A class of semiparametric transformation models for survival data with a cured proportion

Sangbum Choi, Xuelin Huang, Yi Hau Chen

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


We propose a new class of semiparametric regression models based on a multiplicative frailty assumption with a discrete frailty, which may account for cured subgroup in population. The cure model framework is then recast as a problem with a transformation model. The proposed models can explain a broad range of nonproportional hazards structures along with a cured proportion. An efficient and simple algorithm based on the martingale process is developed to locate the nonparametric maximum likelihood estimator. Unlike existing expectation-maximization based methods, our approach directly maximizes a nonparametric likelihood function, and the calculation of consistent variance estimates is immediate. The proposed method is useful for resolving identifiability features embedded in semiparametric cure models. Simulation studies are presented to demonstrate the finite sample properties of the proposed method. A case study of stage III soft-tissue sarcoma is given as an illustration.

Original languageEnglish
Pages (from-to)369-386
Number of pages18
JournalLifetime Data Analysis
Issue number3
Publication statusPublished - 2014 Jun
Externally publishedYes


  • Counting process
  • Crossing survivals
  • Discrete frailty
  • Nonparametric likelihood
  • Survival analysis
  • Transformation model

ASJC Scopus subject areas

  • Applied Mathematics


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