that both the cTTO task and the DCE task needed to be designed such that the data would allow for estimating separate models without the need for data from the other part of the study. This would leave room for the scientists conducting such studies to estimate models using only cTTO data, or only DCE data, or hybrid models combining the two sets of data. The EQ-VT designs were developed using a staged approach. Designs were created for the pilot studies that informed the development of the EQ-VT protocol. These pilot studies also informed refnements with respect to the experimental design.

As described in Chap. 1, for EQ-5D-3L valuation studies, the study protocols and experimental designs were not standardised, although most studies followed some or all of the protocol used in the frst time trade-off (TTO) study for EQ-5D-3L: the Measurement and Valuation of Health (MVH) study conducted in the United Kingdom (Dolan 1997). In the end, different countries produced EQ-5D-3L value sets based on different elicitation tasks. Some used a visual analogue scale (VAS) based elicitation technique, while others used TTO. In addition, different numbers of health states were used (e.g., 43 health states in total for MVH, while others used e.g., 17 states or 24 states, or a more saturated 196 health state approach), and a different selection of health states. These methodological differences between studies hampered comparison between countries: it is unknown to what extent differences in results obtained between countries were due to differences in the preferences of the study populations or due to differences in the study protocol and experimental design. Therefore, for the valuation of the EQ-5D-5L, the EuroQol Group decided to create a standardised study protocol including an experimental design (see Chap. 2 for more details on this standardisation).

# **3.2 EQ-VT Designs**

# *3.2.1 cTTO Design*

The states selected for the design of the cTTO need to be optimised for model estimation. This means the objective is to avoid introducing a bias in the model that originates from the selection of the health states included in the design. For example, if mild or moderate states are highly overrepresented in the design, this could lead to a bias in the model estimation. In addition, there should be enough states included, so that the model can be specifed. For example, since there are 20 main effect parameters (i.e., the four dummy parameters for each of the fve EQ-5D dimensions) the theoretical minimum number of health states to be included would be 21 (20 main effects +1 error term). For the main pilot study, also referred to as the core multinational pilot study (Oppe et al. 2014), the number of states that would be required for estimating an EQ-5D-5L value set was expected to be around 100. It was considered that a main effects model would have 21 parameters (5\*4 dummy variables for the main effects + intercept) leaving 79 degrees of freedom. Such a number of states would allow estimation of random coeffcient models, and inclusion of different kinds of interactions and/or the effects of background variables.

When regarding the number of observations per EQ-5D-5L health state included in the cTTO tasks, we found that in the cTTO pilot study with 121 observations per state, the standard errors for the severe states were around 0.056, while those for the mild states were around 0.01 (Janssen et al. 2013). This suggested we would achieve adequate average precision of the mean observed values with 100 observations per cTTO state. This was based on the assumption that with the standard errors at those levels, a repetition of the sample would result in observed mean values that would very likely fall within the bounds provided by these standard errors.

From the pilot studies, as well as the valuation studies undertaken for the EQ-5D-3L, it was clear that respondents would be able to complete at least 17 cTTO tasks each without negatively impacting on data quality (Tsuchiya et al. 2002; Lamers et al. 2006). However, since we also wanted to include a DCE task for the

same respondents, it was decided that we should limit the number of cTTO tasks per respondent to ten (excluding warm-up and practice tasks). In order to counteract biases due to framing effects, a blocked design was chosen to achieve a balanced mix of states with respect to where they are expected to lie on the overall value scale. That is to say, each respondent should complete a good balance of health states, covering the entire range from mild to severe. Therefore, each block was designed to include one of the fve very mild states (i.e., states 21111, 12111, 11211, 11121, 11112) and the worst state (i.e., state 55555, sometimes referred to as the "pits" state). It was decided to include ten blocks with two fxed states in each block such that eight states per block would need to be generated, i.e. 80 states. This implied that we would have (10\*8 + 5 + 1=) 86 states in total, which is a little less than in the main pilot study, but still more than four times the number of parameters for a typical main effects model.

Putting the above together, ten blocks of ten EQ-5D-5L states each, with 100 observations per block lead to a required sample size of 1000 respondents. This leaves the fnal part of the design for the cTTO part of the EQ-VT: selecting the set of 80 EQ-5D-5L states to be included.

We selected the 80 states from the total set of 3119 (i.e., the 3125 states in the EQ-5D-5L, minus the six states that were already included in the design, namely the fve mildest states and the "pits" state) using Monte Carlo simulation (see Box 3.1). First, values for all 3125 states for a sample of n=1000 respondents were simulated using a simulation programme implemented in R. Details of the simulation programme can be found in (Oppe and van Hout 2010). For the simulation as well as the optimisation algorithm, a main effects model (without constant) was used. This was decided based on the pilot studies and on two previous studies using the EQ-5D-3L. In the frst EQ-5D-3L study, OLS models including main effects and the N3 term (an interaction parameter which takes the value of 1 if any dimension is at level 3, or 0 otherwise) were estimated on the full data set of the MVH study, which resulted in an adjusted R2 of 0.43, and on a data set that included only the mean observed values of the 42 states included in the MVH study (thereby removing the within state variance), which resulted in an adjusted R2 of 0.97 (Oppe et al. 2013). These results indicate that the main contributor to the uncertainty is the within state variance, not the between state variance; that there is very little to gain by adding interaction terms (i.e., R2 can only increase marginally from 0.97); that you run the risk of overftting if interactions are added. The EQ-5D-5L pilot valuation studies showed that interactions similar to N3 or D1 (a parameter which corresponds to the number of impaired dimensions beyond the frst) from the EQ-5D-3L models did not improve the EQ-5D-5L models. Lastly, in a DCE study for EQ-5D-3L using a design optimised for main effects plus all two-way interactions the (pseudo) R2 increased from 0.266 for main effects to 0.277 for a model including interactions. In total there were 12 model parameters, but three of the main effects were no longer included (Stolk et al. 2010). Therefore, it becomes an issue of parsimony: Is adding interactions – consequently making the model less interpretable – worth a small increase in model ft?

