A comparative study of forest methods for time-to-event data: variable selection and predictive performance.

ABSTRACT:

Background

As a hot method in machine learning field, the forests approach is an attractive alternative approach to Cox model. Random survival forests (RSF) methodology is the most popular survival forests method, whereas its drawbacks exist such as a selection bias towards covariates with many possible split points. Conditional inference forests (CIF) methodology is known to reduce the selection bias via a two-step split procedure implementing hypothesis tests as it separates the variable selection and splitting, but its computation costs too much time. Random forests with maximally selected rank statistics (MSR-RF) methodology proposed recently seems to be a great improvement on RSF and CIF.

Methods

In this paper we used simulation study and real data application to compar

SUBMITTER: Liu Y 

PROVIDER: S-EPMC8465777 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature