Kernel meets sieve: transformed hazards models with sparse longitudinal covariates
Abstract
We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. We propose to combine kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator and contribute to a rigorous theoretical framework for general kernel-weighted sieve M-estimators. Numerical studies corroborate our theoretical results and show that the proposed method performs favorably over existing methods. Applying to a COVID-19 study in Wuhan illustrates the practical utility of our method.
Cite
@article{arxiv.2308.15549,
title = {Kernel meets sieve: transformed hazards models with sparse longitudinal covariates},
author = {Dayu Sun and Zhuowei Sun and Xingqiu Zhao and Hongyuan Cao},
journal= {arXiv preprint arXiv:2308.15549},
year = {2023}
}