The importance of using ordinal scores for patient classification based on health-related quality of life trajectories.

Abstract

Changes in health-related quality of life (HRQoL) over time are not necessarily homogeneous within a population of interest. Our study aim was twofold: to determine homogeneous patient subpopulations distinguished by HRQoL trajectories, and to identify the particular patient profile associated with each subpopulation. To classify patients according to HRQoL dimension scores, we compared mixtures of linear mixed models (LMMs) classically applied to scores defined by the EORTC procedure, and mixtures of random effect cumulative models (CMs) applied to scores treated as ordinal variables. A simulation study showed that the mixture of LMMs overestimated the number of subpopulations and was less able to correctly classify patients than the mixture of CMs. Considering HRQoL scores as ordinal rather than continuous variables is relevant when classifying patients. The mixture of CMs for ordinal scores is able to identify homogeneous subpopulations and their associated trajectories. The application focused on changes over time in HRQoL data (collected using the EORTC QLQ-C30 questionnaire) from 132 breast cancer patients from the Moral study. Once the classification is obtained only from HRQoL scores, class membership was then explained through a logistic regression model, given a large panel of variables collected at baseline. Analysis of data revealed that deterioration over time of role functioning and insomnia was closely related to patient anxiety: anxiety at baseline is a prognostic factor for a poor level and/or a deterioration over time of HRQoL. For functional dimensions, large tumor size and high education level were associated with worse HRQoL scores.