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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-09-26

AUTHORS

Antoine Barbieri, Jean Peyhardi, Thierry Conroy, Sophie Gourgou, Christian Lavergne, Caroline Mollevi

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. More... »

PAGES

148

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12874-017-0410-9

DOI

http://dx.doi.org/10.1186/s12874-017-0410-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091928272

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28950850


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