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A Class of Semiparametric Transformation Models for Doubly Censored Failure Time Data

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  • Shuwei Li
  • Tao Hu
  • Peijie Wang
  • Jianguo Sun

Abstract

Doubly censored failure time data occur in many areas including demographical studies, epidemiology studies, medical studies and tumorigenicity experiments, and correspondingly some inference procedures have been developed in the literature (Biometrika, 91, 2004, 277; Comput. Statist. Data Anal., 57, 2013, 41; J. Comput. Graph. Statist., 13, 2004, 123). In this paper, we discuss regression analysis of such data under a class of flexible semiparametric transformation models, which includes some commonly used models for doubly censored data as special cases. For inference, the non‐parametric maximum likelihood estimation will be developed and in particular, we will present a novel expectation–maximization algorithm with the use of subject‐specific independent Poisson variables. In addition, the asymptotic properties of the proposed estimators are established and an extensive simulation study suggests that the proposed methodology works well for practical situations. The method is applied to an AIDS study.

Suggested Citation

  • Shuwei Li & Tao Hu & Peijie Wang & Jianguo Sun, 2018. "A Class of Semiparametric Transformation Models for Doubly Censored Failure Time Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(3), pages 682-698, September.
    • Handle: RePEc:bla:scjsta:v:45:y:2018:i:3:p:682-698
    DOI: 10.1111/sjos.12319
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    Cited by:

    1. Shuwei Li & Jianguo Sun & Tian Tian & Xia Cui, 2020. "Semiparametric regression analysis of doubly censored failure time data from cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 315-338, April.
    2. Choi, Taehwa & Kim, Arlene K.H. & Choi, Sangbum, 2021. "Semiparametric least-squares regression with doubly-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    3. Rong Liu & Shishun Zhao & Tao Hu & Jianguo Sun, 2022. "Variable Selection for Generalized Linear Models with Interval-Censored Failure Time Data," Mathematics, MDPI, vol. 10(5), pages 1-18, February.

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