Theory and software for boosted nonparametric hazard estimation
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:149-158, 2021.
Nonparametric approaches for analyzing survival data in the presence of time-dependent covariates is a timely topic, given the availability of high frequency data capture systems in healthcare and beyond. We present a theoretically justified gradient boosted hazard estimator for this setting, and describe a tree-based implementation called BoXHED (pronounced ‘box-head’) that is available from GitHub:www.github.com/BoXHED. Our numerical study demonstrates that there is a place in the machine learning toolbox for a nonparametric method like BoXHED that can flexibly handle time-dependent covariates. The results presented here are distilled from the recent works of Lee et al. (2021) and Wang et al. (2020).