Response mapping to the eq 5d methods and comparative performance
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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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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

iHEAorganised session 9th July 2013

[email protected]


Introduction to response mapping

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 methods to calculate eq 5d utilities from predicted probabilities

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


Pros and cons of methods to calculate eq 5d utilities

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


Response mapping to the eq 5d methods and comparative performance

Aims

  • To review how response mapping to EQ-5D has been used to date

  • To compare the performance of response mapping with other methods


Methods

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)


Characteristics of studies identified

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


Modelling methods used

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


Methods for estimating utilities

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


Relative prediction accuracy

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


Conclusions

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


Acknowledgements

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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