Inference on semi-parametric transformation model with a pairwise likelihood based on left-truncated and interval-censored data. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Lou, Yichen; Wang, Peijie; Sun, Jianguo
• Is Part Of: Journal of nonparametric statistics. Volume 35:Issue 1(2023)
• Page Start: 38
• Page End: 55
• Publication Date: 2023-01-02
• Subjects: Efficient estimation -- interval censoring -- left truncation -- pairwise pseudo-likelihood -- semi-parametric transformation model; 62N02; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2022.2138383
• Languages: English
• ISSNs: 1048-5252

Abstract

Abstract : Semi-parametric transformation models provide a general and flexible class of models for regression analysis of failure time data and many methods have been developed for their estimation. In particular, they include the proportional hazards and proportional odds models as special cases. In this paper, we discuss the situation where one observes left-truncated and interval-censored data, for which it does not seem to exist an established method. For the problem, in contrast to the commonly used conditional approach that may not be efficient, a pairwise pseudo-likelihood method is proposed to recover some missing information in the conditional method. The proposed estimators are proved to be consistent and asymptotically efficient and normal. A simulation study is conducted to assess the empirical performance of the method and suggests that it works well in practical situations. This method is illustrated by using a set of real data arising from an HIV/AIDS cohort study.