• Biosimilars
  • Drug Development/R&D
  • All Topics
OHE OHE
Newsletter SignupSubscribe

News & Insights
  • News
  • Events
  • Insights
  • Bulletin
  • News
  • Events
  • Insights
  • Bulletin

News & Insights

  • News
  • Events
  • Insights
  • Bulletin
Newsletter SignupSubscribe
  • News
  • Events
  • Insights
  • Bulletin

Close
OHE OHE
  • Research & Publications
  • News & Insights
  • Education
  • Innovation Policy Prize
  • Events
  • About Us
  • OHE Experts
  • Contact Us
Newsletter SignupSubscribe

Research & Publications

All Publications

Filter by:
  • Antimicrobial Resistance (AMR)
  • Biosimilars
  • Cell and Gene Therapies
  • Chronic Diseases
  • Combination Therapies
  • COVID-19 Research
  • Digital Health
  • Drug Development/R&D
  • Emerging Markets
  • EQ-5D and PROMs
  • Health Care Systems
  • Health Data and Statistics
  • Health Technology Assessment
  • Precision Medicine
  • Real World Evidence
  • Use of Medicines
  • Value-Based Pricing
  • Vaccine Research
  • Economics of Innovation
  • Measuring and Valuing Outcomes
  • Policy, Organisation and Incentives in Health Systems
  • Value, Affordability and Decision Making

News & Insights

  • News
  • Events
  • Insights
  • Bulletin

Education

  • Education Hub
  • OHE Graduate School
  • EVIA Programme

Innovation Policy Prize

  • The Prize Fund
  • 2022 Prize Fund

Latest Research & Publications

Proposal for a General Outcome-based Value Attribution Framework for Combination Therapies

CombTher_Adobe_photoguns_portrait
Read more
© photoguns
  • Digital Health

Navigating the Landscape of Digital Health – United Kingdom

Healthcare_Adobe_elenabsl
Read more

2021 OHE Annual Report to the Charity Commission

charityreport_lina-trochez-unsplash_landscape
Read more
© Lina Trochez/Unsplash

Supporting the Era of Green Pharmaceuticals in the UK

Sustainability_AdobeStock_270582392_landscape
Read more

Quality of life and wellbeing in individuals with experience of fertility problems and assisted reproductive techniques

Quality of life assisted reproduction Cover
Read more
  • Cell and Gene Therapies
  • Value, Affordability, and…

Health Technology Assessment of Gene Therapies: Are Our Methods Fit for Purpose?

gene_therapies_national-cancer-institute-unsplash_landscape
Read more
© NCI/Unsplash
  • Drug Development/R&D
  • Economics of Innovation
  • Health Policy and Regulation

Limitations of CBO’s Simulation Model of New Drug Development as a Tool for Policymakers

CBO-US_mayer-tawfik-K4Ckc0AxgDI-unsplash_landscape
Read more
© Mayer Tawfik/Unsplash
  • Measuring and Valuing Outcomes

When Generic Measures Fail to Reflect What Matters to Patients: Three Case Studies

PROMS_unsplash_National Cancer Institute_landscape
Read more
© NCI/Unsplash
Close
OHE
  • All Publications

    Filter by:
    • Antimicrobial Resistance (AMR)
    • Biosimilars
    • Cell and Gene Therapies
    • Chronic Diseases
    • Combination Therapies
    • COVID-19 Research
    • Digital Health
    • Drug Development/R&D
    • Emerging Markets
    • EQ-5D and PROMs
    • Health Care Systems
    • Health Data and Statistics
    • Health Technology Assessment
    • Precision Medicine
    • Real World Evidence
    • Use of Medicines
    • Value-Based Pricing
    • Vaccine Research
    • Economics of Innovation
    • Measuring and Valuing Outcomes
    • Policy, Organisation and Incentives in Health Systems
    • Value, Affordability and Decision Making
    • News
    • Events
    • Insights
    • Bulletin
    • Education Hub
    • OHE Graduate School
    • EVIA Programme
    • The Prize Fund
    • 2022 Prize Fund
  • Events
  • About Us
  • OHE Experts
  • Contact Us
Newsletter SignupSubscribe
Back
  • Insight
11 min read 29th January 2016

5 Things You Should Do with EQ-5D Data

Professor Nancy Devlin outlines her top 5 recommendations for anyone collecting or reporting EQ-5D data. Written by Professor Nancy Devlin, Director of Research at OHE and member of the EuroQol Group EQ-5D is the most widely used measure of patient…

Share:
  •  Twitter
  •  LinkedIn
  •  Facebook
  • has-icon Email
ND_blog_1

Professor Nancy Devlin outlines her top 5 recommendations for anyone collecting or reporting EQ-5D data.

