1. Professor Nancy J. Devlin Office of Health Economics Royal Statistical Society June 18 th 2015 Measuring and ‘valuing’ patient reported health

2. Royal Statistical Society June 18th 2015 2 1. Measuring patient reported health 2. Use and applications of PROs 3. The role of PROs in economic evaluation 4. ‘Weighting’/summarising PROs: an example (the EQ-5D-5L value set for England) 5. Statistical issues relating to the use of weights 6. Normative issues relating to the use of weights 7. Concluding remarks Contents

3. Royal Statistical Society June 18th 2015 3 1. Measuring patient reported health Clinical measures of health (e.g. mortality rates) can provide important evidence about effectiveness and quality of health care. But these things miss the patients’ perspective on health. Most health care has as its aim to make the patient feel better. Growing awareness of the importance of this. Patient reported outcomes (PROs) are questionnaires that aim to measure patients’ subjective accounts of their health in a structured, systematic way, that is valid and reliable. Amenable to cross sectional and longitudinal analysis

4. Royal Statistical Society June 18th 2015 4 PRO instruments “The use of PRO instruments is part of a general movement toward the idea that the patient, properly queried, is the best source of information about how he or she feels”. [FDA 2006] Many well-validated instruments exist which are reliable, sensitive and widely used. (Oxford University website)Oxford University website Simple to complete; quick to analyse. Repeated observations (e.g. before and after treatment) can provide a clear picture of changes in health, and outcomes from treatment. Condition specific PROMs: more question items/response options; focussed on a specific aspect of health. Generic PROMs: measure health related quality of life generally. Enable comparisons of health across conditions/health services. E.g. “EQ-5D” and “SF-36”

5. Royal Statistical Society June 18th 2015 5 A generic PRO: the EQ-5D-5L Descriptive system/’profile’ 5 5 = 3,125 ‘states’ Patients self-reported health, which is summarised in descriptive terms as 11111, 55555, etc. Methods of analysis: descriptives; ‘level sum scores’; ‘Pareto Classification of Health Change’.

6. Royal Statistical Society June 18th 2015 6 The EQ-VAS - The EQ-VAS – used to obtain the patients’ overall assessment of their health. - Simple to analyse.

7. Royal Statistical Society June 18th 2015 7 2. Use and applications of PROs Data collectionUses Clinical trialsEffectiveness & cost effectiveness Observational studiesEffectiveness & cost effectiveness Population health surveysBurden of disease Individual patientsPersonal health diaries; shared decision making Routine data collection as part of health service delivery -English NHS -Private hospitals in the UK -Sweden, Canada… Monitoring quality of services Provider performance Effectiveness/cost effectiveness of treatments

8. Royal Statistical Society June 18th 2015 8 4. PRO data in economic evaluation In cost effectiveness analysis, the incremental cost effectiveness ratio (ICER) =  cost / QALYs. Enables comparisons of ‘cost per QALY gained’ of different treatments competing for funding. QALYs: A measure of outcome which combines both quality and length of life. Quality of life used to ‘weight’ length of life Weights on a scale anchored at 1 = full health, 0 = dead (< 0 ‘worse than being dead’) 1 QALY = a year of perfect health Can capture changes in quality of life, length of life or both.

9. Royal Statistical Society June 18th 2015 9 5. Weighting/valuing PROs For use in economic evaluation, each health state described by a PRO requires a QoL weight, anchored on a scale anchored at 0 = dead and 1 = full health. Weights are obtained from stated preference studies – a sample of respondents asked to consider a set of health states that are hypothetical to them, and engage in a series of tasks intended to discover how good or bad they consider each to be Regression analysis used to model a ‘value set’ for all health states

10. Royal Statistical Society June 18th 2015 10 EQ-5D-5L value set for England Research protocol developed by the EuroQol Research Foundation Stated preference data collected in face-to-face computer- assisted personal interviews n = 1000 members of the adult general public of England, selected at random from residential postcodes Sample recruitment sub-contracted to Ipsos MORI Each respondent valued 10 health states using TTO, randomly assigned from 86 health states in an underlying design; and seven DCE tasks, randomly assigned from 196 pairs of states ‘Composite’ TTO approach: conventional TTO for values > 0 and ‘lead time’ TTO for values < 0 The EuroQol Valuation Technology software (EQ-VT) was used to present the tasks and to capture respondents’ responses

