Semiparametric analysis of competing risks data with missing causes of failure and covariate measurement error

Akurathi Jayanagasri and S. Anjana

Journal of Applied Statistics, 2026, vol. 53, issue 2, 331-355

Abstract: Competing risks data with missing causes of failure are common in biomedical studies. Often, competing risks data may arise with the covariates that are measured with error. In this work, we consider a semiparametric linear transformation model to deal with the combined problem of competing risks data with missing causes of failure and the covariate measurement error. We consider a set of estimating equations to obtain the estimators of the parameters involved in this linear transformation model. To handle the missing causes of failure, we employ the Inverse Probability Weight (IPW) approach, and a flexible Simulation Extrapolation (SIMEX) method is adopted as the covariate measurement error correction technique. We study the asymptotic properties of the estimators and assess the finite sample properties of the estimators by a Monte Carlo simulation study. The proposed method is illustrated using real data.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2025.2512965 (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:japsta:v:53:y:2026:i:2:p:331-355

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2025.2512965

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().