Abstract
BackgroundThe use of health-related quality of life (HRQoL) as an endpoint in cancer clinical trials is growing rapidly. Hence, research into the statistical approaches used to analyze HRQoL data is of major importance, and could lead to a better understanding of the impact of treatments on the everyday life and care of patients. Amongst the models that are used for the longitudinal analysis of HRQoL, we focused on the mixed models from item response theory, to directly analyze raw data from questionnaires.MethodsWe reviewed the different item response models for ordinal responses, using a recent classification of generalized linear models for categorical data. Based on methodological and practical arguments, we then proposed a conceptual selection of these models for the longitudinal analysis of HRQoL in cancer clinical trials.ResultsTo complete comparison studies already present in the literature, we performed a simulation study based on random part of the mixed models, so to compare the linear mixed model classically used to the selected item response models. As expected, the sensitivity of the item response models to detect random effects with lower variance is better than that of the linear mixed model. We then used a cumulative item response model to perform a longitudinal analysis of HRQoL data from a cancer clinical trial.ConclusionsAdjacent and cumulative item response models seem particularly suitable for HRQoL analysis. In the specific context of cancer clinical trials and the comparison between two groups of HRQoL data over time, the cumulative model seems to be the most suitable, given that it is able to generate a more complete set of results and gives an intuitive illustration of the data.
Highlights
The use of health-related quality of life (HRQoL) as an endpoint in cancer clinical trials is growing rapidly
Simulation study In the previous section, we focused on the use of mixed models for ordinal data analysis, and discussed their relevance in the HRQoL analysis in oncology
Even if the linear mixed model (LMM) take into account the HRQoL score, which is a summary variable, this approach is at least equivalent to the item response theory (IRT) models in terms of power
Summary
The use of health-related quality of life (HRQoL) as an endpoint in cancer clinical trials is growing rapidly. EORTC QLQ-C30 is composed of 30 ordinal items assessing several dimensions of HRQoL: the global health status (GHS), five functional dimensions (physical, role, cognitive, emotional and social), four multi-item symptomatic dimensions (fatigue, pain, nausea and vomiting, loss of appetite), and five single item symptomatic dimensions (diarrhea, constipation, insomnia, dyspnea and perceived financial impact) It is completed by the patients themselves, and collected at different time points defined in the trial protocol (usually at inclusion, during treatment and at follow-up). In a Likert scale, the gap which separates each adjacent category of response (“not at all”, “a little”, “quite a bit” and “very much”) may not be the same, and the calculation used to generate a HRQoL score does not take this characteristic into account Another drawback to the HRQoL score is that subjects could have different item outcomes and obtain the same score. The score does not make a distinction between these subjects [6]
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