Written by Professor Nancy Devlin, Director of Research at OHE and member of the EuroQol Group

EQ-5D is the most widely used measure of patient reported outcomes (PRO) around the world. For a couple of decades now, it has been used in clinical trials, observational studies, population health surveys and – more recently – in routine data collection in health care systems.  Because it’s a generic PRO, and because it is accompanied by ‘value sets’ (a.k.a ‘utilities’) used in the calculation of QALYs (quality adjusted life years), EQ-5D has become the cornerstone of health technology appraisal (HTA), affecting important decisions about access to new medicines.

But despite all that, EQ-5D data are often under-reported, and inadequately analysed.  The bottom line is – if you collect these data from your patients, you should be committed to making sure you learn as much as possible from what they tell you. 

So: for anyone collecting or reporting EQ-5D data, here are my recommendations for what to do with EQ-5D data. This applies to the original three-level version, the EQ-5D-3L; the five-level version, EQ-5D-5L; the child-friendly version, EQ-5D-Y – and arguably, any PRO instrument.

1. What not to do – don’t skip straight to ‘utilities’ or use of scores of any kind to summarise patients’ data

OK: you’ve collected EQ-5D data. That’s great for lots of reasons! The EQ-5D is a fantastic way of measuring health outcomes in a generic way that can be compared across disease areas. A bunch of patients/people have ticked boxes to describe their health on the five dimensions. A common approach is to summarise those responses into a single number on a scale anchored at 1 (full health) using the value sets that are available for that purpose (Szende et al 2007). It makes analysis of the data a lot easier: after all – statistically, single numbers are easier than categorical data.

Job done? Wrong!

You should know that:

  • There is no ‘neutral’, or ‘objective’ way of summarising EQ-5D data (or data from any PRO measure, for that matter!)  
  • Whatever ‘value set’ you use to summarise your EQ-5D data, it will introduce an exogenous source of variance into statistical inference (Parkin, Devlin and Rice 2010) – that is, variance which does not come from the data that your patients have given you.  Conclusions about whether there are statistically significant differences between different population or patient groups – or between different arms of a clinical trial – is affected by which value set is used. There are important differences between the statistical properties of the various value sets available for the EQ-5D. (For more on this, see Parkin et al 2014. We are currently looking at these same things in relation to EQ-5D-5L data – which we’ll be reporting soon in Feng et al 2016).
  • Be aware of where the values come from! The values come from ‘stated preference’ studies:  by convention, these studies collect views from members of the general public – who are asked to imagine living with various health problems. We know that there are differences between the general public and patients with respect to their views and opinions about how good or bad health problems are.  These preference-based value sets were developed with a specific purpose in mind: to estimate QALYs. If you aren’t using EQ-5D data to estimate QALYs, there may not be a very strong rationale for using value sets to summarise EQ-5D data.

Note – none of this is a criticism of EQ-5D – if anything, the opposite! The EuroQol Group is open about these sorts of issues and has extensively researched them. All other generic and condition specific PROs have the exact same problems – they just don’t say much about it.

2. Look at patients’ responses to the question items (dimensions)

Don’t underestimate the importance and usefulness of good, old fashioned descriptive statistics!  Descriptive statistics on PRO data are undervalued and underreported in papers, which is a shame.

Summarising EQ-5D data by the value sets doesn’t tell you much about which aspects of patient or population health have been most affected by their condition, or improved by treatment.  To know about that, you need to look at the data that patients have actually given you: the boxes they have ticked on the EQ-5D questions.  For example, you should always report the number and percentage of patients reporting each level of problem on each dimension of the EQ-5D. If you want to simplify things, you can also collapse levels 2 and 3 together, and report the number reporting ‘no’ problems, and the number reporting ‘any’ problems.

In addition to describing patient health at one point in time, you may be interested in describing the changes in patient health – for example, before or after surgery, or at various time points in a clinical trial, compared to baseline.

