Semiparametric estimation for the transformation model with length-biased data and covariate measurement error. Issue 3 (11th February 2020)

• Record Type: Journal Article
• Authors: Chen, Li-Pang
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 3(2020)
• Page Start: 420
• Page End: 442
• Publication Date: 2020-02-11
• Subjects: Length-based sampling -- measurement error -- prevalent cohort -- survival analysis -- transformation model; 62N01; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1687700
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Analysis of survival data with biased samples caused by left-truncation or length-biased sampling has received extensive interest. Many inference methods have been developed for various survival models. These methods with ignorance of mismeasurement, however, may produce different estimations and yield misleading conclusions when survival data are typically error-contaminated. Although error-prone survival data commonly arise in practice, little work has been available in the literature for handling length-biased data with measurement error. In survival analysis, methods of analyzing the transformation model with those complex features have not been fully explored. In this paper, we develop a valid inference method under the transformation model. We establish asymptotic results for the proposed estimators. The proposed method enjoys appealing features in that there is no need to specify the distribution of the covariates and the increasing function in the transformation model. Numerical studies are reported to assess the performance of the proposed method.