Item response models for the longitudinal analysis of health-related quality of life in cancer clinical trials

Abstract: Statistical research regarding health-related quality of life (HRQoL) is a major challenge to better evaluate the impact of the treatments on their everyday life and to improve patients' care. Among the models that are used for the longitudinal analysis of HRQoL, we focused on the mixed models from the item response theory to analyze directly the raw data from questionnaires. Using a recent classification of generalized linear models for categorical data, we discussed about a conceptual selection of these models for the longitudinal analysis of HRQoL in cancer clinical trials. Through methodological and practical arguments, the adjacent and cumulative models seem particularly suitable for this {context}. Specially in cancer clinical trials and for the comparison between two groups, the cumulative models has the advantage of providing intuitive illustrations of results. To complete the comparison studies already performed in literature, a simulation study based on random part of the mixed models is then carried out to compare the linear mixed model classically used to the discussed item response models. As expected, the sensitivity of item response models to detect random effect with lower variance is better than the linear mixed model sensitivity. Finally, a longitudinal analysis of HRQoL data from cancer clinical trial is carried out using an item response cumulative model.