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

Royal Statistical Society June 18th 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

Royal Statistical Society June 18th 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

Royal Statistical Society June 18th 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”

Royal Statistical Society June 18th 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’.

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

Royal Statistical Society June 18th 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

Royal Statistical Society June 18th 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.

Royal Statistical Society June 18th 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

Royal Statistical Society June 18th 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

Royal Statistical Society June 18th 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

Royal Statistical Society June 18th 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)

Royal Statistical Society June 18th DCE task

Royal Statistical Society June 18th England EQ-5D-5L values95% CIs constant 1.003( ) Mobilityslight0.057( ) moderate0.074( ) severe0.207( ) unable0.255( ) Self careslight0.059( ) moderate0.083( ) severe0.176( ) unable0.208( ) Usual activitiesslight0.048( ) moderate0.067( ) severe0.165( ) unable0.165( ) Pain/discomfortslight0.059( ) moderate0.079( ) severe0.244( ) extreme0.298( ) Anxiety/depressionslight0.072( ) moderate0.099( ) severe0.282( ) extreme0.282( ) Key elements of modelling: 20 parameter model ‘hybrid’ model of TTO and DC data values at -1 treated as censored

Royal Statistical Society June 18th EQ-5D-5L value set for EnglandExample: the value for health state constant1.000Constant =1.003 Mobility = Minus MO level Mobility = Mobility = Mobility = Self care = Self care = Minus SC level Self care = Self care = Usual activities = Minus UA level Usual activities = Usual activities = Usual activities = Pain/discomfort = Pain/discomfort = Pain/discomfort = Minus PD level Pain/discomfort = Anxiety/depression = Anxiety/depression = Anxiety/depression = Anxiety/depression = Minus AD level State = EQ-5D-5L values for England: a worked example

Royal Statistical Society June 18th 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.

Royal Statistical Society June 18th 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?)

Royal Statistical Society June 18th 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.