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PharmacoEconomics

, Volume 35, Issue 1, pp 111–124 | Cite as

Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs)

  • Billingsley Kaambwa
  • Gang Chen
  • Julie Ratcliffe
  • Angelo Iezzi
  • Aimee Maxwell
  • Jeff Richardson
Original Research Article

Abstract

Purpose

Economic evaluation of health services commonly requires information regarding health-state utilities. Sometimes this information is not available but non-utility measures of quality of life may have been collected from which the required utilities can be estimated. This paper examines the possibility of mapping a non-utility-based outcome, the Sydney Asthma Quality of Life Questionnaire (AQLQ-S), onto five multi-attribute utility instruments: Assessment of Quality of Life 8 Dimensions (AQoL-8D), EuroQoL 5 Dimensions 5-Level (EQ-5D-5L), Health Utilities Index Mark 3 (HUI3), 15 Dimensions (15D), and the Short-Form 6 Dimensions (SF-6D).

Methods

Data for 856 individuals with asthma were obtained from a large Multi-Instrument Comparison (MIC) survey. Four statistical techniques were employed to estimate utilities from the AQLQ-S. The predictive accuracy of 180 regression models was assessed using six criteria: mean absolute error (MAE), root mean squared error (RMSE), correlation, distribution of predicted utilities, distribution of residuals, and proportion of predictions with absolute errors <0.0.5. Validation of initial ‘primary’ models was carried out on a random sample of the MIC data.

Results

Best results were obtained with non-linear models that included a quadratic term for the AQLQ-S score along with demographic variables. The four statistical techniques predicted models that performed differently when assessed by the six criteria; however, the best results, for both the estimation and validation samples, were obtained using a generalised linear model (GLM estimator).

Conclusions

It is possible to predict valid utilities from the AQLQ-S using regression methods. We recommend GLM models for this exercise.

Keywords

Root Mean Square Error Generalise Linear Model Ordinary Little Square Validation Sample Mean Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Compliance with Ethical Standards

Funding

This work was supported through an Australian National Health and Medical Research Council (NHMRC) project Grant (Grant Number 1006334).

Contribution of authors

Jeff Richardson contributed to the study inception and writing of the NHMRC grant application. Billingsley Kaambwa analysed the data, interpreted the results, and wrote the first draft of the manuscript. Julie Ratcliffe, Gang Chen, Angelo Iezzi, Aimee Maxwell, and Jeff Richardson contributed to the interpretation of results and revision of the manuscript. All authors have read and approved the final manuscript. Billingsley Kaambwa is the guarantor of the manuscript.

Conflict of interest

Billingsley Kaambwa, Gang Chen, Julie Ratcliffe, Angelo Iezzi, Aimee Maxwell, and Jeff Richardson declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval was granted by the MUHREC (CF11/3192–2011001748).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

40273_2016_446_MOESM1_ESM.docx (93 kb)
Supplementary material 1 (DOCX 93 kb)

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Billingsley Kaambwa
    • 1
  • Gang Chen
    • 2
  • Julie Ratcliffe
    • 1
  • Angelo Iezzi
    • 2
  • Aimee Maxwell
    • 2
  • Jeff Richardson
    • 2
  1. 1.Flinders Health Economics GroupFlinders University, A Block, Repatriation General HospitalAdelaideAustralia
  2. 2.Centre for Health Economics, Building 75, 15 Innovation WalkMonash UniversityClaytonAustralia

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