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

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

Medical Decision Making, 2003, vol. 23, issue 1, 54-66

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.

Date: 2003
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:23:y:2003:i:1:p:54-66

DOI: 10.1177/0272989X02239650

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