Bounds for the regression parameters in dependently censored survival models
Authors
Research Topics
Paper Information
-
Journal:
Journal of the Royal Statistical Society Series B -
DOI:
10.1093/jrsssb/qkag119 -
Published:
August 13, 2026 -
Added to Tracker:
Aug 14, 2026
Abstract
Abstract We propose a semiparametric model to study the effect of covariates on the distribution of a censored event time while making minimal assumptions about the censoring mechanism. The model is partially identified, and we obtain bounds on the covariate effects which are allowed to be time-dependent. Moreover, these bounds can be interpreted as classical confidence intervals and are obtained by aggregating information in the conditional Peterson bounds over the covariate space. As a special case, our approach can be used to study the popular Cox proportional hazards model while leaving the censoring distribution as well as its dependence with the time of interest completely unspecified. A simulation study illustrates good finite sample performance of the method, and several data applications in both economics and medicine demonstrate its practicability on real data. All developed methodology is implemented in R and made available in the package depCensoring.
Author Details
Ilias Willems
AuthorJad Beyhum
AuthorIngrid Van Keilegom
AuthorResearch Topics & Keywords
Machine Learning
Research AreaSurvival Analysis
Research AreaCitation Information
APA Format
Ilias Willems
,
Jad Beyhum
&
Ingrid Van Keilegom
(2026)
.
Bounds for the regression parameters in dependently censored survival models.
Journal of the Royal Statistical Society Series B
, 10.1093/jrsssb/qkag119.
BibTeX Format
@article{paper1509,
title = { Bounds for the regression parameters in dependently censored survival models },
author = {
Ilias Willems
and Jad Beyhum
and Ingrid Van Keilegom
},
journal = { Journal of the Royal Statistical Society Series B },
year = { 2026 },
doi = { 10.1093/jrsssb/qkag119 },
url = { https://doi.org/10.1093/jrsssb/qkag119 }
}