A flexible copula model for bivariate survival data with dependent censoring

Reuben Adatorwovor () and Yinghao Pan
Additional contact information
Reuben Adatorwovor: University of Kentucky, Department of Biostatistics
Yinghao Pan: University of North Carolina at Charlotte, Department of Mathematics and Statistics

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2026, vol. 32, issue 1, No 2, 18 pages

Abstract: Abstract Independent censoring is a key assumption usually made when analyzing time-to-event data. However, this assumption is difficult to assess and can be problematic, particularly in studies with disproportionate loss to follow-up due to adverse events. This paper addresses the challenges associated with dependent censoring by introducing a likelihood-based approach for analyzing bivariate survival data under dependent censoring. A flexible Joe-Hu copula is used to formulate the interdependence within the quadruple times (two events and two censoring times). The marginal distribution of each event/censoring time is modeled via the Cox proportional hazards model. Our estimator possesses consistency and desirable asymptotic properties under regularity conditions. We present results from extensive simulation studies and further illustrate our approach using prostate cancer data.

Keywords: Archimedean copula; Bivariate survival; Cancer survival; Dependent censoring; Prostate (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10985-025-09678-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lifeda:v:32:y:2026:i:1:d:10.1007_s10985-025-09678-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10985

DOI: 10.1007/s10985-025-09678-7

Access Statistics for this article

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data is currently edited by Mei-Ling Ting Lee

More articles in Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().