A semiparametric cure model for interval-censored data

Kwok Fai Lam, Kin Yau Wong, Feifei Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


There is a growing interest in the analysis of survival data with a cured proportion particularly in tumor recurrences studies. Biologically, it is reasonable to assume that the recurrence time is mainly affected by the overall health condition of the patient that depends on some covariates such as age, sex, or treatment type received. We propose a semiparametric frailty-Cox cure model to quantify the overall health condition of the patient by a covariate-dependent frailty that has a discrete mass at zero to characterize the cured patients, and a positive continuous part to characterize the heterogeneous health conditions among the uncured patients. A multiple imputation estimation method is proposed for the right-censored case, which is further extended to accommodate interval-censored data. Simulation studies show that the performance of the proposed method is highly satisfactory. For illustration, the model is fitted to a set of right-censored melanoma incidence data and a set of interval-censored breast cosmesis data. Our analysis suggests that patients receiving treatment of radiotherapy with adjuvant chemotherapy have a significantly higher probability of breast retraction, but also a lower hazard rate of breast retraction among those patients who will eventually experience the event with similar health conditions. The interpretation is very different to those based on models without a cure component that the treatment of radiotherapy with adjuvant chemotherapy significantly increases the risk of breast retraction. KGaA, Weinheim.
Original languageEnglish
Pages (from-to)771-788
Number of pages18
JournalBiometrical Journal
Issue number5
Publication statusPublished - 1 Sept 2013
Externally publishedYes


  • Asymptotic normal data augmentation
  • Compound Poisson distribution
  • Cure model
  • Interval-censored data
  • Multiple imputation

ASJC Scopus subject areas

  • Statistics and Probability
  • General Medicine
  • Statistics, Probability and Uncertainty


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