Flexible boosting of accelerated failure time models.

ABSTRACT:

Background

When boosting algorithms are used for building survival models from high-dimensional data, it is common to fit a Cox proportional hazards model or to use least squares techniques for fitting semiparametric accelerated failure time models. There are cases, however, where fitting a fully parametric accelerated failure time model is a good alternative to these methods, especially when the proportional hazards assumption is not justified. Boosting algorithms for the estimation of parametric accelerated failure time models have not been developed so far, since these models require the estimation of a model-specific scale parameter which traditional boosting algorithms are not able to deal with.

Results

We introduce a new boosting algorithm for censored time-to-event da

SUBMITTER: Schmid M 

PROVIDER: S-EPMC2453145 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature