This fully automated pipeline computes reliable risk and treatment response signatures for high-dimensional data, i.e. in the overfitting regime. It can handle multiple clinical outcome types: time-to-event, ordinal class outcomes, and real-valued clinical scales. It suppresses overfitting effects by a combination of iterative removal of irrelevant covariates based on probabilistic arguments (as opposed to on z-scores), and bias removal and optimal regularization based on a statistical mechanical theory of overfitting in Generalized Linear Models (GLM).

This theory, developed by Saddle Point in collaboration with academic partners (see the publications list), is based on the so-called replica method. The benefits of this strategy compared to more standard regularization approaches to overfitting (as used in other statistical software packages) are that (i) no data need to be sacrificed for optimization of the hyperparameters; they are either not needed (in bias removal mode) or they are computed analytically (in regularization mode), and (ii) also nuisance parameters (e.g. base hazards) are corrected for overfitting.​Other features include: ​Handling of informative covariate missingness.Optimized personalized risk and treatment response signatures, and personalized optimal drug doses.  Shadow analysis (analysis with outcome-randomized data, to exclude false positive inferences).Generation of simulated clinical data.Validation of predictions on further data sets.Fully automated report generation.