Model-based methodology for analyzing incomplete quality-of-life data and integrating them into the Q-TWiST framework.
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.


CONCLUSIONS
Our model-based approach is useful to analyze studies with incomplete QOL data, especially when approximate QOL assessment by health state is not possible.
Mounier
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Groupe d'Etude des Lymphomes de l'Adulte (GELA), Institut d'Hématologie, Hôpital Saint Louis, AP-HP, Paris, France. nicolas.mounier@sls.ap-hop-paris.fr
Ferme
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C
Flechtner
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Henzy-Amar
M
M
Lepage
E
E
D000328:Adult; D000367:Age Factors; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D006304:Health Status; D006689:Hodgkin Disease / Q000188:drug therapy / Q000401:mortality; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D016019:Survival Analysis