GKantidakis/SM-vs-ML-for-CRs
This repository stores the R-code of a research project that compared statistical models (SM) with machine learning (ML) techniques for competing risks in private extremity soft-tissue sarcoma (eSTS) data, and a comprehensive example in publicly available R data for Follicular Cell Lymphoma (data "follic"). The aim was to develop and validate prognostic clinical prediction models for competing risks (CRs) with small/medium sample size and in a low-dimensional setting. Two SM (cause-specific Cox, Fine-Gray) and three ML techniques (PLANNCR original, PLANNCR extended, RSFCR) were compared in terms of discrimination and calibration. -Notes- PLANNCR, Partial Logistic Artificial Neural Network for Competing Risks; RSFCR, Random Survival Forests for Competing Risks.