Incomplete quality of life data in randomized trials: missing forms.
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.
Curran
D
D
European Organization for Research and Treatment of Cancer (EORTC) Data Center, Brussels, Belgium. dcu@eortc.be
Molenberghs
G
G
Fayers
P M
PM
Machin
D
D
D015982:Bias; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D011788:Quality of Life; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*; D009626:Terminology as Topic