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Deep learning for the change-point Cox model with current status data

Qiyue Huang et al.

Lifetime Data Analysis: an international journal devoted to the methods and applications of reliability and survival analysis2026https://doi.org/10.1007/s10985-026-09689-yarticle
ABDC B
Weight
0.50

What the paper says

This study develops estimation methods for a deep partially linear Cox proportional hazards model with a change point under current status data, aiming to accommodate complex change-point effects. Prior work has largely relied on linear models, which may inadequately capture relationships among multivariate covariates and thus hinder accurate change-point detection. To address this, we use a deep neural network to model covariate effects within the Cox framework and propose a maximum likelihood estimation procedure for the model. We establish asymptotic properties of the resulting estimators, including consistency, asymptotic independence, and semiparametric efficiency. Simulation studies indicate that the proposed inference procedure performs well in finite samples. An analysis of a breast cancer dataset is provided to illustrate the methodology.

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https://doi.org/https://doi.org/10.1007/s10985-026-09689-y

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@article{qiyue2026,
  title        = {{Deep learning for the change-point Cox model with current status data}},
  author       = {Qiyue Huang et al.},
  journal      = {Lifetime Data Analysis: an international journal devoted to the methods and applications of reliability and survival analysis},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10985-026-09689-y},
}

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Deep learning for the change-point Cox model with current status data

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.