Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk

Yijun Wang, Jiajia Zhang and Yincai Tang

Statistical Theory and Related Fields, 2020, vol. 4, issue 1, 97-108

Abstract: The mixture cure model is the most popular model used to analyse the major event with a potential cure fraction. But in the real world there may exist a potential risk from other non-curable competing events. In this paper, we study the accelerated failure time model with mixture cure model via kernel-based nonparametric maximum likelihood estimation allowing non-curable competing risk. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error densities, in which a kernel-smoothed conditional profile likelihood is maximised in the M-step, and the resulting estimates are consistent. Its performance is demonstrated through comprehensive simulation studies. Finally, the proposed method is applied to the colorectal clinical trial data.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2019.1600123 (text/html)
Access to full text is restricted to subscribers.

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:taf:tstfxx:v:4:y:2020:i:1:p:97-108

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20

DOI: 10.1080/24754269.2019.1600123

Access Statistics for this article

Statistical Theory and Related Fields is currently edited by Zhao Wei

More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().