MÓNICA HERNÁNDEZ ALAVA AND ALLAN

WAILOOHealth Economics and Decision Science, School of Health and Related Research (ScHARR), University of Sheffield, UK

Email: m.hernandez@sheffield.ac.ukWeb: 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•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

Dataset•US not for profit observational database•6 monthly assessments from adults with diagnosis of RA•N=103,867 from 16k patients

Conclusions•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

MethodsWe have previously developed a bespoke statistical model for EQ-5D

Two key elements:1)A distribution that is limited above at 1, below at -0.561 and has a gap between 1 and 0.8832)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

•All models estimate EQ-5D (UK tariff) as a function of HAQ, pain and other patient covariates

ResultsLinear model – underestimates at top, overestimates at bottom4 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

Fig 1: Distribution of data

Fig 2: Mean EQ-5D vs pain: observed and predicted

Fig 3: Simulations a-d) 4 components of mixture, e) mixture combined, f) linear model

Table 1: Comparison of model fit