This too can be done at the level of the EQ-5D dimensions. Again – descriptive statistics can tell you a lot. For example, when we looked at the change in the number and percentage of NHS hip replacement patients reporting problems by dimension (see Table 1 below), before and after surgery, we found that there were quite striking improvements in patients’ anxiety and depression, self-care and pain/discomfort –  not just mobility (Devlin et al 2010). In fact, what was striking was that no patients had a ‘level 3’ on mobility before surgery, so the only improvements possible as a result for surgery were from ‘some’ to ‘no’ problems. The reason? Level 3 on the EQ-5D-3L mobility dimension is ‘confined to bed’ – and even patients with very poor mobility because of hip problems aren’t stuck in bed. That’s a problem with the EQ-5D-3L – as we have pointed out previously (Oppe et al 2011) – and has been corrected in the new EQ-5D-5L (Herdman et al 2014). None of these things would have been apparent if these patients’ data had been analysed just in terms of the utilities.  

 
Source: Devlin et al (2010)

3. Summarising change without using value sets

Tables like the one shown above can be very informative – but are complicated to look at, and sometimes an overall summary is required.   The good news is that it is easy to summarise changes in EQ-5D health status, just using the data patients have given you.

In 2010, we came up with a way of doing that, based on the principles of a Pareto improvement in Welfare Economics – the Pareto Classification of Health Change (PCHC) (Devlin et al 2010). The idea is simple: an EQ-5D health state is deemed to be ‘better’ than another if it is better on at least one dimension, and is no worse in any other dimension. And an EQ-5D health state is deemed to be ‘worse’ than another if it is worse in at least one dimension, and is no better in any other dimension. Using that principle to compare a patient’s EQ-5D health states between any two time periods, there are only 4 possibilities:

–          Their health state is better

–          Their heath state is worse

–          Their health state is exactly the same

–          The changes in health are ‘mixed’: better on one dimension, but worse on another.

Applying this to the hip replacement data, we found less than 5% had no change, 82% had improved health, less than 5% had worse health, and under 10% had a ‘mixed’ change (Devlin et al 2010).  In other words, this simple analysis provides a very clear summary of what is happening to patients’ health as a result of hip surgery – without relying on value sets.  It also highlighted important differences in the benefits from hip surgery, compared with other types of elective surgery.

There are also other ways of summarising EQ-5D data. I won’t go into them all here – but there are a variety of approaches, and some have important limitations as a way of summarising patients’ data. An example of the latter is to approximate the overall ‘severity’ of a state by its ‘level sum score’, which simply adds up the levels on each dimension. The best EQ-5D-3L health state involves having no problem on any dimension. ‘No problem’ = 1, so no problems at all (1+1+1+1+1) = 5. The most severe problem on any dimension = 3, so the worst health state is (3+3+3+3+3) = 15. Every other health state on the EQ-5D-3L will have a level sum score between the best (5) and the worst (15). This can also be applied to the EQ-5D-5L, where the best is again 5, and the worst is (5+5+5+5+5) = 25. These level sum scores can be useful in some situations– but there are obvious limitations.  It’s a very crude summary score – for example, 22222, 33211 and 11233 all have the same level sum score (= 10).  And each score contains a very different number of potential profiles (5 and 15 have just one profile each; whereas level sum score 10 contains 51 profiles). Also, just because it weights the dimensions equally doesn’t mean it is ‘value judgement free’ – equal weighting of dimensions in itself represents a view about their importance (Parkin et al 2010).

4. Use your EQ-VAS data!

The EQ-VAS is a 0-100 scale where patients are asked to indicate their overall health today. We (speaking here as a member of the EuroQol Group!) regard it to be an integral part of the EQ-5D questionnaire – but is often completely overlooked and unreported (or worse still – some users drop it from data collection, even though it is a part of the copyright instrument!). The EQ-VAS offers important, complementary information to the health state information patients provide when they self-report their health on the EQ-5D. In fact, the EQ-5D is unique as a PRO instrument in generating data showing patients’ own, overall assessment of their health. Not someone else’s view of how good or bad their overall health state is, if they imagine being in it, but the view of the person actually experiencing it. That sounds like useful information – and it is.

For example, the EQ-VAS can capture problems that aren’t captured within the 5 dimensions of the EQ-5D – potentially revealing some gaps in the EQ-5D relevant to particular patient groups (see Feng et al 2014). This helps to interpret the EQ-5D data you’ve collected, and whether there might be any reason to suggest it does not fully capture the effects of health problems.

