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Quality of Life Research
Published in: Quality of Life Research 4/2016

01-04-2016

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

Authors: Padraig Dixon, Helen Dakin, Sarah Wordsworth

Published in: Quality of Life Research | Issue 4/2016

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.
Appendix
Available only for authorised users
Footnotes
1
Impact response categories are answered in response to questions such as ‘If I did not have macular degeneration, friendships and social life would be…’. Typical categories are ‘very much better' [scoring −3], ‘much better' [scoring −2], ‘a little better' [scoring −1], ‘the same' [scoring 0], and ‘worse’ [scoring +1].
 
2
Importance response categories are answered in response to questions such as ‘My friendships and social life are…’ with response categories ‘very important' [scoring 3], ‘important' [scoring 2], ‘somewhat important' [scoring 1], ‘not at all important’ [scoring 0].
 
Literature
1.
Longworth, L., & Rowen, D. (2013). Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value in Health, 16(1), 202–210.CrossRefPubMed
2.
Tosh, J., Brazier, J., Evans, P., & Longworth, L. (2012). A review of generic preference-based measures of health-related quality of life in visual disorders. Value in Health, 15(100883818), 118–127.CrossRefPubMedPubMedCentral
3.
Dakin, H. (2013). Review of studies mapping from quality of life or clinical measures to EQ-5D: An online database. Health and Quality of Life Outcomes, 11, 6.CrossRef
4.
Brazier, J., & Dixon, S. (1995). The use of condition specific outcome measures in economic appraisal. Health Economics, 4(4), 255–264.CrossRefPubMed
5.
Longworth, L., Yang, Y., Young, T., Mulhern, B., Hernández Alava, M., Mukuria, C., et al. (2014). Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: A systematic review, statistical modelling and survey. Health Technology Assessment, 18(9), 1.CrossRefPubMedPubMedCentral
6.
Usha, C., Jennifer, E., & Philip, J. R. (2010). Age related macular degeneration. BMJ, 340, c981.CrossRef
7.
Woo, J. H., Sanjay, S., & Au Eong, K.-G. (2009). The epidemiology of age-related macular degeneration in the Indian subcontinent. Acta Ophthalmologica, 87(3), 262–269.CrossRefPubMed
8.
Owen, C. G., Jarrar, Z., Wormald, R., Cook, D. G., Fletcher, A. E., & Rudnicka, A. R. (2012). The estimated prevalence and incidence of late stage age related macular degeneration in the UK. British Journal of Ophthalmology, 96(5), 752–756.CrossRef
9.
Mitchell, J., & Bradley, C. (2004). Design of an individualised measure of the impact of macular disease on quality of life (the MacDQoL). Quality of Life Research, 13(6), 1163–1175.CrossRefPubMed
10.
Mitchell, J., Wolffsohn, J., Woodcock, A., Anderson, S., McMillan, C., Rubinstein, M., et al. (2005). + Psychometric evaluation of the MacDQoL individualised measure of the impact of macular degeneration on quality of life. Health and Quality of Life Outcomes, 3(1), 25.CrossRefPubMedPubMedCentral
11.
Mitchell, J., Wolffsohn, J., Woodcock, A., Anderson, S. J., Ffytche, T., Rubinstein, M., et al. (2008). The MacDQoL individualized measure of the impact of macular degeneration on quality of life: Reliability and responsiveness. American Journal of Ophthalmology, 146(3), 447.e442–454.e442.CrossRef
12.
Berdeaux, G., Mesbah, M., & Bradley, C. (2011). Metric properties of the MacDQoL, individualized macular-disease-specific quality of life instrument, and newly identified subscales in French, German, Italian, and American populations. Value in Health, 1(1524-4733 (Electronic)), 10.
13.
Chakravarthy, U., Harding, S. P., Rogers, C. A., Downes, S. M., Lotery, A. J., Wordsworth, S., & Reeves, B. C. (2012). Ranibizumab versus bevacizumab to treat neovascular age-related macular degeneration: One-year findings from the IVAN randomized trial. Ophthalmology, 119(7), 1399–1411.CrossRefPubMed
14.
