What does this research mean for the field?

A novel inference procedure utilizing a three-block loss function and dimension reduction provides a consistent, computationally simple, and empirically advantageous method for estimating quantile regression parameters in survival data with a cure fraction. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a computationally efficient quantile regression estimation procedure for censored survival data containing a cured subgroup.

August 29, 2026Journal of the American Statistical Association

Quantile regression for censored data with a cure fraction

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Authors

BHBo Han IKIngrid Van Keilegom XWXiaoguang Wang

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Overview

Statistical modeling study demonstrates robust survival quantile estimation in populations with a cure fraction, indicating improved precision for cancer outcome analyses.

Key Points

To develop a computationally efficient quantile regression estimation procedure for censored survival data containing a cured subgroup.
Formulated a two-component mixture cure model using a logistic model for cure status and a linear quantile regression model for uncured subjects.
Designed an objective loss function combining a check function, a weight function for censored observations, and a latent cure status weight function.
Incorporated dimension reduction to address high dimensionality and assessed performance using simulation studies and breast cancer clinical data.
Proved theoretical asymptotic properties for the proposed estimators, verifying both consistency and weak convergence.
Demonstrated superior empirical estimation performance across simulation settings featuring both homogeneous and heterogeneous error distributions.
Successfully modeled survival percentiles in an empirical breast cancer cohort, confirming the practical utility of the cure rate quantile regression framework.

Cite This Study

Han et al. (2026) studied this question.

synapsesocial.com/papers/6a9299bb8e5d7d1fc0c11ea3 https://doi.org/10.1080/01621459.2026.2723402

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August 29, 2026Journal of the American Statistical Association

Quantile regression for censored data with a cure fraction

View Full Paper

Ask AI

Bookmark

Authors

BHBo Han IKIngrid Van Keilegom XWXiaoguang Wang

Discussion

Member takes

Overview

Statistical modeling study demonstrates robust survival quantile estimation in populations with a cure fraction, indicating improved precision for cancer outcome analyses.

Key Points

To develop a computationally efficient quantile regression estimation procedure for censored survival data containing a cured subgroup.
Formulated a two-component mixture cure model using a logistic model for cure status and a linear quantile regression model for uncured subjects.
Designed an objective loss function combining a check function, a weight function for censored observations, and a latent cure status weight function.
Incorporated dimension reduction to address high dimensionality and assessed performance using simulation studies and breast cancer clinical data.
Proved theoretical asymptotic properties for the proposed estimators, verifying both consistency and weak convergence.
Demonstrated superior empirical estimation performance across simulation settings featuring both homogeneous and heterogeneous error distributions.
Successfully modeled survival percentiles in an empirical breast cancer cohort, confirming the practical utility of the cure rate quantile regression framework.

Cite This Study

Han et al. (2026) studied this question.

synapsesocial.com/papers/6a9299bb8e5d7d1fc0c11ea3 https://doi.org/10.1080/01621459.2026.2723402

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