A random design of 80 states was generated from the simulated data. An OLS main effects regression model (without constant) was estimated on the simulated set of cTTO data comprising the 80 states and 1000 respondents. Next, the sum of the mean squared errors (MSE) was calculated between the parameters that were used to create the simulated preference data and the parameters resulting from the OLS model. The difference between perfect level balance and achieved level balance of the 80 generated states was also calculated. The construction of the level balance criterion can be found in Appendix A. The regression procedure was repeated 10,000 times and an iterative procedure was used where designs that had either worse level balance or worse MSE were discarded.

The "optimal" set of 80 states was divided over the ten blocks using the blocking algorithm included in the "AlgDesign" package in R (Wheeler 2004). The blocking algorithm divides the states over the blocks in such a way that the within block variance is maximised (i.e., the full severity range is more or less covered within a block), while the between block variance is minimised (i.e., all blocks are more or less the same with respect to the mean severity per block).

In summary, the design of the cTTO experiment consists of 86 states divided over ten blocks with 100 observations per block, leading to about 10,000 observations in total, where the fve very mild states and state 55555 were oversampled compared to the other 80 states. For a main effects model this means that there will be about 400 observations per model parameter (8000 observations/20 parameters). The required sample size was determined to be 1000 (i.e., 10 blocks \* 100 observations per block). The 86 states of the cTTO design can be found in Appendix B.

# *3.2.2 DCE Design*

A Bayesian effcient design algorithm was used to select the pairs for the DCE. The priors were based on the results of a main effects model (without intercept) estimated on the data of an EQ-5D-3L DCE study (Stolk et al. 2010). We assumed that the levels 1, 2 and 3 from the EQ-5D-3L study corresponded to the levels 1, 3 and 5 for the EQ-5D-5L, while the levels 2 and 4 were assumed to be the mid-points between the levels 1 and 3, and 3 and 5 respectively. The standard errors of the parameters of the model we estimated on the EQ-5D-3L DCE data varied between 0.06 and 0.08. Conservatively, these were increased to 0.10 for the priors. The priors that were used can be found in Table 3.1.

Similar to the cTTO design there was an interest in making sure that at least some pairs of health states containing only mild states would be included in the DCE design. Therefore, ten such pairs were created manually. Pilot studies showed that the sample size of 1000 respondents determined for the cTTO would also be suffcient for estimating a DCE model (Krabbe et al. 2014; Oppe et al. 2014). In order to put limits on respondent burden, the number of DCE pairs per respondent was set to seven. The minimum number of observations needed per pair was deemed to be 35. This was based on being slightly more conservative than Hensher and colleagues, who refer to a minimum of 30 responses per set, based on the law of large numbers as stated by Bernoulli (Hensher et al. 2005). Putting these numbers together, a 196 pair design divided over 28 blocks of seven pairs was created using a Bayesian D-effcient design algorithm (see Box 3.2).

First, the set of ten mild pairs was manually selected. Next, a set of 186 pairs was randomly generated. For this set of 186 pairs, the Bayesian D-error of the design was determined using 1000 randomly drawn sets of priors. This process was repeated 10,000 times and the 186 pair design with the best D-error was kept. The ten mild pairs were added to this design, and the total set of 196 pairs was then blocked into 28 blocks of seven pairs each.

The Bayesian D-effcient design algorithm was implemented in R and we used the blocking algorithm included in the "AlgDesign" package in R (Wheeler 2004).

In summary, the DCE designs consists of 196 pairs divided over 28 blocks of seven pairs each. With the same sample size as the cTTO, this leads to a total of 7000 observations, meaning about 350 observations per parameter for a main effects model. The 196 pair DCE design can be found in Appendix C.

**Table 3.1** Priors used for the DCE design

Reproduced from Oppe and van Hout (2017)

**Box 3.2: Algorithm for Selection of DCE Pairs of Health States Used for the EQ-VT Design**

# *3.2.3 Other Considerations*

Apart from the sample size, and the selection of the set of health states for the cTTO and pairs for the DCE, another consideration for the experimental design of the EQ-VT was the randomisation schema needed. This is important, as a proper randomisation schema can counteract potential biases. For the cTTO, each respondent is randomly allocated one block of ten health states. The order in which the ten health states appear for each respondent is also randomised. For the DCE, each respondent is randomly assigned to one of the 28 blocks of pairs. The order of appearance of the seven pairs allocated to each respondent is also randomised, and for each pair of health states the order of appearance on the screen of the two health states comprising a pair (i.e., left versus right) is randomised. The order of appearance of the dimensions was not randomised, because the EQ-5D-5L instrument itself has a fxed order of appearance with respect to the dimensions (see Chap. 1).

# **3.3 Alternative cTTO Designs**

As noted above, the cTTO design of the EQ-VT protocol includes the selection of 86 different EQ-5D-5L health states and a minimal sample size of 1000. While this sample size is considered suffcient and achievable for most countries, reducing the number of states in the design, and thus the sample size, has the appeal that it could increase the feasibility of a valuation study in countries with limited resources or diffculty recruiting such a large number of respondents.