Chakravarthy, U., Harding, S. P., Rogers, C. A., Downes, S. M., Lotery, A. J., Culliford, L. A., & Reeves, B. C. (2013). Alternative treatments to inhibit VEGF in age-related choroidal neovascularisation: 2-year findings of the IVAN randomised controlled trial. The Lancet, 382, 1258.CrossRef
15.
van Reenen, M., & Oppe, M. (2015). EQ-5D-3L user guide: Basic information on how to use the EQ-5D-3L instrument version 5.1. Rotterdam: EuroQol Research Foundation.
16.
NICE. (2013). Guide to the methods of technology appraisal. Manchester: The National Institute for Health and Care Excellence.
17.
Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35(11), 12.CrossRef
18.
Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., & Gray, A. (2015). Preferred reporting items for studies mapping onto preference-based outcome measures: The MAPS statement. Quality of Life Research, 30, 1–7.
19.
Wooldridge, J. M. (2009). Introductory econometrics: A modern approach. Mason: South Western, Cengage Learning.
20.
Pullenayegum, E. M., Tarride, J.-E., Xie, F., Goeree, R., Gerstein, H. C., & O’Reilly, D. (2010). Analysis of health utility data when some subjects attain the upper bound of 1: Are Tobit and CLAD models appropriate? Value in Health, 13(4), 487–494.CrossRefPubMed
21.
Powell, J. L. (1984). Least absolute deviations estimation for the censored regression model. Journal of Econometrics, 25(3), 303–325.CrossRef
22.
Jolliffe, D., Krushelnytskyy, B., & Semykina, A. (2000, November). Censored least absolute deviations estimator: CLAD. Stata Technical Bulletin, 58, 13–16.
23.
Longworth, L., & Rowen, D. (2011). NICE DSU technical support document 10: The use of mapping methods to estimate health state utility values. Decision Support Unit, ScHARR, University of Sheffield.
24.
Browne, C., Brazier, J., Carlton, J., Alavi, Y., & Jofre-Bonet, M. (2012). Estimating quality-adjusted life years from patient-reported visual functioning. Eye, 26(10), 1295–1301.CrossRefPubMedPubMedCentral
25.
Sengupta, N., Nichol, M. B., Wu, J., & Globe, D. (2004). Mapping the SF-12 to the HUI3 and VAS in a managed care population. Medical Care, 42(9), 927–937.CrossRefPubMed
26.
Brazier, J., Yang, Y., Tsuchiya, A., & Rowen, D. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European Journal of Health Economics, 11(2), 215–225.CrossRefPubMed
27.
Payakachat, N., Summers, K., Pleil, A., Murawski, M., Thomas, J., Jennings, K., et al. (2009). Predicting EQ-5D utility scores from the 25-item National Eye Institute Vision Function Questionnaire (NEI-VFQ 25) in patients with age-related macular degeneration. Quality of Life Research, 18(9210257, bqm), 801–813.
28.
Dakin, H., Gray, A., & Murray, D. (2013). Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score. Quality of Life Research, 22(3), 683–694.CrossRefPubMedPubMedCentral
29.
Brazier, J., Connell, J., Papaioannou, D., Mukuria, C., Mulhern, B., Peasgood, T., et al. (2014). A systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures. Health Technology Assessment, 18(34), 1.CrossRefPubMedPubMedCentral
30.
Ara, R., & Brazier, J. E. (2010). Populating an economic model with health state utility values: Moving toward better practice. Value in Health, 13(5), 509–518.CrossRefPubMed
31.
IVAN Study Team. (2011). IVAN study protocol V7.0: NIHR technology assessment programme.
32.
Gray, A. M., Rivero-Arias, O., & Clarke, P. M. (2006). Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Medical Decision Making, 26(1), 18–29.CrossRefPubMed
Metadata
Title
Generic and disease-specific estimates of quality of life in macular degeneration: mapping the MacDQoL onto the EQ-5D-3L
Authors
Padraig Dixon
Helen Dakin
Sarah Wordsworth
Publication date
01-04-2016
Publisher
Springer International Publishing
Published in
Quality of Life Research / Issue 4/2016
Print ISSN: 0962-9343
Electronic ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-015-1145-x

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