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01-04-2016 | Uitgave 4/2016

Quality of Life Research 4/2016

Generic and disease-specific estimates of quality of life in macular degeneration: mapping the MacDQoL onto the EQ-5D-3L

Quality of Life Research > Uitgave 4/2016
Padraig Dixon, Helen Dakin, Sarah Wordsworth
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Electronic supplementary material

The online version of this article (doi:10.​1007/​s11136-015-1145-x) contains supplementary material, which is available to authorized users.



The macular degeneration quality of life (MacDQoL) instrument is a validated condition-specific measure of quality of life in patients with macular degeneration. This paper presents the first mapping algorithm to predict EQ-5D from responses to the MacDQoL instrument.


Responses to the MacDQoL and EQ-5D-3L instruments from 482 patients were collected from the IVAN multicentre trial of two alternative drug treatments for neovascular age-related macular degeneration. Regression specifications were estimated using OLS, censored least absolute deviation, Tobit and two-part models. Their predictive performance was assessed using mean squared error. An internal validation sample based on a random selection of 25 % of patients was used to assess the performance of the model estimated on the remaining 75 % of patients.


A two-part model had the best predictive performance on the full sample. The covariates of this model include responses and weighted impact scores for all 23 condition-specific domains of the MacDQoL, and responses to a general MacDQoL quality of life question. The selected models were successful at predicting means and standard deviations of target populations, but prediction is weaker at the upper and lower extremes of the EQ-5D-3L distribution.


The mapping algorithms provide a means of predicting EQ-5D-3L index scores from MacDQoL scores, and could facilitate cost-effectiveness analyses when the latter but not the former are available to researchers. Further validation of the performance of the algorithms using external data would provide a means of establishing the robustness of the algorithms.

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