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Biostatistics Advance Access published online on March 15, 2008
Biostatistics, doi:10.1093/biostatistics/kxn005
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© The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Boosting method for nonlinear transformation models with censored survival data

Wenbin Lu* and Lexin Li

Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
lu{at}stat.ncsu.edu

* To whom correspondence should be addressed.

We propose a general class of nonlinear transformation models for analyzing censored survival data, of which the nonlinear proportional hazards and proportional odds models are special cases. A cubic smoothing spline–based component-wise boosting algorithm is derived to estimate covariate effects nonparametrically using the gradient of the marginal likelihood, that is computed using importance sampling. The proposed method can be applied to survival data with high-dimensional covariates, including the case when the sample size is smaller than the number of predictors. Empirical performance of the proposed method is evaluated via simulations and analysis of a microarray survival data.

Keywords: Boosting; Censored survival data; Importance sampling; Marginal likelihood; Nonlinear transformation models; Smoothing spline

Received July 27, 2007; revised January 18, 2008; accepted for publication February 7, 2008.


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