Quality-of-life data are generally collected longitudinally in cancer clinical trials. Often the data collection is most intense during the treatment phase of the study when patients have frequent clinic visits (e.g., 1-month intervals). Posttreatment measurements are generally collected at longer intervals corresponding to follow-up visits or are obtained using mailed surveys (e.g., 6-month intervals). Therefore, a longitudinal analysis procedure is appropriate. Standard methods include repeated-measures analysis of variance or more general mixed effects regression models. Other techniques include growth curve models and the construction of summary measures.
The main difficulty in analyzing longitudinal quality-of-life data is the appropriate handling of missing observations. Observations may be missing for many reasons, some of which may be considered missing at random, whereas others are related to the quality of life the patient is experiencing. For example, a patient may be too ill to complete the questionnaire, or the patient may be doing so well that he or she does not visit the clinic at the time when a quality-of-life assessment is due.
As with most statistical analyses, a graphical display of the data is a useful starting point. A common display for longitudinal quality-of-life data consists of a plot of mean scores over time according to treatment group for each quality-of-life domain measured. It is useful to indicate on these graphs which assessment time points occurred during the treatment phase of the study. It is also useful to indicate how many subjects provided data at each time point. These summaries will help to guide the modeling of the data and the interpretation of the modeling results.
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