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Model-Based Methodology for Analyzing Incomplete Quality-of-Life Data and Integrating Them into the Q-Twist Framework

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  • N. Mounier
  • C. Ferme
  • H. Flechtner
  • M. M. Henry-Amar
  • E. Lepage

Abstract

Background . The standard Q-TWiST approach defines a series of health states and weights each state’s duration according to its quality of life (QOL) to calculate quality-adjusted lifetimes. However, a fixed weight may not adequately reflect time variations in QOL. Methods . To account for measurements derived from irregular visits and informative missing data, the authors estimated the mean QOL profile using a mixed-effect growth curve model for the response, combined with a logistic regression model for the drop-out process. Results . Using data from a clinical study of lymphoma patients, the authors demonstrated better readaptation to normal life for patients younger than 30. Sensitivity analyses and computer simulations demonstrated that modeling the drop-out probability as a function of the QOL measurements is necessary if conditioning by health state is not possible. Conclusion . Our model-based approach is useful to analyze studies with incomplete QOL data, especially when approximate QOL assessment by health state is not possible.

Suggested Citation

  • N. Mounier & C. Ferme & H. Flechtner & M. M. Henry-Amar & E. Lepage, 2003. "Model-Based Methodology for Analyzing Incomplete Quality-of-Life Data and Integrating Them into the Q-Twist Framework," Medical Decision Making, , vol. 23(1), pages 54-66, January.
    • Handle: RePEc:sae:medema:v:23:y:2003:i:1:p:54-66
    DOI: 10.1177/0272989X02239650
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