  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 tradeoff
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 models
consistently perform well across a wide range of scenarios by adaptively
balancing the strengths and weaknesses of individual candidate survival models.
In addition, stacked survival models perform as good as, or better than, the
model selected through cross-validation. Lastly, stacked survival models are
applied to a well-known German breast cancer study.
