Analysis of EQ-VAS and EQ-5D profile data from PROMs

Professor Nancy J Devlin ndevlin@ohe.org

Canmore, Canada October 18th 2012

1. The elements of the EQ-5D instrument

2. Cautions about reliance on index weighting

3. What does the EQ-VAS tell us?

4. What does the analysis of profile data tell us?

5. Conclusions

Contents

1. The elements of the EQ-5D instrument

• EQ-5D comprises two distinct self-report elements, providing three principal approaches to analysis

(i) the EQ-5D profile: the patients’ self reported health on the dimensions/levels of the descriptive system.

(ii) the EQ-VAS: the patients’ own global rating of their overall health, on a scale from 0 (worst possible health) to 100 (best possible health)

• Both types of data can themselves be the focus of analysis, plus

(iii) Profiles can be summarised using ‘value sets’ (EQ-5D Index) which reflect the preferences of the general public.

EQ-5D profile

• EQ-VAS

Index weighting EQ-5D profiles

This weight depends on: - who is asked - which stated preference method is used - how the preference data are modeled (eg the infamous MVH ‘N3’ term)

2. Cautions about reliance on index weighting

• Index-weighting profiles is the most common approach to the analysis of EQ-5D data • in part, because analysing ‘single numbers’ is alot more

convenient than analysing multiple attribute profile data

• There is a clear rationale for Index weighting when the purpose is to estimate QALYs for cost effectiveness analysis

• The rationale is less clear for use in summarising PROMs data eg. whose ‘values’ are relevant in assessing hospital performance: general public, or patients?

• And there are (poorly understood) issues with relying on Index-weighting as a means of summarising profiles.

• No set of weights is ‘neutral’

• each has implications for the relative importance of changes between levels and across dimensions.

• Including ‘equal weighting’!

• Each value set has its own properties – introducing an exogenous source of variance.

• The selection of which value set to use will have implications for statistical inference.

• Summarising profile data using an index loses some potentially important underlying information • what aspects of health are effected by the condition? • what aspects of health are affected by treatment? Source: Parkin D, Rice N, Devlin N. (2010) Statistical analysis of EQ-5D profiles: does the use of value sets bias inference? Medical Decision Making 30(5), 556-565.

Source: Parkin D, Devlin N, Feng Y. (2013) What determines the shape of an EQ-5D index distribution? Paper to be presented to the January HESG meeting, Exeter University.

Bi-modality in Index weighted patient data

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-.5 0 .5 1EQ_5D_Scores

MVH weighted index

Pre-surgery EQ-5D scores for hip replacement patients

• Patients’ ‘raw’ EQ-5D responses – both the profile and the EQ-VAS – tend to be under-analysed.

• Yet there is much to be gained from regarding these, at the very least, as important and complementary information.

3. What does the EQ-VAS tell us?

• Concerns that the EQ-VAS data from the PROMs programme produces different results than either the Index-weighted or condition specific instruments.

• How to interpret those differences?

• What is being measured by the EQ-VAS?

Source: IC (2012)

Source: Feng, Parkin, Devlin (2012) Assessing the performance of the EQ-VAS in the NHS PROMs programme, OHE Research Paper 12/01.

EQ-VAS and EQ Index distributions

Before (Q1) and after (Q2) hip surgery in the NHS

Coefficient Standard Error

Mobility Level 2 -5.1151 0.1412

Mobility Level 3 -10.6205 0.8807

Self Care Level 2 -6.5424 0.1098

Self Care Level 3 -10.5095 0.5634

Usual Activities Level 2 -3.4104 0.1439

Usual Activities Level 3 -7.6327 0.2009

Pain & Discomfort Level 2 -2.3947 0.1608

Pain & Discomfort Level 3 -6.6688 0.1929

Anxiety & Depression Level 2 -7.8700 0.1001

Anxiety & Depression Level 3 -15.2284 0.2524

Constant 86.3203 0.1298

The relationship between the EQ-VAS and EQ-5D profile

Notes: Number of observations = 154,890. R2 = 0.2672, Adjusted R2 = 0.2672, F = 5647.66, p 0.00005 . All coefficients significantly different from 0 at the 0.0005 level

Source: Feng, Parkin, Devlin (2012) Assessing the performance of the EQ-VAS in the NHS PROMs programme, OHE Research Paper 12/01.

