ABSTRACT
Quality of life (QOL) evaluated by patients themselves has become one of the important outcomes in clinical practice as well as clinical trials. Recently clinicians have attempted to gather QOL evaluation data in their clinical practice setting and integrate the findings into the medical decision-making process. To date, several multidimensional generic questionnaires consisting of multiple domains such as functional, physical, mental and social well-being, have been developed and utilized for generic QOL evaluation in clinical trials, especially in the oncology area. To develop a well-constructed and valid QOL questionnaire, its psychometric characteristics such as reliability, validity, responsiveness and feasibility must be adequately assessed in the research setting.<BR>In clinical trials, QOL data are generally measured in a longitudinal fashion and there are two prominent embarrassing statistical problems : one is the multiplicity due to replication (in time) of statistical tests and the other is the occurrence of missing data due to a variety of reasons. Non-random missing data which occurs because of any reasons related to a patient's present status and/or future prognosis possibly leads to bias and misinterpretation of the results of a trial. To solve the multiplicity problem, the repeated-measures ANOVA-type data analysis or summarization of a repeated measures into an appropriate summary measure can be applied. Missing data can be prevented to some extent by allocating/training coordinators at each participating institute and establishing a communication network between a data center and participating institutes. However, missing data will occur inevitably due to the deterioration of a patient's physical status in the area of life threatening diseases suchas advanced cancer or other diseases with poor prognosis. Although several statistical approaches to cope with missing data even including non-random one have been proposed, there is no single complete analytical solution that can handle the non-random missing problem. The best remedy would be to collect information about reasons why the missing data occurred so that we can identify the missing mechanism and take it into account in a statistical analysis. A so-called “sensitivity analysis” of comparing the results of several analytical methods suchas different imputation techniques or newly proposed ideas would also be a useful approach. The QALY (Quality Adjusted Life Year) used the idea of weighting life time by utility evaluated by patients themselves and is coined for incorporating a patient's judgment into the treatment selection. Ultimately, an assessment of QOL should be utilized for “individualized” or “tailor-made” treatment and statistical methodology should be developed further for gathering, analyzing and utilizing QOL data.