An important criterion for using a small design is that the accuracy of the estimated health state values should not be compromised (i.e., bias should be minimized). Following the study design established by Yang et al. in comparing EQ-5D-3L designs in a saturated VAS study (Yang et al. 2018), this process was replicated for the EQ-5D-5L. First, an EQ-5D-5L saturated VAS dataset was collected from a Chinese university student sample, with 100 VAS values for all 3125 EQ-5D-5L health states. Next, 100 variants of an orthogonal design1 with 25 health states were created and modelled. Their predictive performances were quantifed by calculating the Root Mean Squared Error (RMSE) against the observed VAS values from the saturated dataset. 25 health states were chosen as it is the minimal number for an orthogonal design in a fve-dimension fve-level classifcation system. For comparison, 100 variants of a random design and 100 variants of a D-effcient design were created, also with 25 states in each design. The EQ-VT design was also included as a reference (Yang et al. 2019a). The results showed that the RMSE was 3.44 for the EQ-VT design and 3.40 for the orthogonal design on the VAS scale (from 0 to 100). Little variance is observed among the 100 variants of the orthogonal design. Nevertheless, the inclusion of 11111 in the orthogonal design degraded the overall prediction performance. When extending the orthogonal design with the fve mildest states and the "pits" state (to counteract biases due to framing effects), the RMSE was 3.87. These results showed that the orthogonal design extended with fve mildest and the "pits" state could allow robust and precise estimations of EQ-5D-5L VAS values, as the RMSE was only slightly increased compared with the RMSE of 3.44 for EQ-VT design (i.e., the difference was 0.43 on VAS scale).

Considering the data distribution characteristics of the cTTO values from EQ-5D-5L valuation studies using the EQ-VT (e.g., they are not normally distributed, their distribution was separated by death into two parts, they displayed large heterogeneity etc.), a second study was performed validating the performance of orthogonal designs using cTTO data (Yang et al. 2019b). Following the EQ-VT protocol version 1.1 (as described in Chap. 2) cTTO data were collected from a sample of Chinese university students. In total, three designs were included in the study, i.e., (1) the EQ-VT design; (2) the best performing orthogonal design variant from the VAS saturated study; (3) a D-effcient design with 25 states. In total, 100 observations per health state were collected for the three designs of a total 136 health states (i.e., 86 + 25 + 25). Next, the value sets were modelled by design and their prediction accuracy was evaluated for the 136 states. The RMSEs of the (1) EQ-VT, (2) orthogonal + fve mildest states + the "pits" state and (3) D-effcient designs + fve mildest states + the "pits" state were 0.053, 0.066 and 0.063 on the value scale (0-1) respectively. Based on the fndings of these two studies, the use of

<sup>1</sup>An orthogonal design satisfes the criterion that all severity levels and all severity level combinations are equally prevalent and therefore balanced.

the EQ-VT design was confrmed as a default design choice for EQ-5D-5L valuation studies. However, the orthogonal design with 25 states + fve mildest states + one "pits" state can be used in some specifc contexts, e.g., when resources are not available for a standard EQ-VT study. Peru is the frst country to use the orthogonal design + fve mildest states + one "pits" state (referred as 'Lite' protocol; see Appendix D) for establishing its EQ-5D-5L value set (see Chap. 4). In that study, the modelling results suggested the 'Lite' protocol could produce logical consistent coeffcients, but some coeffcients were not signifcant. Additionally, the DCE coeffcients and cTTO coeffcients were found to be inequivalent in that study and a hybrid model of combining both responses was not used for the fnal Peruvian EQ-5D-5L value set. For the above-mentioned reasons, the authors suggest more research is needed to further explore the feasibility of such 'Lite' protocol (Augustovski et al. 2020).

# **3.4 Future Research**

Regarding design principles employed in EQ-5D-5L valuation studies, ongoing work focuses on refnement of current design strategies, and identifcation of new designs for emerging valuation approaches. Regarding the design used in the EQ-VT, evidence to date suggests that the design is ft for purpose. Across the range of studies already conducted with the EQ-VT (see Chap. 1 for details), the design has allowed precise estimation of health state values. However, there are a number of issues that require addressing in future.

First, it is apparent that the ten pairs of relatively mild health states appended to the DCE design are potentially problematic, and may cause bias in the parameter estimates. One plausible explanation for this is that the values for these health states are likely to be similar (as they are all close to full health and to each other), but the choice probabilities are not necessarily close to 50/50 since there may be a small but consistent preference for accepting a particular dimension at level two over another. Second, it may be that using a broader set of EQ-5D-5L health states yields a more accurate value set as the value of health states not directly observed in the data are more likely to have a near neighbour health state valued. For instance, the ongoing Indian EQ-5D-5L valuation study is exploring the use of an expanded set of 150 health states as part of the cTTO (Jyani et al. 2020). Third, the number of DCE choice pairs typically asked in the standard EQ-VT (i.e., seven) is limiting in terms of the models we might seek to estimate using the resultant data. For example, if we are interested in preference heterogeneity of the DCE data, then only having a small amount of DCE data precludes reliable estimation of more sophisticated latent class, mixed logit or generalised multinomial logit models (Fiebig et al. 2010), particularly if we are concerned with estimating correlations.

