Fully nonparametric survival analysis in the presence of time-dependent covariates and dependent censoring

被引：2

作者：

Ruth, David M. ^{[1
]}

Wood, Nicholas L. ^{[1
]}

VanDerwerken, Douglas N. ^{[1
]}

机构：

[1] US Naval Acad, Dept Math, Annapolis, MD 21402 USA

来源：

JOURNAL OF APPLIED STATISTICS | 2023年 / 50卷 / 05期

关键词：

Survival analysis; Kaplan-Meier estimation; inverse probability censoring weighting; dependent censoring; organ transplantation; INVERSE PROBABILITY; HAZARDS;

D O I：

10.1080/02664763.2022.2031128

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In the presence of informative right censoring and time-dependent covariates, we estimate the survival function in a fully nonparametric fashion. We introduce a novel method for incorporating multiple observations per subject when estimating the survival function at different covariate values and compare several competing methods via simulation. The proposed method is applied to survival data from people awaiting liver transplant.

引用

页码：1215 / 1229

页数：15

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