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

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. Parametric statistical models such as ones assuming exponential growth are useful for summarizing tumor volume over ranges for which the growth model holds, with the advantage that the model's parameter estimates can be used to summarize between-group differences in tumor volume growth with statistical measures of uncertainty.