One more novel valuation approach that is under consideration currently is the use of DCE as a stand-alone task, a concept which has been growing in popularity in the health preference literature more generally (Mulhern et al. 2019). There are a range of potential advantages of a stand-alone DCE. Most importantly, the task can be undertaken without an interviewer (hence reducing the cost signifcantly) (Mulhern et al. 2013). Further, as it does not require interviewer travel, a smoother geographical distribution of respondents can be achieved (assuming that the surveying approach is equally accessible across regions). However, if we are reliant on the DCE alone (rather than as a component of the EQ-VT alongside cTTO tasks), then there is a need for more than seven choice observations per person, particularly if we want to move beyond estimation of population mean preferences, which is useful if we want to identify population sub-groups with specifc views. Further, there is a need to anchor the data so they can be used to populate cost-utility analysis, for instance through including one or more of a duration attribute, a 'dead' health state, or some other external anchor. Regarding design strategy for a stand-alone DCE, there has been particular focus on generator-type approaches and effcient designs. The relative merits of each have been widely discussed in the literature. For example, EuroQol-funded work has conducted a large DCE in Peru looking at different composite approaches to anchoring and design; these results have been reported as part of a larger study including cTTO and latent DCE tasks (Augustovski et al. 2020). Ongoing analysis of these data, and similar data collected in Denmark (Jensen et al. 2021), will explore whether there is clear enough evidence of superiority of one or the other design approach for this specifc purpose, and then to identify a design (or design approach) which can be used across countries conducting such a valuation survey.

This chapter has described the design strategies that have been used in existing EQ-5D-5L valuation projects, and their relative advances on those used for the EQ-5D-3L. The designs have been selected to balance statistical effciency with respondent ease (and hence data quality), and the current approach appears to refect a good trade-off between the two, with good completion rates, precise model estimates, and face validity of the fnal value sets across a number of languages, countries and cultures. The approaches used to this point are fexible, and can be adapted to meet the challenge of novel valuation approaches which may become more prominent in future years, and give policy makers confdence that the valuation surveys have accurately captured the attitudes of the general public without bias.

# **Appendices**

# *Appendix A: Construction for Level Balance Optimisation Criterion*

**Step 1:** A matrix (labelled "EQ lvl mat") with the counts for each level-domain combination is constructed (note that the example tables below contain hypothetical data using ten EQ-5D-5L states for illustrative purposes):

Reproduced from Oppe and van Hout (2017)

*AD* anxiety/depression, *MO* mobility, *PD* pain/discomfort, *SC* self-care, *UA* usual activities

**Step 2:** Using the data from "EQ lvl mat" a second matrix, containing the squares of the differences between the presence of levels per dimension is created (labelled "lvl dist mat"):

Reproduced from Oppe and van Hout (2017)

*AD* anxiety/depression, *MO* mobility, *PD* pain/discomfort, *SC* self-care, *UA* usual activities

**Step 3:** The elements of "lvl dist mat" are summed and the square root is taken over the sum to obtain the optimisation parameter (labelled "lvl bal check"):

"lvl bal check" = square root ( sum ( "lvl dist mat" ) ) = 7.75

A value for "lvl bal check" = 0 indicates perfect level balance (i.e. each leveldomain combination occurs twice).

A value for "lvl bal check" = 44.72 indicates the worst possible level balance: for each domain only 1 level is included. In this case "EQ lvl mat" contains one 10 and four 0's for each domain; "lvl dist mat" contains four 100's and six 0's, and the sum of "lvl dist mat" = 2000, with a square root = 44.72.

Note that perfect level balance is not a requirement (and might actually be undesirable in some cases). Small deviations can be allowed by e.g., setting a maximum allowable value for "lvl bal check" and letting the algorithm sample designs until it fnds one for which "lvl bal. check" is lower than this preset maximum.

# *Appendix B: The cTTO design of the EQ-VT*

**Table 3.2** The 86 EQ-5D-5L health states included in the composite TTO task of the EQ-VT

**Table 3.2** (continued)

Reproduced from Oppe and van Hout (2017)

*AD* anxiety/depression, *MO* mobility, *PD* pain/discomfort, *SC* self-care, *UA* usual activities

# *Appendix C: The DCE design of the EQ-VT*

**Table 3.3** The 196 pairs of EQ-5D-5L health states included in the DCE task of the EQ-VT

**Table 3.3** (continued)

**Table 3.3** (continued)

**Table 3.3** (continued)

**Table 3.3** (continued)

Reproduced from Oppe and van Hout (2017)

# *Appendix D: The Design Used in the Peruvian EQ-5D-5L Valuation Study*

**Table 3.4** The 31 EQ-5D-5L health states included in the composite TTO task in the EQ-VT in the Peruvian valuation study (Augustovski et al. 2020)

a Health states numbers 1 to 25 are based on the orthogonal design, 26 to 30 are the fve mildest states and 31 is the "pits" state

*AD* anxiety/depression, *MO* mobility, *PD* pain/discomfort, *SC* self-care, *UA* usual activities

AD

# **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Chapter 4 EQ-5D-5L Value Set Summaries**

**Bram Roudijk, Kristina Ludwig, and Nancy Devlin**

**Abstract** This chapter provides structured summaries of all 25 currently published national EQ-5D-5L value sets. The summaries were developed by extracting information from the published manuscripts of each value set and conducting secondary data analyses of the original valuation data generated in each country/region. The summaries include the mathematical formula for the preferred model for each national value set; information on the representativeness of the samples that were used to generate the value set; the mean values observed for each health state valued using composite time trade-off, the distribution of responses in the discrete choice experiment; information on the number of interviewers and whether any interviewer effects were present in the valuation data; key characteristics of the predicted values and relative importance of the EQ-5D-5L dimensions; and information on the uptake of the value set by local decision makers and health technology assessment bodies. This chapter serves as a compendium of EQ-5D-5L value sets, which may inform users of these value sets about the characteristics of all published EQ-5D-5L value sets.

# **4.1 Introduction**

This chapter provides summary-level information on the currently published EQ-5D-5L value sets. The countries/regions are reported alphabetically, by study wave. This means that the oldest studies are reported frst, and the newest studies are reported last. Figure 4.1 provides an overview of the 25 studies which are included, by wave and protocol version.

