Statistical models versus machine learning for competing risks: development and validation of prognostic models.

ABSTRACT:

Background

In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recently there is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also been extended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM for CRs within non-complex data (small/medium sample size, low dimensional setting).

Methods

A dataset with 3826 retrospectively collected patients with extremity soft-tissue sarcoma (eSTS) and nine predictors is used to evaluate model-predictive performance in terms of discrimination and calibration. Two SM (cause-

SUBMITTER: Kantidakis G 

PROVIDER: S-EPMC9951458 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature