Efficient estimation for deep generalized accelerated hazards models with interval-censored data.
Q, W., M, D., S, M., & X, Z. (2026). Efficient estimation for deep generalized accelerated hazards models with interval-censored data.. Biometrics. https://doi.org/10.1093/biomtc/ujag140
Q W, M D, S M, X Z. Efficient estimation for deep generalized accelerated hazards models with interval-censored data.. Biometrics. 2026; doi: 10.1093/biomtc/ujag140
Q W, M D, S M, et al. Efficient estimation for deep generalized accelerated hazards models with interval-censored data.[J]. Biometrics. 2026. DOI: 10.1093/biomtc/ujag140.
@article{q2026,
author = {Wu Q and Du M and Ma S and Zhao X},
title = {Efficient estimation for deep generalized accelerated hazards models with interval-censored data.},
journal = {Biometrics},
year = {2026},
doi = {10.1093/biomtc/ujag140},
note = {PMID: 42574000},
}
TY - JOUR AU - Wu Q AU - Du M AU - Ma S AU - Zhao X TI - Efficient estimation for deep generalized accelerated hazards models with interval-censored data. T2 - Biometrics PY - 2026 DO - 10.1093/biomtc/ujag140 AN - PMID:42574000 ER -
For the analysis of interval-censored data, we propose a deep generalized accelerated hazards model. This model is designed to facilitate a detailed exploration of the relationship between various risk factors and the hazard associated with failure time. We develop a sieve maximum likelihood estimation procedure that combines deep neural networks and monotonic splines. By employing deep neural networks, we can effectively capture nonparametric effects, enabling a flexible and adaptive modeling approach for complex relationships. Under certain regularity conditions, we derive a nonasymptotic error bound for the resulting estimator and show that the finite-dimensional estimator is asymptotically normal and achieves the semiparametric efficiency. We conduct simulation studies to evaluate the finite-sample performance of the proposed approach. Furthermore, the proposed method is applied to the Atherosclerosis Risk in Communities study for practical illustration.