Sparse boosting for high‐dimensional survival data with varying coefficients

Abstract

Motivated by high-throughput profiling studies in biomedical research, variable selection methods have been a focus for biostatisticians. In this paper, we consider semiparametric varying-coefficient accelerated failure time models for right censored survival data with high-dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel sparse boosting (SparseL Boosting) algorithm to conduct model-based prediction and variable selection. One main advantage of this new method is that we do not need to perform the time-consuming selection of tuning parameters. Extensive simulations are conducted to examine the performance of our sparse boosting feature selection techniques. We further illustrate our methods using a lung cancer data analysis.

Sparse Boosting Based Machine Learning Methods for High-Dimensional Data

“…The results from [17] show that L2 boosting and sparse boosting for varyingcoefficient AFT model not only produce relatively sparser model, but also have smaller in-sample and out-of-sample prediction error compared to the four methods for constant-coefficient AFT model. Again, sparse boosting produce even sparser model than L2 boosting.…”

“…The simulation results from [17] show that both boosting methods can identify important variables while sparse boosting selects much fewer irrelevant variables than L2 boosting. Although in-sample prediction errors (defined as…”

“…In this chapter we review some sparse boosting based methods for the following high-dimensional problems based on three research papers. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers [17]. Then, a two-step sparse boosting to carry out the variable selection and the model-based prediction is studied for the highdimensional longitudinal observations measured repeated over time [18].…”

Transformation boosting machines

“…The proportional hazards (with F Z (z) = 1 − exp(− exp(z))) and proportional odds (with F Z (z) = expit(z)) models are the most well-known representatives of this class of shift transformation models (STM, often also referred to as linear or nonlinear transformation models, depending on the functional form of β(x)). Boosting procedures that allow flexible estimation of β(x) have been studied for proportional hazards models under right censoring (Ridgeway 1999;Schmid and Hothorn 2008;Lu and Li 2008;Yue et al 2017) and proportional odds models have been studied for ordered responses (Schmid et al 2011). A comparison of prominent and less prominent members of this model class is given in Hothorn et al (2018).…”

Robust nonparametric integrative analysis to decipher heterogeneity and commonality across subgroups using sparse boosting

“…The proposed approach is based on sparse boosting, 19 which demonstrates competitive performances in high-dimensional data analysis compared to penalization and other techniques. 15,20 In all, this study is warranted by providing a practically useful new approach for exploring heterogeneity and commonality across multiple high-dimensional datasets.…”