A class of semiparametric transformation cure models for interval-censored failure time data

Shuwei Li, Tao Hu, Xingqiu Zhao, Jianguo Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

This paper discusses regression analysis of interval-censored failure time data with a cured subgroup under a general class of semiparametric transformation cure models. For inference, a novel and stable expectation maximization (EM) algorithm with the use of Poisson variables is developed to overcome the difficulty in maximizing the observed data likelihood function with complex form. The asymptotic properties of the resulting estimators are established and in particular, the estimators of regression parameters are shown to be semiparametrically efficient. The numerical results obtained from a simulation study indicate that the proposed approach works well for practical situations. An application to a set of data on children's mortality is also provided.

Original languageEnglish
Pages (from-to)153-165
Number of pages13
JournalComputational Statistics and Data Analysis
Volume133
DOIs
Publication statusPublished - May 2019

Keywords

  • EM algorithm
  • Interval censoring
  • Maximum likelihood estimation
  • Transformation cure models

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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