Flexible modeling of longitudinal health-related quality of life data accounting for informative dropout in a cancer clinical trial
- PMID: 36115002
- DOI: 10.1007/s11136-022-03252-6
Flexible modeling of longitudinal health-related quality of life data accounting for informative dropout in a cancer clinical trial
Abstract
Purpose: A joint modeling approach is recommended for analysis of longitudinal health-related quality of life (HRQoL) data in the presence of potentially informative dropouts. However, the linear mixed model modeling the longitudinal HRQoL outcome in a joint model often assumes a linear trajectory over time, an oversimplification that can lead to incorrect results. Our aim was to demonstrate that a more flexible model gives more reliable and complete results without complicating their interpretation.
Methods: Five dimensions of HRQoL in patients with esophageal cancer from the randomized clinical trial PRODIGE 5/ACCORD 17 were analyzed. Joint models assuming linear or spline-based HRQoL trajectories were applied and compared in terms of interpretation of results, graphical representation, and goodness of fit.
Results: Spline-based models allowed arm-by-time interaction effects to be highlighted and led to a more precise and consistent representation of the HRQoL over time; this was supported by the martingale residuals and the Akaike information criterion.
Conclusion: Linear relationships between continuous outcomes (such as HRQoL scores) and time are usually the default choice. However, the functional form turns out to be important by affecting both the validity of the model and the statistical significance.
Trial registration: This study is registered with ClinicalTrials.gov, number NCT00861094.
Keywords: Cancer; Clinical trials; Health-related quality of life; Joint model; Linear mixed model; Spline.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
References
-
- Wu, L., Liu, W., Yi, G. Y., & Huang, Y. (2011). Analysis of longitudinal and survival data: Joint modeling, inference methods, and issues. Journal of Probability and Statistics, 2012, 18. https://doi.org/10.1155/2012/640153 - DOI
-
- Ediebah, D. E., Galindo-Garre, F., Uitdehaag, B. M. J., Ringash, J., Reijneveld, J. C., Dirven, L., Zikos, E., Coens, C., van den Bent, M. J., Bottomley, A., & Taphoorn, M. J. B. (2015). Joint modeling of longitudinal health-related quality of life data and survival. Quality of Life Research, 24, 795–804. https://doi.org/10.1007/s11136-014-0821-6 - DOI - PubMed
-
- Fairclough, D. L., Peterson, H. F., Cella, D., & Bonomi, P. (1998). Comparison of several model-based methods for analysing incomplete quality of life data in cancer clinical trials. Statistics in Medicine, 17, 781–796. https://doi.org/10.1002/(SICI)1097-0258(19980315/15)17:5/7%3c781::AID-SI... - DOI - PubMed
-
- Rizopoulos, D. (2012). Joint models for longitudinal and time-to-event data. Chapman and Hall/CRC. - DOI
-
- Touraine, C., Cuer, B., Conroy, T., Beata, J., Gourgou, S., & Mollevi C. (2021). When a joint model should be preferred over a linear mixed model for analysis of longitudinal health-related quality of life data in cancer clinical trials. BMC Medical Research Methodology (under review).
Publication types
MeSH terms
Associated data
Grants and funding
LinkOut - more resources
Full Text Sources
Medical