• There is a predictable relationship between EQ-5D profile and EQ-VAS – coefficients for level 3 consistently > level 2, and all highly statistically significant.

• Patients’ EQ-5D profiles only partly explain their EQ-VAS scores.

• Conceptually, what is measured by EQ-VAS is different…

• Broader than just the dimensions of health represented by the EQ-5D profile

• Much broader than the tightly-focused items in condition specific instruments

• In contrast to Index weighted profiles, tells us the patients’ view of how aspects of their health effect their overall health.

4. What does the analysis of profile data tell us?

EQ-5D profiles – distributions

EQ-5D Profiles – categorising change

Source: Feng, Parkin, Devlin (2012) Assessing the performance of the EQ-VAS in the NHS PROMs programme, OHE Research Paper 12/01.

Hospital performance by profile dimension

Usual activities

Pain/discomfort

Source: Gutacker N, Bojke C, Daidone S, Devlin N, Street A. (2012) Analysing hospital variations in health outcome at the level of EQ-5D dimensions. Research Paper No. 74, Centre for Health Economics, University of York.

Profile Index Number Within cluster Overall % Cumulative % % Cumulative %

High cluster 21221 0.691 27412 43.14 43.14 19.19 19.19

21222 0.62 10040 15.80 58.94 7.03 26.22

22221 0.587 6613 10.41 69.35 4.63 30.85

22222 0.516 5525 8.70 78.05 3.87 34.72

21121 0.727 4332 6.82 84.87 3.03 37.76

Low cluster 21231 0.159 10867 21.34 21.34 11.85 11.85

22232 -0.016 7502 14.73 36.08 8.18 20.03

21232 0.088 6413 12.59 59.51 6.99 33.04

22231 0.055 5518 10.84 59.51 6.02 33.04

22332 -0.074 4177 8.20 67.71 4.55 37.59

Frequency of profiles, hip replacement Q1

• Just 10 of the 243 EQ-5D profiles account for 75% of all patients’ Q1 profiles. • The nature of those profiles tells us important things about these groups eg. it is not

differences in mobility that are important in differentiating between these groups, but pain/discomfort.

5. Conclusions

• Analysis of index weighted EQ-5D profiles is useful…

• But analysis of EQ-5D data should not be restricted to analysis of index weighted profiles

• Index weighting obscures relevant information on the underlying health problems that drive index weighted scores

• Hospital performance varies across dimensions

• Implications for understanding where improvements in performance are possible

• Profile data can tells us important things about relevant patient sub-groups.

• The EQ-VAS provides the patients’ own perspective on their overall health

References and acknowledgements

Feng Y, Parkin D, Devlin N (2012) Assessing the performance of the EQ-VAS in the NHS PROMs programme. OHE Research Paper 12/01. London: Office of Health Economics. * Gutacker N, Bojke C, Daidone S, Devlin N, Street A. (2012) Analysing hospital variations in health outcome at the level of EQ-5D dimensions. Research Paper No. 74, Centre for Health Economics, University of York. Parkin D, Rice N, Devlin N. (2010) Statistical analysis of EQ-5D profiles: does the use of value sets bias inference? Medical Decision Making 30(5), 556-565. Parkin D, Devlin N, Feng Y. (2013) What determines the shape of an EQ-5D index distribution? Paper to be presented to the January HESG meeting, Exeter University.

* This project was funded by the National Institute for Health Research (NIHR) in England under the Health Services Research (HSR) stream (project number 09/2000/47). The views expressed are those of the authors and may not reflect those of the NIHR HSR programme or the Department of Health.