A framework for leveraging machine learning tools to estimate personalized survival curves.

ABSTRACT: The conditional survival function of a time-to-event outcome subject to censoring and truncation is a common target of estimation in survival analysis. This parameter may be of scientific interest and also often appears as a nuisance in nonparametric and semiparametric problems. In addition to classical parametric and semiparametric methods (e.g., based on the Cox proportional hazards model), flexible machine learning approaches have been developed to estimate the conditional survival function. However, many of these methods are either implicitly or explicitly targeted toward risk stratification rather than overall survival function estimation. Others apply only to discrete-time settings or require inverse probability of censoring weights, which can be as difficult to estimate as the outco

SUBMITTER: Wolock CJ 

PROVIDER: S-EPMC11338658 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature