Competing Risks Model with Short-Term and Long-Term Covariate Effects for Cancer Studies

Guoqing Diao (), Anand N. Vidyashankar, Sarah Zohar and Sandrine Katsahian
Additional contact information
Guoqing Diao: George Mason University
Anand N. Vidyashankar: George Mason University
Sarah Zohar: Sorbonne Université, Université de Paris
Sandrine Katsahian: Sorbonne Université, Université de Paris

Statistics in Biosciences, 2021, vol. 13, issue 1, No 8, 142-159

Abstract: Abstract Patients are frequently exposed to failure from several mutually exclusive causes, leading to a competing risk setting. Standard methods concerning the effects of covariates on the cause-specific hazards assume constant hazard ratios across time. This assumption, however, is violated in several applications. To address this issue and test the effect of covariates on multiple risks, we develop a new regression model allowing for nonconstant hazard ratios over time. The proposed model allows explicit specification of the short-term and long-term covariate effects, which can be of clinical interest. We develop a statistically efficient nonparametric likelihood methodology for estimation and inference concerning the parameters of interest and compare it to the existing methods. We investigate the performances of the proposed methods using simulations and apply them to a European study on a registry cohort of patients with acute leukemia undergoing bone marrow transplantation. Our proposed model detects the differences in short-term and long-term risks of primary relapse between patients with and without acute lymphoblastic leukemia.

Keywords: Competing risks setting; Internal time-dependent covariates; Multi-state model; Nonconstant hazard ratio model; Nonparametric maximum likelihood estimation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-020-09288-x 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:stabio:v:13:y:2021:i:1:d:10.1007_s12561-020-09288-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-020-09288-x

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().