| Added by | mollevi |
|---|---|
| Group name | EquipeMY |
| Item Type | Journal Article |
| Title | Item response models for the longitudinal analysis of health-related quality of life in cancer clinical trials |
| Creator | Barbieri et al. |
| Author | Antoine Barbieri |
| Author | Jean Peyhardi |
| Author | Thierry Conroy |
| Author | Sophie Gourgou |
| Author | Christian Lavergne |
| Author | Caroline Mollevi |
| Abstract | BACKGROUND: The 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. METHODS: We 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. RESULTS: To 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. CONCLUSIONS: Adjacent 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. |
| Publication | BMC medical research methodology |
| Volume | 17 |
| Issue | 1 |
| Pages | 148 |
| Date | Sep 26, 2017 |
| Journal Abbr | BMC Med Res Methodol |
| Language | eng |
| DOI | 10.1186/s12874-017-0410-9 |
| ISSN | 1471-2288 |
| Library Catalog | PubMed |
| Extra | PMID: 28950850 PMCID: PMC5615461 |
| Tags | Algorithms, Clinical Trials as Topic, Health Status, Humans, Linear Models, Longitudinal Studies, Mixed models, Neoplasms, Ordinal data, original, Outcome Assessment (Health Care), Quality of Life, Surveys and Questionnaires |
| Date Added | 2018/11/13 - 17:25:52 |
| Date Modified | 2019/05/21 - 13:28:54 |
Institut de Recherche en Cancérologie de
