Workshop on Missing Data in Quality of Life Research in Cancer Clinical Trials : Practical and Methodological Issues. Bad Horn, CHE, 1996/07/01.
There is increasing interest in measuring health related quality of life in cancer clinical trials.
Most quality of life data are measured repeatedly over a fixed time schedule to capture changes and to reflect relative advantages of study treatments.
A multivariate repeated measures model is usually used to analyse this type of data.
However, one of the difficulties of this analysis is that quality of life may be affected by the occurrence of some critical events experienced by patients.
We may separate a patient's lifetime during study into different'health states'The duration of these health states may vary among patients, and may relate to the efficacy of the study treatment.
In some cases quality of life data may be missing due to one of the many different types of missing data mechanisms specific for a health state.
It is reasonable to assume that the missing data mechanism for a treatment arm is homogeneous within a defined health state, and to control for the potential confounding effect to appropriately assess the impact of treatment on the quality of life.
In this paper, we propose a growth curve model conditional on a time-dependent variable of defined health states in order to assess the overall treatment effect while taking into account occurrences of missing data and measurements from irregular visits. (...)
Mots-clés Pascal : Qualité vie, Essai clinique, Cancérologie, Courbe croissance, Modélisation, Mesure répétée, Analyse multivariable, Analyse donnée, Homme, Tumeur maligne, Donnée manquante
Mots-clés Pascal anglais : Quality of life, Clinical trial, Cancerology, Growth curve, Modeling, Repeated measurement, Multivariate analysis, Data analysis, Human, Malignant tumor, Missing data
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Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0228440
Code Inist : 002B30A01A2. Création : 11/09/1998.