B. Roudijk (\*)

K. Ludwig

EuroQol Research Foundation, Rotterdam, The Netherlands

#### N. Devlin Centre for Health Policy, University of Melbourne, Melbourne, VIC, Australia

EuroQol Research Foundation, Rotterdam, The Netherlands e-mail<EMAIL_ADDRESS>
Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany

**Fig. 4.1** Overview of EQ-VT studies by study wave and protocol version

Most of the information reported in this chapter was extracted from the manuscripts in which these value sets were published. However, in some cases, some of the relevant information was not included in the published papers. In these cases, we have undertaken secondary analyses of the data sets, with permissions from the study authors, using the methods reported below.

From each value set, we have extracted the mathematical formula for the preferred model for the value set, presented as *V*(*x*) and present the relative weights for each of the different dimension levels (20 parameters). The parameter therefore represents the decrement from level 1 to the respective level. We also present some other key characteristics of the value sets, such as the order of importance of each of the 5 dimensions of the EQ-5D-5L, the value for the worst and best health states, as well as the value for the best suboptimal health state. Lastly, we report key aspects of the study, such as the time frame in which the valuation data was collected, the sample size, sampling frame and sample characteristics.

# **4.2 Methods**

As each study has valued the same 86 health states using cTTO using the study design discussed in Chap. 3, 1 we report the arithmetic means and standard errors for each of these 86 health states in each country/region. The means are calculated for the same sample used in modelling the value set in each case i.e., following any exclusions which may have been made, which we describe. For studies in which the feedback module was used, as discussed in Chap. 2, the arithmetic means are calculated after the exclusion of any fagged responses.

For the DCE data, we report the proportion of respondents choosing EQ-5D-5L state A by the difference in level sum score between the two states included in the pair. Here, the level sum score is merely the sum of the levels of an EQ-5D-5L health state to give a broad indication of the severity of the state. For example, for state 12312, the level sum score would be 1+2+3+1+2=9. For example, the difference in level sum scores within a DCE choice pair with alternative A being 12341 and alternative B being 22335 is then 11 − 15= −4. We then report the percentage of responses choosing A when the difference in level sum scores equals −4. This will be a mix of choice pairs, as various choice pairs will have a level sum score difference of −4.

The modelling and other data analysis strategies used by the value set research team in each country/region differ somewhat. Therefore, we report the following for

<sup>1</sup>With the exception of Peru, where a different health state design for the cTTO was used, as this study was conducted using an EQ-VT 'Lite' protocol. A health state design including 31 unique health states was used instead of the 86-state design. For Peru, we therefore report the means of 31 health states.

each study: (1) data exclusion criteria and the number of excluded responses/ respondents; (2) interviewer effects; and (3) a description of modelling choices. To determine whether there are any interviewer effects, we partition the variance in the valuation data into variance related to interviewers *i*, respondents *j* and responses *k*, and determine the relative share of variance attributed to differences between interviewers. This is done by employing a mixed model in the form of equation 4.1 to each of the valuation datasets:

$$U\_{\eta k} = \beta\_0 + \gamma\_i + \mu\_{\eta} + \varepsilon\_{\eta k} \tag{4.1}$$

This model assumes there is a mean value *β*0 for all health states, which varies by interviewer (*γi*), respondent (*μij*) and health state valued (*εijk*). Here, *β*0 is a fxed effects parameter, while all others are random effects parameters. We then assume that *<sup>i</sup>* ~ , *N* 0 <sup>2</sup> , *ij* ~ , *N* 0 <sup>2</sup> and *ij* ~ , *N* 0 <sup>2</sup> . 2

To determine the share of variance attributed to differences between interviewers, we then calculate the Intra Class Correlation (ICC) coeffcient as in Eq. 4.23 :

$$ICC = \frac{\sigma\_{\gamma}^{2}}{\sigma\_{\gamma}^{2} + \sigma\_{\mu}^{2} + \sigma\_{\varepsilon}^{2}} \tag{4.2}$$

The relative importance of the EQ-5D-5L dimensions was determined by taking the sum of the coeffcients for one dimension, and dividing this by the sum of the coeffcients for all coeffcients. This measure can be seen as the size of the share of the total weight assigned to all dimension levels, and takes into account the relative importance for each dimension at all levels.

Finally, depending on the availability of the relevant information, we report the uptake of the value set by local HTA agencies, as reported in the manuscript or drawing on information provided by the principal investigators of the valuation studies to the authors of this book.

For each value set, we include full reference details and any other relevant literature directly related to the value set. Permission to reproduce these value sets and related information have been granted by the journals in which they are published, and access to the data to facilitate secondary analyses reported in this chapter was granted by the principal investigators, on behalf of the study teams in each case.

<sup>2</sup>We did not consider correlation between the variance in responses between interviewers, respondents and responses, which is a limitation. However, accounting for correlation between variance in random slopes for interviewers and respondents may be challenging, as not every study may use a number of interviewers that is suffciently large to be able to compute these correlations reliably.

<sup>3</sup>The ICC partitions variance into shares attributed to interviewers, respondents and responses. This is a way of operationalising interviewer effects, as it measures the share of variance caused by differences between interviewers. Small differences in distributions refect good agreement between interviewers, and therefore small interviewer effects. However, they do not necessarily refect good data quality only, as other factors, such as clustering of values and inconsistent responses are not captured by this measure.

