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Inference on semiparametric transformation model with general interval-censored failure time data

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  • Peijie Wang
  • Hui Zhao
  • Mingyue Du
  • Jianguo Sun

Abstract

Failure time data occur in many areas and in various censoring forms and many models have been proposed for their regression analysis such as the proportional hazards model and the proportional odds model. Another choice that has been discussed in the literature is a general class of semiparmetric transformation models, which include the two models above and many others as special cases. In this paper, we consider this class of models when one faces a general type of censored data, case K informatively interval-censored data, for which there does not seem to exist an established inference procedure. For the problem, we present a two-step estimation procedure that is quite flexible and can be easily implemented, and the consistency and asymptotic normality of the proposed estimators of regression parameters are established. In addition, an extensive simulation study is conducted and suggests that the proposed procedure works well for practical situations. An application is also provided.

Suggested Citation

  • Peijie Wang & Hui Zhao & Mingyue Du & Jianguo Sun, 2018. "Inference on semiparametric transformation model with general interval-censored failure time data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(3), pages 758-773, July.
    • Handle: RePEc:taf:gnstxx:v:30:y:2018:i:3:p:758-773
    DOI: 10.1080/10485252.2018.1478091
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    Cited by:

    1. Du, Mingyue & Zhao, Xingqiu & Sun, Jianguo, 2022. "Variable selection for case-cohort studies with informatively interval-censored outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).

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