Lifetime Data Analysis

, Volume 25, Issue 1, pp 26–51 | Cite as

A class of semiparametric cure models with current status data

  • Guoqing DiaoEmail author
  • Ao Yuan


Current status data occur in many biomedical studies where we only know whether the event of interest occurs before or after a particular time point. In practice, some subjects may never experience the event of interest, i.e., a certain fraction of the population is cured or is not susceptible to the event of interest. We consider a class of semiparametric transformation cure models for current status data with a survival fraction. This class includes both the proportional hazards and the proportional odds cure models as two special cases. We develop efficient likelihood-based estimation and inference procedures. We show that the maximum likelihood estimators for the regression coefficients are consistent, asymptotically normal, and asymptotically efficient. Simulation studies demonstrate that the proposed methods perform well in finite samples. For illustration, we provide an application of the models to a study on the calcification of the hydrogel intraocular lenses.


Box–Cox transformation Cure fraction Empirical process NPMLE Proportional hazards cure model Proportional odds cure model Semiparametric efficiency 



The authors wish to thank Dr. Donglin Zeng and the referees for their helpful comments and suggestions, which lead to a considerable improvement in the presentation of this manuscript. The authors also would like to thank Drs. A. F. K. Yu, K. F. Lam, and HongQi Xue for providing the Calcification data.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of StatisticsGeorge Mason UniversityFairfaxUSA
  2. 2.Department of Biostatistics, Bioinformatics and BiomathematicsGeorgetown UniversityWashingtonUSA

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