Abstract
BackgroundWhen predictive survival models are built from high-dimensional data, there are often additional covariates, such as clinical scores, that by all means have to be included into the final model. While there are several techniques for the fitting of sparse high-dimensional survival models by penalized parameter estimation, none allows for explicit consideration of such mandatory covariates.ResultsWe introduce a new boosting algorithm for censored time-to-event data that shares the favorable properties of existing approaches, i.e., it results in sparse models with good prediction performance, but uses an offset-based update mechanism. The latter allows for tailored penalization of the covariates under consideration. Specifically, unpenalized mandatory covariates can be introduced. Microarray survival data from patients with diffuse large B-cell lymphoma, in combination with the recent, bootstrap-based prediction error curve technique, is used to illustrate the advantages of the new procedure.ConclusionIt is demonstrated that it can be highly beneficial in terms of prediction performance to use an estimation procedure that incorporates mandatory covariates into high-dimensional survival models. The new approach also allows to answer the question whether improved predictions are obtained by including microarray features in addition to classical clinical criteria.
Highlights
When predictive survival models are built from high-dimensional data, there are often additional covariates, such as clinical scores, that by all means have to be included into the final model
We introduce a new boosting algorithm for censored time-to-event data that shares the favorable properties of existing approaches, i.e., it results in sparse models with good prediction performance, but uses an offset-based update mechanism
It is demonstrated that it can be highly beneficial in terms of prediction performance to use an estimation procedure that incorporates mandatory covariates into high-dimensional survival models
Summary
When predictive survival models are built from high-dimensional data, there are often additional covariates, such as clinical scores, that by all means have to be included into the final model. Because of the typically large number of covariates, techniques have been developed that result in sparse models, i.e., models where only a small number of covariates is used In modern approaches, such as boosting [1] and the Lasso-like path algorithms [2], it is avoided to discard covariates before model fitting, and parameter estimation and selection of covariates is performed simultaneously. This is implemented by (explicitly or implicitly) putting a penalty on the model parameters for estimation. The structure of this penalty is chosen such that most of the estimated parameters will be equal to zero, i.e., the value of the corresponding covariates does not influence predictions obtained from the fitted model
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