Copula-based analysis of dependent current status data with semiparametric linear transformation model

Huazhen Yu (), Rui Zhang () and Lixin Zhang ()
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Huazhen Yu: Zhejiang University
Rui Zhang: Zhejiang University
Lixin Zhang: Zhejiang University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 4, No 3, 742-775

Abstract: Abstract This paper discusses regression analysis of current status data with dependent censoring, a problem that often occurs in many areas such as cross-sectional studies, epidemiological investigations and tumorigenicity experiments. Copula model-based methods are commonly employed to tackle this issue. However, these methods often face challenges in terms of model and parameter identification. The primary aim of this paper is to propose a copula-based analysis for dependent current status data, where the association parameter is left unspecified. Our method is based on a general class of semiparametric linear transformation models and parametric copulas. We demonstrate that the proposed semiparametric model is identifiable under certain regularity conditions from the distribution of the observed data. For inference, we develop a sieve maximum likelihood estimation method, using Bernstein polynomials to approximate the nonparametric functions involved. The asymptotic consistency and normality of the proposed estimators are established. Finally, to demonstrate the effectiveness and practical applicability of our method, we conduct an extensive simulation study and apply the proposed method to a real data example.

Keywords: Copula; Identifiability; Dependent censoring; Nonparametric transformation; Current status data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-024-09632-z

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