Workshop on Missing Data in Quality of Life Research in Cancer Clinical Trials : Practical and Methodological Issues. Bad Horn, CHE, 1996/07/01.
Analysing quality of life (QOL) data may be complicated for several reasons, such as :
repeated measures are obtained ;
data may be collected on ordered categorical responses ;
the instrument may have multidimensional scales, and complete data may not be available for all patients.
In addition, it may be necessary to integrate QOL with length of life.
The major undesirable effects of missing data, in QOL research, are the introduction of biases due to inadequate modes of analysis and the loss of efficiency due to reduced sample sizes.
Currently, there is no standard method for handling missing data in QOL studies.
In fact, there are very few references to methods of handling missing data in this context.
The aim of this paper is to provide an overview of methods for analysing incomplete longitudinal QOL data which have either been presented in the QOL literature or in the missing data literature.
These methods of analysis include complete case, available case, summary measures, imputation and likelihood-based approaches.
We also discuss the issue of bias and the need for sensitivity analyses.
Mots-clés Pascal : Donnée manquante, Distribution aléatoire, Essai clinique, Information incomplète, Qualité vie, Erreur systématique, Article synthèse, Méthode analyse, Distribution longitudinale, Méthode statistique
Mots-clés Pascal anglais : Missing data, Random distribution, Clinical trial, Incomplete information, Quality of life, Bias, Review, Analysis method, Longitudinal distribution, Statistical method
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0228439
Code Inist : 002B30A01A2. Création : 11/09/1998.