Flexible boosting of accelerated failure time models

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 data which is suitable for fitting
parametric accelerated failure time models. Estimation of the
predictor function is carried out simultaneously with the
estimation of the scale parameter, so that the negative log
likelihood of the survival distribution can be used as a loss
function for the boosting algorithm. The estimation of the scale
parameter does not affect the favorable properties of boosting with
respect to variable selection. Conclusion: The analysis of a
high-dimensional set of microarray data demonstrates that the new
algorithm is able to outperform boosting with the Cox partial
likelihood when the proportional hazards assumption is
questionable. In low-dimensional settings, i.e., when classical
likelihood estimation of a parametric accelerated failure time
model is possible, simulations show that the new boosting algorithm
closely approximates the estimates obtained from the maximum
likelihood method.