Longitudinal quality of life data: a comparison of continuous and ordinal approaches.

ABSTRACT

PURPOSE: In cancer clinical trials, health-related quality of life (HRQoL) is a major outcome measure. It is generally assessed at specified time intervals by filling out a questionnaire with ordered response categories. Despite recent advances in the statistical methodology for handling ordinal longitudinal outcome data, most users keep treating HRQoL scales as continuous rather than ordinal variables regardless of the number of categories. The purpose of this study was to compare the results of analyzing HRQoL longitudinal data under both approaches, continuous and ordinal. METHODS: The EORTC QLQ-C30 scores of two EORTC randomized brain cancer clinical trials (26951 and 26981) were analyzed using the two approaches. In the 26951 trial, a total of 368 patients were randomly assigned to receive either radiotherapy (RT) or the same RT plus procarbazine, CCNU, and vincristine. In the 26981 trial, 573 patients were randomly allocated to RT or RT plus temozolomide. Comparison of the two treatment arms was done using methods for longitudinal quantitative and longitudinal ordinal data. Both statistical methods were adapted to account for missing data and compared in terms of statistical significance of the results (p values) but also with respect to data interpretation. RESULTS: Three scales, i.e., appetite loss, insomnia, and drowsiness, presenting four response categories ("Not at all", "A little", "Quite a bite", and "Very much") were analyzed in each trial. Both statistical methods (continuous and ordinal) showed statistically significant differences between the two treatments, not only globally but also at the same assessment time points. The magnitude of the p values, however, varied at some time points and was less pronounced in the ordinal approach. CONCLUSIONS: The analysis of the two clinical trials showed that treating the HRQoL scales by a quantitative or an ordinal method did not make much difference as far as statistical significance was concerned. The interpretation of results, however, was easier under the ordinal approach. Treatment effects may be more meaningful when expressed in terms of odds ratios than as mean values, particularly when the number categories is small.