11. Royal Statistical Society June 18th 2015 11 TTO for values > 0 (states better than dead) Example shown: U(h i ) = 5/10 = 0.5 U(h i ) = (x/t) where x is the time in full health and t is the time in health state h i at the respondent’s point of indifference

12. Royal Statistical Society June 18th 2015 12 Example shown: U(h i ) = (5-10)/10 = -0.5 t = 20 years lead time (LT) = 10 years U(h i )= (x-LT)/(t-LT) = (x-10)/10 Min value = -1 TTO for values < 0 (states worse than dead)

13. Royal Statistical Society June 18th 2015 13 DCE task

14. Royal Statistical Society June 18th 2015 14 England EQ-5D-5L values95% CIs constant 1.003(0.983 -1.019) Mobilityslight0.057(0.043 -0.075) moderate0.074(0.057 -0.093) severe0.207(0.190 -0.227) unable0.255(0.237 -0.275) Self careslight0.059(0.045 -0.074) moderate0.083(0.061 -0.101) severe0.176(0.157 -0.197) unable0.208(0.189 -0.225) Usual activitiesslight0.048(0.033 -0.066) moderate0.067(0.047 -0.086) severe0.165(0.147 -0.180) unable0.165(0.152 -0.184) Pain/discomfortslight0.059(0.042 -0.075) moderate0.079(0.059 -0.098) severe0.244(0.225 -0.264) extreme0.298(0.278 -0.317) Anxiety/depressionslight0.072(0.058 -0.089) moderate0.099(0.079 -0.119) severe0.282(0.263 -0.298) extreme0.282(0.267 -0.300) Key elements of modelling: 20 parameter model ‘hybrid’ model of TTO and DC data values at -1 treated as censored

15. Royal Statistical Society June 18th 2015 15 EQ-5D-5L value set for EnglandExample: the value for health state 23245 constant1.000Constant =1.003 Mobility = 20.057Minus MO level 2 -0.057 Mobility = 30.074 Mobility = 40.207 Mobility = 50.255 Self care = 20.059 Self care = 30.083Minus SC level 3 -0.083 Self care = 40.176 Self care = 50.208 Usual activities = 20.048Minus UA level 2 -0.048 Usual activities = 30.067 Usual activities = 40.165 Usual activities = 50.165 Pain/discomfort = 20.059 Pain/discomfort = 30.079 Pain/discomfort = 40.245Minus PD level 4 -0.245 Pain/discomfort = 50.298 Anxiety/depression = 20.072 Anxiety/depression = 30.099 Anxiety/depression = 40.282 Anxiety/depression = 50.282Minus AD level 5 -0.282 State 23245 = 0.288 EQ-5D-5L values for England: a worked example

16. Royal Statistical Society June 18th 2015 16 6. Statistical issues re: use of weights Generic PROs like EQ-5D-5L use ‘utilities’ to summarise data i.e weighting dimensions/levels. Condition specific PROs usually use ‘scores’ – a simple summing up of points for each item There is no ‘neutral’ way of summarising patients’ PRO data. The weights are used introduce an exogenous source of variance into statistical inference Parkin D, Rice N, Devlin N. (2010) Statistical analysis of EQ-5D profiles: does the use of value sets bias inference? Medical Decision Making) Judgements made by researchers about which data to include/exclude, how to model the value sets, can have a non- trivial impact on the weights.

17. Royal Statistical Society June 18th 2015 17 7. Normative issues re: use of weights Current approaches to weighting EQ-5D are driven by the requirements of economic evaluation/QALYs Who – usually ‘the general public’ (apart from Sweden, which prefers ‘experience based utilities’ from patients. How – ‘utility’-based approaches (but what underlying theory is relevant is disputable) SG = expected utility theory; TTO = Hicks utility theory; DCE = random utility theory; VAS? Other methods; other theories (eg minimisation of regret? Prospect theory?)

18. Royal Statistical Society June 18th 2015 18 Concluding remarks The QoL weights for PROs like EQ-5D have been dictated by the requirements of cost effectiveness analysis i.e. estimation of QALYs. The weights are sensitive to decisions made by researchers about how to model stated preference data. The weights are often used, in other applications, to summarise PRO data, because it is convenient. But results will be effected by the characteristics of the value sets/weights used. Develop and promulgate other ways of summarising PRO data, and encourage sensitivity analysis.