Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models.

ABSTRACT: For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival mod

SUBMITTER: Wey A 

PROVIDER: S-EPMC5963472 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature