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Articles

Generalized Additive Mixed Modeling of Longitudinal Tumor Growth Reduces Bias and Improves Decision Making in Translational Oncology

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By William Forrest, Bruno Alicke, Alférez DG, Biankin AV
| cancerres.aacrjournals.org
Translational Science William F. Forrest, Bruno Alicke, Oleg Mayba, Magdalena Osinska, Michal Jakubczak, Pawel Piatkowski, Lech Choniawko, Alice Starr and Stephen E. Gould DOI: 10.1158/0008-5472.CAN-20-0342 Published November 2020 Abstract Scientists working in translational oncology regularly conduct multigroup studies of mice with serially measured tumors. Longitudinal data collected can feature mid-study dropouts and complex nonlinear temporal response patterns.
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