  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.
