Analysing quality of life (QOL) data may be complicated for several re
asons, such as: repeated measures are obtained; data may be collected
on ordered categorical responses; the instrument may have multidimensi
onal 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 t
he 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, th
ere are very few references to methods of handling missing data in thi
s 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. The
se 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. (C) 1998 John
Wiley & Sons, Ltd.