# **4.3 Country-by-Country Overview of Value Sets**

# *4.3.1 Wave 1*

#### **<IP_ADDRESS> Country/Region: Canada (Table 4.1)**

**Table 4.1** Overview of EQ-5D-5L value set for Canadaa

*AD* anxiety/depression, *MO* mobility, *PD* pain/discomfort, *SC* self-care, *UA* usual activities a We report the value set up to four decimals instead of the 3 decimal structure seen in other value set summaries in this chapter, due to the different modelling strategy that was applied to the Canadian data

The mathematical representation of the model for health state X is4 :

> *V x MO Level SC Level UA Level* 1 1351 0 0389 0 0458 0 0195 0 . . . . . . . . . 0444 0 0376 0 051 45 0 0584 45 0 1103 *PD Level AD Level MO SC U A PD AD Num sq* 45 0. . 1409 45 0 1277 45 0.0085 45

#### (i) **Date/wave of study**

Data were collected in the frst wave of EQ-5D-5L valuation studies using the EQ-5D-5L valuation protocol 1.0. Additionally, traditional TTO was used as elicitation technique to supplement the EQ-VT. Interviews were conducted in 2012.

#### (ii) **Sample size; sample frame**

1209 interviews with the general population were conducted in three English speaking metropolitan areas: Vancouver, Hamilton and Edmonton, and French speaking respondents were recruited in Montreal. Quota sampling with respect

<sup>4</sup>MO45, SC45, UA45, PD45 and AD45 are dummy variables that equal 1 when level 4 or 5 problems are reported in that dimension, and 0 otherwise. For example, SC45 will equal 1 in state 14111, but 0 in state 12111. Num45sq is a variable that represents the square of the number of level 4 or 5 problems in a health state, beyond the frst.

to age, gender, and education was applied. Of the 1073 respondents included in the fnal value set, 55.5% were female and 44.5% were male. The age distribution of the respondents was:

#### (iii) **Representativeness of achieved sample**

The study sample was representative for the Canadian population in terms of age (over 18 years), gender, marital status, born in Canada and language spoken at home. The sample was more educated, but had lower incomes compared to the general population in Canada (Table 4.2).

**Table 4.2** Representativeness of the sample in the Canadian valuation study

Reproduced from Xie et al. (2016)

a Statistics Canada 2006 and 2011

#### (iv) **Mean observed cTTO values of EQ-5D-5L states** (Table 4.3)

**Table 4.3** Mean observed cTTOa values by health state

*SE* standard error

a Conventional TTO data was collected alongside the cTTO data and was included in the modelling as well. Here, we report only the cTTO data for comparability with the other value sets reported in the book

# (v) **Proportions choosing A in the DCE based on relative severities of A and B** (Fig. 4.2)

**Fig. 4.2** Proportions choosing A based on relative severities of A and B. (DCE data were collected during the study, but not used in the modelling stage)

#### (vi) **Exclusion criteria**

A share of respondents with inconsistent responses were excluded. Inconsistencies were defned as strict dominance (e.g., assigning a higher value to state 11411 compared to state 11311). For each respondent, the number of dominated states by the very mild health states (with just one deviation from full health, e.g., 11121) was counted. Respondents were excluded if they assigned (a) the same or a lower value to at least half of the states that were dominated or (b) the same or a lower value for the very mild health state compared with the pits state (55555). 136 respondents met these exclusion criteria.

#### (vii) **Number of interviewers; Interviewer effects**

In total, 1209 interviews were conducted by 8 interviewers. The variance of the responses included in the fnal value set can be partitioned into variance related to differences between interviewers (2.6%), respondents (35.8%) and responses (61.6%).

#### (viii) **Description of modelling choices**

The Canadian EQ-5D-5L value set was based on a combination of cTTO and conventional TTO data. The authors chose a fnal model that included a linear parameter for each dimension, with each dimension variables having levels 1, 2, 3, 4 and 5. Furthermore, the authors added dummy variables for each of the 5 dimensions, that equal 1 if that dimension reports level 4 or 5 problems, and 0 otherwise. Lastly, a term was added that represents the square of the number of dimensions reporting level 4 or 5 problems, beyond the frst. The estimated model used a Tobit link function, assuming censoring at 0 for negative values and values equal to 0 in the cTTO, and assumed a random intercept for each respondent.

#### (ix) **Value Set** (Table 4.4 and Fig. 4.3)

**Table 4.4** Key characteristics of the Canadian value set

**Fig. 4.3** Value decrements across dimensions (*AD* anxiety/depression, *MO* mobility, *PD* pain/ discomfort, *SC* self-care, *UA* usual activities)

4 EQ-5D-5L Value Set Summaries

# (x) **Uptake by local HTA/health care decision makers**

Canadian Agency for Drugs and Technologies in Health (CADTH) is the national HTA body that reviews and makes reimbursement recommendations to the public insurance programs across Canada. Cost-utility analysis (CUA) is a recommended type of economic evaluation for reimbursement applications. Validated generic health state classifcation systems with Canadian-specifc value sets are recommended for the estimation of QALYs. Although there is no preference for any specifc instrument, the EQ-5D-5L has become one of the commonly used instruments in clinical trials and economic evaluations.

