Related Experiment Videos
Model-based methodology for analyzing incomplete quality-of-life data and integrating them into the Q-TWiST framework
N Mounier1, C Ferme, H Flechtner
1Groupe 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
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.