Simple mixed-effects growth models applied to tumor growth (modeling the effect of treatment and measurement method).
These models - or their variants - were used in the following papers:
- Johanna Sápi, Tamás Ferenci, Dániel András Drexler, Levente Kovács. Tumor model identification and statistical analysis. In: IEEE International Conference on Systems, Man, and Cybernetics 2015: IEEE SMC 2015. Hong Kong, China, 2015.10.09-2015.10.12. Hong Kong: IEEE, 2015. pp. 2481-2486. (ISBN:978-1-4799-8697-2).
- T Ferenci, J Sápi, L Kovács. Modelling xenograft tumor growth under antiangiogenic inhibitation with mixed-effects models. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings: SMC 2016. Budapest, Hungary, 2016.10.09-2016.10.12. Budapest: IEEE, 2016. pp. 3912-3917. (ISBN:978-1-5090-1897-0).
- Tamás Ferenci, Johanna Sápi, Levente Kovács. Modelling Tumor Growth Under Angiogenesis Inhibition with Mixed-effects Models. ACTA POLYTECHNICA HUNGARICA 14:(1) pp. 221-234. (2017).
Code is available here. Columns of RawData are: Date (date of the measurement), Type (C denotes control, E denotes treated), Code (unique identifier of the mouse), Caliper1, Caliper2, Caliper3, MRI (measurements of the given mouse at the given date with the respective measurement methods). An example file is provided with randomly generated data.
A demonstration of how the statistical analysis plan (from simple comparison of final volume through ANCOVA baseline adjustment to mixed model) influences the results. It is also a re-analysis of already published data.
This analysis was used in the following paper:
- Lack of effectiveness of bevacizumab with metronomic administration in colon adenocarcinoma mouse models. (Submitted to: PLoS One.)
Code is available here.