#### (xi) **Reference(s) of value set**

Xie F, Pullenayegum E, Gaebel K, Bansback N, Bryan S, Ohinmaa A, Poissant L, Johnson JA (2016) A time trade-off-derived value set of the EQ-5D-5L for Canada. Med Care 54(1):98–105

## **Further Literature**

Statistics Canada (2006) 2006 Census of Canada. https://www.statcan.gc.ca/. Accessed 14 July 2021

Statistics Canada (2011) https://www12.statcan.gc.ca/census-recensement/2011/ dp-pd/tbt-tt/Index-eng.cfm. Accessed 28 July 2021

#### **<IP_ADDRESS> Country/Region: China (Table 4.5)**

**Table 4.5** Overview of EQ-5D-5L value set for China

*AD* anxiety/depression, *MO* mobility, *PD* pain/discomfort, *SC* self-care, *UA* usual activities

The mathematical representation of the model for health state X is:

$$\begin{aligned} V(X) &= 1 - 0.066 \, MO\_2 - 0.158 \, MO\_3 - 0.287 \, MO\_4 - 0.345 \, MO\_5 - 0.048 \, SC\_2 \\ &- 0.116 \, SC\_3 - 0.210 \, SC\_4 - 0.253 \, SC\_5 - 0.045 \, UA\_2 - 0.107 \, UA\_3 \\ &- 0.194 \, UA\_4 - 0.233 \, UA\_5 - \textbf{0.058} \, PD\_2 - 0.138 \, PD\_3 - 0.252 \, PD\_4 \\ &- 0.302 \, PD\_3 - 0.049 \, AD\_2 - 0.118 \, AD\_3 - 0.215 \, AD\_4 - 0.258 \, AD\_5 \end{aligned}$$

#### (i) **Date/wave of study**

Data were collected in the frst wave of EQ-5D-5L valuation studies using the EQ-5D-5L valuation protocol 1.0. Interviews were conducted in 2012.

#### (ii) **Sample size; sample frame**

1332 interviews with the general population were conducted in fve urban areas in different parts of China: Beijing, Chengdu, Guiyang, Nanjing and Shenyang. Within these cities, respondents were sampled to represent these cities with respect to age, gender and educational level using a nonprobability sampling strategy. Of the 1271 respondents included in the fnal value set, 49.9% were female and 50.1% were male. The age distribution of the respondents was:

#### (iii) **Representativeness of achieved sample** (Table 4.6)

Study sample (N=1271) Chinese adult general population (2011)a **Sampling characteristics** Age, n (%) 18–29 313(24.6%) 25.3% 30–39 244(19.2%) 19.7% 40–49 272(21.4%) 22.9% 50–59 220(17.3%) 14.7% ≥ 60 222(17.5%) 17.3% Gender, n (%) Female 634 (49.9%) 49.4% Male 637 (50.1%) 50.6% Education, n (%) Primary or lower 138 (10.9%) Junior high school 396 (31.2%) Senior high school 446 (35.1%) College or higher 291 (22.9%) Residence of origin City 749 (58.9%) Country 82 (6.5%) Township or village 440 (34.6%) Employment status Full time employee 378 (29.7%) Temporary worker 301 (23.7%) Individual freelancer 148 (11.6%) Retired 240 (18.9%) Student 115 (9.1%) Unemployed 48 (3.8%) Other 41 (3.2%)

**Table 4.6** Representativeness of the sample in the Chinese valuation study

Reproduced from Luo et al. (2017)

a Chinese Statistical Yearbook 2011

#### (iv) **Mean observed cTTO values of EQ-5D-5L states** (Table 4.7)

**Table 4.7** Mean observed cTTO values by health state

*SE* standard error

# (v) **Proportions choosing A in the DCE based on relative severities of A and B** (Fig. 4.4)

**Fig. 4.4** Proportions choosing A based on relative severities of A and B

## (vi) **Exclusion criteria**

Respondents that were younger than 18 years old were excluded (n=25). Another 36 respondents were excluded as they did not finish the interview.

# (vii) **Number of interviewers; Interviewer effects**

In total, 1332 interviews were conducted by 20 interviewers. The variance of the responses included in the fnal value set can be partitioned into variance related to differences between interviewers (2.4%), respondents (28.8%) and responses (68.8%).

#### (viii) **Description of modelling choices**

The Chinese EQ-5D-5L value set was based on cTTO data only. The selected model was an 8-parameter multiplicative model with a random intercept, in which 5 coeffcients are estimated for the EQ-5D-5L's 5 dimensions, and 3 coeffcients are estimated for the 3 intermediate levels of the EQ-5D-5L (2,3 and 4), representing level weights. For levels 1 and 5, these are assumed to be 0 and 1, respectively. The predicted coeffcients were rescaled by (1-intercept). The eight-parameter model can be converted to 20 parameters, as presented in Table 4.8 for consistency purposes in this chapter.

#### (ix) **Value Set** (Table 4.8 and Fig. 4.5)

**Table 4.8** Key characteristics of the Chinese value set

**Fig. 4.5** Value decrements across dimensions (*AD* anxiety/depression, *MO* mobility, *PD* pain/ discomfort, *SC* self-care, *UA* usual activities)

4 EQ-5D-5L Value Set Summaries

# (x) **Uptake by local HTA/health care decision makers**

There is currently no HTA agency in China. There are only academic methodology Pharmacoeconomic/Health Technology Assessment guidelines, (China Guidelines for Pharmacoeconomic Evaluations Writing Group, 2019) but there are no recommendations about which specifc health related quality of life instruments are preferred for use.

# (xi) **Reference(s) of value set**

Luo N, Liu G, Li M, Guan H, Jin X, Rand-Hendriksen K (2017) Estimating an EQ-5D-5L value set for China. Value Health 20(4):662–669

# **Further Literature**

#### **<IP_ADDRESS> Country/Region: England (Table 4.9)**

**Table 4.9** Overview of EQ-5D-5L value set for England

*AD* anxiety/depression, *MO* mobility, *PD* pain/discomfort, *SC* self-care, *UA* usual activities

The mathematical representation of the model for health state X is:

$$\begin{aligned} V\left(X\right) &= 1 - 0.058\,MO\_2 - 0.076\,MO\_3 - 0.207\,MO\_4 - 0.274\,MO\_5 - 0.050\,SC\_2 \\ &- 0.080\,SC\_3 - 0.164\,SC\_4 - 0.203\,SC\_3 - 0.050\,UA\_2 - 0.063\,UA\_3 - 0.162\,UA\_4 \\ &- 0.184\,UA\_3 - 0.063\,PD\_2 - 0.084\,PD\_3 - 0.276\,PD\_4 - 0.335\,PD\_5 - 0.078\,AD\_2 \\ &- 0.104\,AD\_3 - 0.285\,AD\_4 - 0.289\,AD\_5 \end{aligned}$$

#### (i) **Date/wave of study**

Data were collected in the frst wave of EQ-5D-5L valuation studies using the EQ-5D-5L valuation protocol 1.0. Interviews were conducted between November 2012 and May 2013.

