Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring

Pao-sheng Shen ()
Additional contact information
Pao-sheng Shen: Tunghai University

Computational Statistics, 2022, vol. 37, issue 1, No 2, 29-42

Abstract: Abstract In this article, we consider nonparametric estimation of the cumulative incidence function (CIF) for left-truncated and interval-censored competing risks (LT-ICC) data. To reduce the bias of the pseudo-likelihood estimator (PLE) of CIF in the literature, we proposed two alternative estimators. The first estimator, called the modified PLE (MPLE), is obtained based on the modified NPMLE of F(t). The second estimator, called the modified maximum likelihood estimator (MMLE), is derived using modified likelihood functions for LT-ICC data, where the left endpoints of the intervals for left-censored observations with failure type j are the maximum of left-truncated variables and the estimated left endpoint of the support of the observations. Simulation studies show that the MPLE and MMLE are less biased than the PLE for most of the cases considered and their standard deviations are significantly smaller than that of the PLE.

Keywords: NPMLE; Cumulative incidence function; Left truncation; EM algorithm (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-021-01111-5 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:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01111-5

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-021-01111-5

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().