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Response mapping to the EQ-5D: methods and comparative performance

Response mapping to the EQ-5D: methods and comparative performance. Oliver Rivero-Arias, Alastair Gray and Helen Dakin iHEA organised session 9 th July 2013 helen.dakin @dph.ox.ac.uk. Introduction to response mapping.

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Response mapping to the EQ-5D: methods and comparative performance

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  1. Response mapping to the EQ-5D: methods and comparative performance Oliver Rivero-Arias, Alastair Gray and Helen Dakin iHEAorganised session 9th July 2013 helen.dakin@dph.ox.ac.uk

  2. Introduction to response mapping • In response mapping, 5 categorical models (e.g. mlogit) predict the probability or odds that a participant is at level j on each EQ-5D domain • Combine predicted responses with tariff to estimate utilities • Advantages: • Response mapping algorithm can be used with any national EQ-5D tariff • Provides data on the distribution of patients across EQ-5D states • Gives insights into the nature of the relationship between instruments • May give better utility predictions by reflecting the distribution better • Disadvantages: • Models are more complicated to estimate: larger sample size needed? • More complicated to estimate utilities from predicted probabilities

  3. 3 methods to calculate EQ-5D utilities from predicted probabilities Pain/discomfort Self care Anxiety/depression Mobility Usual activities • Highest probability: Assume patient is at the level with the highest predicted probability • Monte Carlo: Randomly assign patients to one level • Expected value: Multiply predicted the probabilities with tariff values to obtain expected utility Highest prob: 1 2 1 -.2*.104 -.1*.214 2 1 1 -.4*.036 -.32*.094 2 3 2 -.35*.123 -.3*.386 2 1 1 -.32*.071 -.12*.236 2 1 1 =0.883 =0.760 =0.312 =0.305 1-.3*.069 -.17*.314 Monte Carlo 1: Expected value: 2: -.17*.1*.32*.3*.12*.269 -(1-(1-.53)*(1-.7)*(1-.27)*(1-.35)*(1-.66)*.081

  4. Pros and cons of methods to calculate EQ-5D utilities • Highest probability • Underestimates % of patients in rare health states (e.g. level 3)  Overestimates predicted utility • Monte Carlo (MC) • ≥1000 draws normally needed • Some studies use only one draw random variability • Expected value (EV) • Equivalent to Monte Carlo with infinite draws • Gives exact result instantly with one equation

  5. Aims • To review how response mapping to EQ-5D has been used to date • To compare the performance of response mapping with other methods

  6. Methods • A systematic review was conducted to identify studies using response mapping to predict EQ-5D responses from responses/scores on other QoL instruments • Included published and available unpublished studies • Searched Medline, Centre for Reviews and Dissemination (CRD), the Health Economists’ Study Group (HESG) website and HERC database of mapping studies (http://www.herc.ox.ac.uk/downloads/mappingdatabase) • Extracted data on: • Source instrument, models estimated • How prediction accuracy varied between models • Methods used to calculate utilities from predicted probabilities • Highest probability, expected value (EV) or Monte Carlo (MC)

  7. Characteristics of studies identified • 21 studies identified • Source instrument: SF-12 in 4 studies; EQ-5D-5L in 1 & disease-specific in 16 • 75% (6/8) of studies found predictions errors from both direct & response mapping higher for patients with utilities <0.5 than those with good health

  8. Modelling methods used • OLS was most common direct mapping method • Multinomial logit was most common response mapping model Direct mapping models Response mapping models Bayesian networks: 2 Mlogit: 14 CLAD: 7 11 2 Other: 5 3 2 2 1 3 2 9 Oprobit or generalised oprobit: 2 2 1 2 Ologit: 6 1 2-part: 5 OLS or GLS: 20 Cross-tabulation: 1

  9. Methods for estimating utilities • Monte Carlo, expected value and highest probability methods all commonly used to estimate utilities • 3 studies compared different methods Expected value: 9 Monte Carlo (n=1 or 11): 7 6 1 6 1 1 2 Highest probability: 5 Monte Carlo (n>100): 3

  10. Relative prediction accuracy • 55% of studies using EV or ≥100 Monte Carlo found response mapping gave similar or more accurate prediction errors • V.s 17% of those using highest probability or ≤11 Monte Carlo draws Predictions from response mapping are: Highest probability or ≤11 Monte Carlo Expected value or ≥100 Monte Carlo

  11. Conclusions • Response mapping and direct mapping have similar prediction errors when EV or MC (n>100) are used to calculate utilities • Highest probability method and n=1 Monte Carlo should be avoided • All mapping models perform poorly in patients with poor QoL • Response mapping has additional advantages • Insights into relationships between instruments • Gives domain-level predictions • Can be used with any EQ-5D valuation tariff • Distribution of Excel or Stata commands to estimate predictions can simplify the process for users • mrs2eq and oks2eq commands accepted for publication in Stata Journal

  12. Acknowledgements • Many thanks to Jason Madan, Kamran Kahn, Aki Tsuchiya and Richard Eldin for allowing us to cite their unpublished work

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