Variable Selection for Case-cohort Studies with Informatively Interval-censored Outcomes

Mingyue Du, Xingqiu Zhao, Jianguo Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Variable selection has recently attracted a great deal of attention and in particular, a couple of methods have been proposed for general interval-censored failure time data or the interval-censored data arising from case-cohort studies. However, all of them have some limitations or apply only to limited situations. Corresponding to these, a new, more general variable selection approach is proposed under a class of flexible semiparametric transformation models that allows for dependent interval censoring. In particular, the oracle property of the method under the broken adaptive ridge penalty function is established and for its implementation, a novel EM algorithm is developed. Also a simulation study is performed and suggests that the proposed approach works well in practical situations. Finally the method is applied to a HIV trial that motivated this study.

Original languageEnglish
Article number107484
Pages (from-to)1-17
Number of pages17
JournalComputational Statistics and Data Analysis
Publication statusPublished - Aug 2022


  • Case-cohort studies
  • Informative censoring
  • Semiparametric transformation model
  • Variable selection

ASJC Scopus subject areas

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


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