Nonparametric Estimation of Event-Free Survival for Data with Left-Truncated Death and Intermittently Assessed Nonfatal Events
Abstract
In certain clinical settings, patients diagnosed with a disease of interest are at risk for death as well as a serious nonfatal event, and interest lies in event-free survival (EFS), a composite endpoint defined as the time from disease onset until the earlier of the nonfatal event and death. Component-wise censoring of EFS arises when each component is subject to a different censoring mechanism. For example, the nonfatal event may be interval censored between assessments, and death is right-censored. Further, in studies where individuals enroll after disease onset (including prevalent cohort studies), EFS is left truncated. Methods to estimate EFS probability with left-truncated and right-censored data are available in the literature, but they cannot handle component-wise censoring. We propose a kernel smoothing method to non-parametrically estimate EFS in this setting. Our method can also estimate and test for differences in the restricted mean event-free survival time, and can leverage two types of supplemental data that may be available: data from participants followed for death only (not followed for the nonfatal event), and incident cohort data, which arises when there is no delay between disease onset and study enrollment. We assess the proposed method using simulations and demonstrate the method using data from the Atherosclerosis Risk in Communities (ARIC) Study to estimate dementia-free survival probability following a myocardial infarction.
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BibTeX
@misc{lu2026nonparametric,
title = {{Nonparametric Estimation of Event-Free Survival for Data with Left-Truncated Death and Intermittently Assessed Nonfatal Events}},
author = {Han Lu and Xianghua Luo and Yifei Sun and Wendy Wang and Thomas Mosley and Priya Palta and Elsayed Z. Soliman and Lin Yee Chen and Anne Eaton},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12082},
eprint = {2609.12082},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.12082},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Nonparametric Estimation of Event-Free Survival for Data with Left-Truncated Death and Intermittently Assessed Nonfatal Events
AU - Han Lu
AU - Xianghua Luo
AU - Yifei Sun
AU - Wendy Wang
AU - Thomas Mosley
AU - Priya Palta
AU - Elsayed Z. Soliman
AU - Lin Yee Chen
AU - Anne Eaton
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.12082
UR - https://arxiv.org/abs/2609.12082
AB - In certain clinical settings, patients diagnosed with a disease of interest are at risk for death as well as a serious nonfatal event, and interest lies in event-free survival (EFS), a composite endpoint defined as the time from disease onset until the earlier of the nonfatal event and death. Component-wise censoring of EFS arises when each component is subject to a different censoring mechanism. For example, the nonfatal event may be interval censored between assessments, and death is right-censored. Further, in studies where individuals enroll after disease onset (including prevalent cohort studies), EFS is left truncated. Methods to estimate EFS probability with left-truncated and right-censored data are available in the literature, but they cannot handle component-wise censoring. We propose a kernel smoothing method to non-parametrically estimate EFS in this setting. Our method can also estimate and test for differences in the restricted mean event-free survival time, and can leverage two types of supplemental data that may be available: data from participants followed for death only (not followed for the nonfatal event), and incident cohort data, which arises when there is no delay between disease onset and study enrollment. We assess the proposed method using simulations and demonstrate the method using data from the Atherosclerosis Risk in Communities (ARIC) Study to estimate dementia-free survival probability following a myocardial infarction.
ER -