State-of-the-art Bayesian platform for computational oncology
Make better decisions by quantifying uncertainty
Our platform makes predictions and supports decisions for individuals, treatments, subgroups, and target populations. We use the latest advances in Bayesian computation, Probabilistic Programming, and Decision Theory to bridge the gap between data, models, and the decision makers.
Interpretability
Our models are generative, transparent, explainable, and testable. In contrast to black box methods, we know what these models are doing.
Predictive Accuracy
In-sample predictions are easy. Our models make well calibrated predictions out of sample: for a new patient, a new study arm, and even a new trial.
.png)
Algorithms
Recent advances in computational statistics allow us to tackle models previously thought too difficult due to non-linearities and the number of unknowns.
Clinical Data
Our models work well in the small data regime, such as in platform trials in Oncology and Rare Diseases, where we need to take advantage of information external to the clinical trial.
Model Types
Joint Survival Models
Linking the model for the hazard with
sub-models for individual biomarkers.
Joint models for comorbidities.
Pharmacometric Models
Using Ordinary Differential Equation (ODE) solvers we encode how the drug moves through different parts of the body.
Genomic Models
Retain proper uncertainties and produce more accurate predictions than popular tools like Lasso and PCA.