# (ii) **Sample size; sample frame**

1004 interviews were conducted (996 respondents completing the valuation tasks in full) with the general population. A sample of 2020 addresses from 66 primary sampling units (based on postcode sectors) across England was randomly selected, using the Post Offce small user Postcode Address File as the sampling frame. The sample was intended to be representative of adults aged 18 years and over living in private residential accommodation in England. Of the 912 respondents included in the analysis, 59.3% were female and 40.7% were male. The age distribution of the 912 respondents was:

#### (iii) **Representativeness of achieved sample**

The study sample was broadly representative for the English population in terms of age (over 18 years), sex and employment status. However, compared to the general population, the sample included a larger proportion of those aged over 75 and retired and a smaller proportion of younger individuals and males (Offce for National Statistics 2011) (Table 4.10).

**Table 4.10** Representativeness of the sample in the England valuation study

Reproduced from Devlin et al. (2018)

a Offce for National Statistics 2011

#### (iv) **Mean observed cTTO values by EQ-5D-5L state** (Table 4.11)

**Table 4.11** Mean observed cTTO values by health state

*SE* standard error

# (v) **Proportions choosing A in the DCE based on relative severities of A and B** (Fig. 4.6)

**Fig. 4.6** Proportions choosing A based on relative severities of A and B

#### (vi) **Exclusion criteria**

Twenty-three participants (2.3%) gave all 10 health states the same value, and 61 participants (6.1%) valued 55555 no lower than the value they gave to the mildest health state. Excluding these participants gave a core modelling dataset of 912 participants (9120 cTTO observations). No DCE data were excluded.

#### (vii) **Number of interviewers; Interviewer effects**

In total, 1004 interviews were conducted by 48 interviewers. Primary data collection was carried out in England by the market research company Ipsos MORI. The valuation data were collected via face-to-face interviews in respondents' homes.

The variance of the responses (following exclusions) can be partitioned into variance related to differences between interviewers (5.39%), respondents (32.46%), and responses (62.15%).

#### (viii) **Description of modelling choices**

The England EQ-5D-5L value set was based on 20-parameter hybrid model, combining both cTTO and DCE data. cTTO data were treated as being censored at −1 and at 1 (to account for asymmetry in the error distributions) and, for specifc responses, at 0 (e.g., for respondents who gave a value of 0 for more than one health state, including 55555). Heterogeneity was addressed via three latent classes, accounting for different groups of respondents differing in their use of

the scale. The latent class coeffcients act to apply an adjustment across all dimensions/level coeffcients; the value set reported in Table 4.12 above simplifes the presentation of the value set by reporting the coeffcients for dimensions/ levels after the application of the latent class coeffcients.

#### (ix) **Value Set** (Table 4.12 and Fig. 4.7)

**Table 4.12** Key characteristics of the English value set

**Fig. 4.7** Value decrements across dimensions (*AD* anxiety/depression, *MO* mobility, *PD* pain/ discomfort, *SC* self-care, *UA* usual activities)

#### (x) **Uptake by local HTA/health care decision makers**

The value set was subject to a 'quality assurance' commissioned by the Department of Health for England from the Economic Evaluation Policy Research Unit (EEPRU). EEPRU's critique is summarised in Hernandez-Alava et al. (2020) and a response from the authors is provided in van Hout et al. (2020). In response to concerns about data quality raised by EEPRU, NICE (2019) issued a position statement indicating it did not recommend use of the value set for England, recommending instead that EQ-5D-5L data be mapped to the EQ-5D-3L using the crosswalk published by van Hout et al. (2012) and the Dolan (1997) UK value set for the EQ-5D-3L be used. A new value set – for the UK, rather than for England – has been commissioned and NICE will review its policy once that study is complete (expected in 2022).

#### (xi) **Reference(s) for this value set**

#### **Further Literature**

#### **<IP_ADDRESS> Country/Region: Netherlands (Table 4.13)**

**Table 4.13** Overview of EQ-5D-5L value set for Netherlands

*AD* anxiety/depression, *MO* mobility, *PD* pain/discomfort, *SC* self-care, *UA* usual activities

The mathematical representation of the model for health state X is:

$$\begin{aligned} V(X) &= 1 - 0.047 - 0.035 \, MO\_2 - 0.057 \, MO\_3 - 0.166 \, MO\_4 - 0.203 \, MO\_3 \\ &- 0.038 \, SC\_2 - 0.061 \, SC\_3 - 0.168 \, SC\_4 - 0.168 \, SC\_5 - 0.039 \, UA\_2 \\ &- 0.087 \, UA\_3 - 0.192 \, UA\_4 - 0.192 \, UA\_5 - 0.066 \, PD\_2 - 0.092 \, PD\_3 \\ &- 0.360 \, PD\_4 - 0.415 \, PD\_5 - 0.070 \, AD\_2 - 0.145 \, AD\_3 - 0.356 \, AD\_4 \\ &- 0.421 \, AD\_3 \end{aligned}$$

#### (i) **Date/wave of study**