Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques.

ABSTRACT: Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.e., they provide no balanced updating procedure for random effects in longitudinal analysis and tend to return biased effect estimation for time-dependent covariates in survival analysis. In this manuscript, we adapt likel

SUBMITTER: Griesbach C 

PROVIDER: S-EPMC8608498 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature