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MÓNICA HERNÁNDEZ ALAVA AND ALLAN WAILOO
Health Economics and Decision Science, School of Health and
Related Research (ScHARR), University of Sheffield, UK
Email: [email protected]
Web: www.shef.ac.uk/scharr/sections/heds/staff/hernandez_m
Estimating EQ-5D health state values for Rheumatoid Arthritis
patients: a limited dependent variable, mixture modelling
approach
Introduction
Methods
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We have previously developed a
bespoke statistical model for EQ-5D
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Clinical trials often do not contain
preference
based
outcome
measure
Cost per QALY evaluation
requires such outcomes
Linking clinical to preference
based outcomes via statistical
model
is commonplace
in
economic evaluation
22% of NICE appraisals include
“mapping”, 100% in RA
Typically done using simple
regression techniques
EQ-5D has an odd distribution
Standard methods perform badly
Develop and refine a new
method here
Two key elements:
1) A distribution that is limited above
at 1, below at -0.561 and has a
gap between 1 and 0.883
2) Mixture model – this is a blend of
several different models, all based
on the distribution in 1).
• This approach provides a flexible
statistical approach, that reflects
the key features of EQ-5D.
• Compare to the standard linear
model
• Using measures of fit and
penalised likelihood
• Simulated values from models
compared to original data
Fig 1: Distribution of data
Dataset
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US not for profit observational
database
6 monthly assessments from
adults with diagnosis of RA
N=103,867 from 16k patients
Results
• All models estimate EQ-5D (UK
tariff) as a function of HAQ, pain
and other patient covariates
Fig 2: Mean EQ-5D vs pain: observed and predicted
Linear model – underestimates at top, overestimates at bottom
4 class mixture model – fits well across the range. Substantial improvement over linear model
Simulations show linear model outside feasible range, mixture reflects distribution of original data
Table 1: Comparison of model fit
Conclusions
Fig 3: Simulations a-d) 4 components of mixture, e) mixture combined, f)
linear model
• Linear models are not appropriate for EQ-5D and lead to biased estimates of treatment benefits when used in
economic evaluation. The bespoke mixture model is appropriate:
• Better fit overall, no systematic bias, no prediction outside feasible range
• More complex to fit, slightly more difficult to incorporate into CE model
• But it matters! Example:
• Mixture model: HAQ 0 to 2.5 = 0.57 QALY
• NICE appraisals linear model = 0.82 QALY