Not convinced? Take a look at this diagram, which has been generated from the vast amounts of data held by the EuroQol Group.  There is a sharply declining EQ-VAS by age for those who report problems on the EQ-5D (i.e as age increases, so do the problems reported on the five dimensions and so too does patients’ overall self-assessed health on the EQ-VAS). But interestingly, EQ-VAS declines with age, even among patients reporting no problems on the EQ-5D. This suggests the EQ-VAS is measuring something a bit different to, and additional to, the things in the 5 dimensions. 


 

5. Last but not least: if you do need to use a value set to summarise your EQ-5D data – e.g., for estimating QALYs – make sure you do sensitivity analysis to alternative value sets.

Value sets are a product of researchers’ decisions about what methods to use, and how to model to data. Those decisions can potentially have a non-trivial effect on the characteristics of the values that are generated – for example, what the minimum value is, and how many negative values there are, and what the distribution of the values looks like.  There is a lack of consensus among researchers about what methods are ‘best’, and different methods, both for eliciting values and modelling them, can lead to different results.

The implication is that the values to be applied to EQ-5D data have some uncertainty associated with them. But then, so does the evidence on lots of other things in cost effectiveness analysis, like the effectiveness of new technologies, and their costs. So – just like any other uncertain parameter in a cost effectiveness model, the implication is that analysts should make sure they check whether their conclusions about cost effectiveness are sensitive to the choice of the value set. And, where value sets report confidence intervals (which they all should!) those confidence intervals should also form part of the sensitivity analysis in cost effectiveness analysis.  

Want to know more? Selected references:

Devlin N, Parkin D, Browne J. (2010). Using the EQ-5D as a performance measurement tool in the NHS. Health Economics 19(8):886-905.

Parkin D, Devlin N, Rice N. (2010) Statistical analysis of EQ-5D profiles: does the use of value sets bias inference? Medical Decision Making 2010; 30:556-565

Parkin, D., Devlin, N. and Feng, Y., 2014. What determines the shape of an EQ-5D distribution?  OHE Research Paper 14/04. 
 
Feng Y, Devlin N, Bateman A, Zamora B, Parkin D. (2016) The distribution of EQ-5D-5L Index in patient populations. OHE Research Paper (forthcoming). 
 
Oppe M, Devlin N, Black N (2011) Comparison of the underlying constructs of EQ-5D and Oxford Hip Score: implications for mapping. Value in Health 14 884-891. 
 
  • EQ-5D and PROMs
  • Measuring and Valuing Outcomes

Related Insights

Air balloon HTA v4
  • Insight
  • March 2023

Around The World in HTAs: Spain – Are We There Yet?

Read more
Informal Carer Burden Series: OHE on behalf of Roche
  • Insight
  • March 2023

Caring about Carers: Improving Consideration of the Burden of Informal Caring in HTA

Read more
Eye-Disease Gene-Therapies blog
  • Insight
  • February 2023

Cost-effectiveness Analysis of Gene Therapies for Inherited Eye Disease: Are Current Discounting Approaches Too Short-sighted?

Read more
Fishing on the lake at sunset. Fishing background.
  • Insight
  • February 2023

Fishing for Innovative Drugs with the ODRS: Potential Benefits and Challenges

Read more
footer_ohe_logo

Leading intellectual authority on global health economics

Sign Up for the OHE News Bulletin

Newsletter SignupStart Sign Up

Research & Publications

News & Insights

Innovation Policy Prize

Education

Events

About Us

OHE Experts

Contact Us

Sign Up for the OHE News Bulletin

Newsletter SignupStart Sign Up

The Office of Health Economics (OHE) is a company limited by guarantee registered in England and Wales (registered number 09848965) and its registered office is at 2nd Floor Goldings House, Hay’s Galleria, 2 Hay’s Lane, London, SE1 2HB.

Terms & Conditions

Privacy Policy

Cookies Policy

© 2023 Website Design

An error has occurred, please try again later.An error has occurred, please try again later.

We are using cookies to give you the best experience on our website.

You can find out more about which cookies we are using or switch them off in settings.

 Twitter
 Facebook
 LinkedIn
 Copy
 Email
Powered by  GDPR Cookie Compliance
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Strictly Necessary Cookies

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.

3rd Party Cookies

This website uses Google Analytics to collect anonymous information such as the number of visitors to the site, and the most popular pages.

Keeping this cookie enabled helps us to improve our website.

Please enable Strictly Necessary Cookies first so that we can save your preferences!