Data for A new data-driven model for post-transplant antibody dynamics in high risk kidney transplantation

Linear and nonlinear dynamic models of different order were attempted to fit the time series, and the third order linear model provided the best description of the common features in both groups. Both deterministic and stochastic parameters are found to be significantly different in the AMR and no-AMR groups. Eigenvalues have been calculated for each fitting, and phase portraits have been plotted to show the trajectories of the system states for both groups.

The results from our previous study with fewer cases have been further confirmed: the time series in the AMR group have significantly higher frequency of oscillations and faster dissipation rates, which may potentially lead to better laboratory measurement strategy and a better chance of understanding the underlying immunological mechanisms.